Sematic and Social Web

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User Model User-Adap Inter (2009) 19:433486

DOI 10.1007/s11257-009-9067-3

ORIGINAL PAPER

Adaptive systems in the era of the semantic and social

web, a survey

Ilaria Torre

Received: 26 May 2008 / Accepted in revised form: 6 May 2009 /

Published online: 3 November 2009 Springer Science+Business Media B.V. 2009

Abstract In this paper we provide a classification of adaptive systems with respect

to the kind of semantic technology they exploit to accomplish or improve specific adap-

tation and user modeling tasks. This classification is based on a distinction between

strong semantic techniques and weak semantic techniques. The former are techniques

based on the Semantic Web, while the latter regard technologies that, in different ways,

annotate resources, enriching their meaning. This second category includes, in par-

ticular, Web 2.0 social annotations and mixed approaches between social annotationsand Semantic Web techniques. While the impact of the Semantic Web on adaptive

systems has been discussed in several survey papers, the potential of weak semantic

technologies has, so far, received little attention. The aim of this analysis is to fill this

gap. Therefore, we will discuss contributions and limits of both approaches, but we

will focus special attention on weak semantic adaptive systems.

Keywords Semantic web Web 2.0 Tag Annotation User model

Adaptation Semantic adaptive systems Review

1 Introduction

Recently, several efforts have been made to enrich Web objects with annotations,

with the aim of improving the retrieval and exchange of resources. Currently, the

main approaches to add annotations are related to ontologies and so-called folksono-

mies (see the definition in Appendix A, at the end of the paper), which have been

defined effectively as two new players in indexing and knowledge representation

(Weller 2007). The former approach is based on the principles of the Semantic Web,

I. Torre (B)

Dipartimento di Informatica, Universit di Torino, Corso Svizzera 185, 10149 Torino, Italy

e-mail: [email protected]

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which provides a common framework to allow data to be shared and reused across

applications, using formal descriptions of concepts and of their relationships. Follow-

ing this approach, resources are annotated with concepts from ontologies that describe

a knowledge domain. Conversely, the other approach is related to Web 2.0, which con-

ceives the Web as a social and collaborative platform: a Social Web, where users havean active role in generating contents and annotating resources, typically using tags

that, altogether, form the folksonomy of a knowledge domain. In the middle, mixed

approaches and new standards, such as microformats, are emerging to bridge the gap

or to find methods to exploit both Semantic Web and Web 2.0 techniques.1

In this decade, several researchers in the community of Adaptive Systems tried

to combine semantic techniques with personalization and user modeling techniques.

Semantic technologies can be useful for allowing personalization techniques to be

applied to open corpora of resources, to enhance the possibility of providing explana-

tions and of exchanging information between adaptive systems, such as informationabout user models.

In this paper we provide an analysis of these lines of research, in order to investigate

the value that semantic techniques can add to adaptive and user modeling systems. In

particular, we investigate whether it is possible to identify tasks and areas of adaptive

systems where these techniques proved to be particularly successful and if the integra-

tion of these approaches can be a third way that gives the benefits of both of them. To

date, there have been several reviews about the contribution of ontologies and Seman-

tic Web to adaptive systems (see for example Dolog and Nejdl 2007; Antoniou et al.

2004; Baldoni et al. 2005; Abel et al. 2007a; Kravcik and Gasevic 2007). However,to the best of our knowledge, there has been no survey that deals with the use of Web

2.0 social annotations and mixed approaches in adaptive and user modeling systems.

The main contribution of this paper is to fill this gap by providing an overview of how

adaptive systems can benefit from both approaches.

The methodology we adopted can be summarized as follows:

(1) analyzing and classifying the main semantic techniques and approaches related

to Web 2.0 social annotations and the Semantic Web,

(2) decomposing the adaptive systems functionality into a set oftasks and sub-tasks,

(3) selecting a set ofsources to discover significant semantic adaptive systems and,consequently, identifying some semantic adaptive systems useful for the analysis

(in the following we will call them SASs),

(4) analyzing and classifying each SAS from two points of view: the semantic tech-

nique it exploits and the adaptation and user-modeling task for which the semantic

technique has been used.

The result has been the definition of a matrix which contains the SAS distribution

with respect to these two parameters.

The structure of the paper follows the methodological steps mentioned above. Sec-

tion 2 describes the semantic technologies considered for the review and introduces the

1 The new concept of Web 3.0 should go in this direction. Though different definitions of Web 3.0 exist,

this term is being used frequently to mean the next generation of intelligent and social Web (see for example

the workshop on Web 3.0 at the 2009 ACM Hypertext Conference).

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Adaptive systems in the era of the semantic and social web 435

dichotomy weak/strong semantic web that will be the basis for the analysis. Section 3

presents a functional model of tasks and sub-tasks for SASs and Sect. 4 presents the

matrix which classifies the reviewed systems on the basis of the semantic technique

that was used (x-axis) and the task it was used for (y-axis). The remaining sections

focus on the analysis of the specific tasks and each section is divided in two subsec-tions which describe how weak and strong semantic techniques are used in a set of

representative systems. In detail, Sect. 5 concerns domain modeling and management,

Sect. 6 concerns user and context modeling and management, Sect. 7 concerns adap-

tation and personalization and Sect. 8 concerns privacy and controllability. Finally,

Sect. 9 discusses the general results of the analysis and Sect. 10 presents some con-

cluding remarks. Two appendices complete the paper: Appendix A describes the

main semantic technologies considered in the analysis and Appendix B provides a

referenced list of the reviewed systems.

2 Semantic technologies

According to the first step of the methodology sketched above, in this section we

introduce the main approaches and technologies that are relevant for this analysis. A

detailed description of languages and tools is provided in Appendix A.

2.1 Semantic web and web 2.0

As known, the objective of the Semantic Web (Berners-Lee 1998; Berners-Lee et al.

2006) is to describe Web resources in a way that allows machines to understand and

process them. The main objectives of the Semantic Web can be summarized as follows:

making information automatically available to software agents, integrating contents

from different sources, optimizing search and enabling trust on the Web. It implies

the adoption of a completely new paradigm of data description and retrieval. Each

content in Web documents has to be formally described, that is annotated, making

reference to concepts defined in public ontologies, using formal languages. Intelli-

gent agents are necessary to reason over ontologies and automate the tasks above.Moreover, rules can be defined to complement and extend ontologies, so as to draw

inferences, express constraints, specify policies, react to events/changes, discover new

knowledge, transform data, etc.2

In adaptive and user modeling systems, and also in other domains, the technologies

most often used are languages and tools for building and querying ontologies. Rules

and polices for trust have been used less and represent areas for future development

of the Semantic Web.

In Web 2.0, the other approach we take into account in this analysis, resources are

annotated by users without using formal languages. Typically, they are added in theform of tags. Annotations can be added to Web objects such as bookmarks, photos,

documents and blog posts, but also to Web users. For example, users can specify their

2 http://2005.ruleml.org/.

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level of trust in other users and the type of relationship with other users and they

can also tag themselves and other users. Social software platforms at the heart of Web

2.0 include Weblogs, Wiki tools and Collaborative Tagging Systems. In general terms,

they are technologies to make information sharing and collaboration on the Web easier,

but, in our analysis, we take them into account specifically for their ability to involvepeople in annotating Web resources according to their specific point of view. While

Weblogs and Wiki tools allow users to enter unstructured or semi-structured contents

(specific microformats have been defined to structure blog contents), Collaborative

Tagging Systems (Marlow et al. 2006; Golder and Huberman 2006) require users to

add annotations in the form of keywords. Folksonomies (Vander Wal 2004), which

derive from this process, can be defined as distributed classification systems without

a predefined structure, and therefore specifically suitable for unstable and evolution-

ary information spaces. Moreover, by allowing an element to be placed in different

categories, they produce overlapping clusters, in contrast to the hierarchical structureof taxonomies.

If the Semantic Webs main objective is to make Web resources machine under-

standable and processable, with the ultimate goal of providing users with improved

services, the Web 2.0 approach is directly focused on users, without the intermedia-

tion of intelligent agents, though software agents can then be exploited to provide or

improve some specific services. The significant phenomenon, specifically relevant to

our analysis, is that in tagging systems, the annotations the user posts progressively

give rise to a description of the tagged resource that reflects its several facets.3 Ana-

lyzing the folksonomy growth patterns of Delicious,4

Golder and Huberman (2006)observe that, after a relatively small number of bookmarks for the same resource (about

100), a nascent consensus seems to form in the tags entered by users to classify the

bookmark. Although other studies provide other results and show high percentages of

errors in tagging there is a consensus that tagging systems can classify resources in

a way that allows to represent their multiple facets but also to express their prevalent

meaning (though this meaning is not universal but very dependent on the community

using the tagging system).5 Algorithms have also been developed to deal with prob-

lems such as polysemy, synonymy, tags grammatical variations, etc. (e.g., Dix et al.

2006).

The peculiarity seen above makes Web 2.0 social annotations and Semantic Web-

based annotations more similar in their ability to express the meaning of resources.

However this similarity can be seen better as a complementarity: social annotations

are mainly useful for information retrieval in changing and instable domains. Some

authors specify they are suited to informal domains (e.g., MacGregor and McCulloch

2006), for example to recreational research, personal information management and

serendipity-based exploration prior to formal exploration. Semantic Web-based

3 Shirky (2005) states that tagging systems allow knowledge to be fully and faithfully represented, sincethe categorization of terms is not conditioned by just one point of view.

4 Delicious.com is a tagging system that lets users to tag bookmarks and savethemonline so that bookmarks

can be classified under different categories/tags and retrieved everywhere.

5 Golder and Huberman (2006), for example, explain that the achieved consensus on tags could be an effect

of imitation and shared background of taggers.

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annotations, on the contrary, are useful for data exchange, integration and reuse, and

are particularly powerful in specific and stable domains. Three main problems limit

their power: the cost and difficulty of development (e.g., ontology definition, descrip-

tion of contents as RDF statements, etc.), which is especially high for pre-existing

systems, the difficulty of ontology alignment and matching, necessary to make anno-tations processable by systems that use different ontologies and the complexity to find

a usabilityreusability trade-off in the definition of ontology concepts that will be used

to annotate resources6.

2.2 Middle-ground and mixed approaches

Several authors have proposed and discussed intermediate solutions to the problems

described above. Despite their differences, many of them are based on some formof integration between Semantic Web and Web 2.0 techniques. According to Gruber

(2007), the dichotomy between Semantic Web and Social Web is false, and he states

that there is increasing value in applying Semantic Web technologies to the data of the

Social Web. Other mixed approaches propose the incorporation of the social context

into the representation of ontologies (Mika 2007), the transformation of social anno-

tations into RDF triples (e.g., Abel et al. 2007c), ontology learning from folksonomies

(e.g., Specia and Motta 2007), etc.

We conclude this section by mentioning two technologies that could be defined

middle-ground standards: microformats and RDFa. Both of them are based on the

idea of using XHTML attributes to add meaning to resources. RDFa is the W3C rec-

ommendation for embedding RDF semantics into Web documents. Mircoformats are

focused on re-using existing standards to represent data in different domains (e.g.,

contact information, geographical coordinates, calendar events, etc.). Even though

they are not quite as powerful, expressive and extensible as RDF or OWL, they are

a way to enable data items to be indexed, searched, cross-referenced and automati-

cally exported (Khare and elik 2006). This evolutionary or, better, non-revolutionary

approach to semantization of the Web has been defined by elik and Marks (2004)

as lowercase semantic web to distinguish it from the uppercase Semantic Web, which

requires revolutionary changes of information representation and formalization.

As we have seen, there are various ways to the semantization of the current Web.

Following Searles definition of weak versus strong AI (Searle 1980), which distin-

guishes systems that are intelligent only in their output (i.e., act intelligently) from

those systems that are intelligent also in the modalities of reasoning (i.e., which have

a mind), we could say that there is a weak semantic web and a strong semantic web.

The former has the goal of adding meaning to resources to improve services to users,

independently of the kind of semantic annotations (folksonomies, microformats and

mixed approaches) used and of the kind of techniques used to obtain them. The latter

aims at data understanding by machines to improve services to users. The former isless extensible and powerful but more effective and short term applicable. The latter, in

6 The usabilityreusability problem states that if concepts are very abstract and general they will be hardly

used in real contexts while if they are very specific, the reusability is limited ( Beys et al. 1996).

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contrast, is more powerful and not limited in knowledge representation, but typically

it is expensive and complex to develop, especially for the actual Web, where it risks

to only be powerful in limited domains.

If we place weak and strong semantic techniques on an axis, we can represent them

as a discontinuous-continuum from weak to strong semantic technologies, as on thex -axis in Fig. 2. In the following, we will use this weak/strong dichotomy to ana-

lyze the adaptive systems that use techniques based on the weak and strong semantic

web. We will call these systems weak and strong semantic adaptive systems. As a

typographical convention for this paper, with the expression semantic web, in the

lowercase format and without adjectives, we refer to both weak and strong semantic

web, while the uppercase format Semantic Web will be used with its traditional

meaning.

3 A functional model of tasks and sub-tasks for SASs

The goal of this study is to analyze how semantic techniques are used, and can be

used, to accomplish the different tasks and sub-tasks in semantic adaptive systems.

Therefore, according to the methodology sketched in the introduction, the next step is

to decompose the functionality of adaptive systems into a set of tasks and sub-tasks.

To this aim, we started from the classic papers of Brusilovsky (1996) and Kobsa et al.

(2001) that analyze tasks, methods and techniques of adaptive hypermedia systems.Both of them identify three tasks (that Brusilovsky calls stages) in the overall adap-

tation process: the acquisition task (collecting data about the user, for Brusilovsky),

the representation and secondary inference task (processing the data to build or update

the user model, for Brusilovsky) and the production task (applying the user model

to provide the adaptation, for Brusilovsky). Each task can be split into sub-tasks and

there exist several methods and techniques to accomplish each of them.

It is important to notice that our analysis focuses on tasks and sub-tasks that can be

supported and improved by using semantic techniques, and not on the whole model

of adaptation. Therefore, some sub-tasks have not been taken into account (as not

relevant to our analysis), while other sub-tasks have been introduced into the model

(such as Adaptation to support annotation, Trust modeling and social networks,

etc.), as a consequence of the new issues addressed by the adaptive systems and by

the SASs in particular. Moreover, it is worth noting that the model is the result of an

iterative process that redefined it several times, according to the issues and approaches

that emerged from the reviewed papers.

We now explain the SAS functional model, displayed in Fig. 1, that will guide our

analysis. The easiest way to describe the model is to start from the goal of the overall

adaptation process, that is to produce an adaptation effect that takes into account the

user and context features, and, in most of the current adaptive systems, respects pri-

vacy criteria and, usually, takes care of the controllability of the adaptation process.

Given this goal, we can follow a bottom-up description of the model, starting from the

production task (at the bottom of the figure) and moving up to the tasks and sub-tasks

that allow it to be accomplished.

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Fig. 1 Functional model of tasks and sub-tasks specifically suited for SASs

(i) Adaptation and personalization is the most important sub-task of the Produc-

tion task. It is responsible for producing the adaptation effect, which can be

achieved in different ways. In our model we identify three not mutually exclu-

sive ways:

Adaptation of content and recommendation, by using adaptation techniques

and methodologies based on different approaches, including content-based

approaches, collaborative approaches and hybrid approaches (Burke 2002,

Herlocker et al. 2004). We note that, when we will classify SASs with respect

to this task, we will just take into account SASs which use semantic

techniques to support these approaches. The same is true for all the other

tasks.

Adaptation of structure and presentation, regards the task in charge of adapting

the link structure of hypermedia documents or its presentation to users (Kobsa

et al. 2001). In our model, this task also includes what Kobsa calls Adaptation

of the Presentation and Modality, which refers to the adaptation of the format,

layout and modality with which objects are presented.

Adaptation to support annotation is a new task, compared to Kobsas classifi-

cation. It is more specific than the previous two tasks, but we introduced and

placed it here for its relevance in many SASs. Most of the systems that address

this task provide support in the form of tag recommendation. We could have

included this under Adaptation of content and recommendation, since sug-

gesting a tag is a form of content recommendation, instead we picked it off

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because some SASs7 use a more complex mechanism of annotation support

that is not just content adaptation.

(ii) Privacy and controllability is defined as an auxiliary task since it supports other

tasks to achieve the overall goal outlined above. Privacy and controllability are

issues which are addressed by an increasing number of systems and are becom-ing particularly relevant in the context of interoperability, as discussed below.

Controllability is intended to be the ability of the user to control her own data. It

includes user awareness and explanation mechanisms for transparent and user-

controllable personalization. This is an auxiliary task for Adaptation and per-

sonalization, since it clearly impacts on the personalized output (what the user

can see, can do, can be aware of, etc.), but it is auxiliary to User and context

modeling and management as well, since constraints and conditions for data

scrutability and exchange are connected with this task.

(iii) Trust modeling and social networks is another auxiliary task which can sup-port the Adaptation and personalization and User and context modeling and

management tasks. The phenomenon ofsocial networks, originally studied by

sociologists in the real world, grew on the Web as well, pushed by the tools

made available by social software discussed in Sect. 2. The consequence is a

heavy need for associated trust- and reputation-management systems, advocated

both in Web 2.0 and in the Semantic Web. Web 2.0 allows everybody to add

content, typically without any control over what users enter. Building distrib-

uted mechanisms to verify the reliability of users and their content has become a

critical issue. In the so-called Web of Trust, strategies are under developmentto semantically annotate resources by using trust and reputation markers. The

Semantic Web specifically introduced a proof layer that aims to provide guar-

antees about distributed semantic resources. The focus, in this case, is on both

the authors of content and the processes of exchange, inference and integration

carried out by intelligent agents.

The attention paid to trust and social networks is growing in adaptive hypermedia sys-

tems, as well as on the Web, and in SAS it is particularly relevant. For this reason we

introduced a specific task that addresses these issues. In current systems, trust values

and relationships between users are typically used to define the users social context,which is modeled in terms of the users social networks. These relationships between

users are then usually used to estimate the users trust in other users, and these trust

values can be, and are often, used by recommendation algorithms.

(iv) User and context modeling and management is a complex task that deals with

representing and managing user and context models. It has two sub-tasks that

correspond to two modalities to extend the knowledge about users and context.

These sub-tasks are particularly relevant in our Functional Model since both

of them can be greatly improved by the use of semantic techniques.

User and context models extension by reasoning, is the sub-task that aug-ments user and context models by making inferences from data in such models.

7 For instance, the systems described in (e.g., Bateman et al. 2006; van der Sluijs and Houben 2008), that

will be analyzed in Sect. 7.3.2.

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Kobsa et al., in the work mentioned above, identify three main types of rea-

soning (deductive, inductive and analogical8 reasoning), and all of them can

employ weak or strong semantic techniques. A typical application is making

class-membership inferences by reasoning over ontologies; other applications

include inferring similarity between users by computing the semantic distancebetween their models or between their tag clouds.

User and context models exchange and integration, is a sub-task that allows a

SAS to extend the models of users and context, by acquiring these data from

other applications. It may acquire unknown values of features or refine a value

with fresher or more reliable data. But it can also update the user and context

models by introducing new features and corresponding values.

(v) Domain modeling and management is a task for the representation of the domain

knowledge and its eventual update and extension.

Domain knowledge and service exchange and integration is a sub-task that

regards the extension of the content (and even services) that will be person-

alized, by using external sources. The possibility of using semantic techniques

and, in particular, shared ontologies described with standard markup languages,

initially attracted several researchers in the adaptive hypermedia community.

It gave the opportunity of extending the adaptive techniques outside of a local

context, opening the way to the recommendation of open corpora of resources

besides closed sets of already known resources. Brusilovsky (2001) and Henze

and Nejdl (2002) provide a definition of Open Adaptive Hypermedia System(OAHS) as an adaptive hypermedia system that operates on an open corpus of

documents.

Finally, to complete the overview of the Functional Model of tasks and sub-tasks, we

notice that each task makes use of some type of knowledge to accomplish its goals.

Considering the systems we reviewed and the main reference models for Adaptive

Hypermedia Systems, such as AHAM (Bra et al. 1999) and the Munich Reference

Model (Koch and Wirsing 2002), we identified ten main types of knowledge, often

called model in the literature. In Fig. 1 they are very roughly represented within

rounded black boxes. Typical SASs use the semantic approaches seen in the previous

section to represent these models or a set of them. Therefore, for example, we can

have a strong SAS that represents the user model as a collection of RDF statements

and a weak SAS that annotates the domain items by using social tags.

In more detail, the types of knowledge (models) we have identified are about: the

domain (including knowledge related to domain concepts, contents and also services),

the user (e.g., user characteristics, user knowledge, usually an overlay of the domain

model, interests, preferences, etc.), the context and device model (the definition of

context is discussed in Sect. 6). Besides these, we have identified three other types

of knowledge that could have been included in the definition of user knowledge, butwe decided not to include there, since several SASs represent them as independent

8 The authors use the term analogical reasoning to mean all the approaches which are based on recog-

nizing similarities between users (Kobsa et al. 2001).

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models: knowledge about the users interaction with the system, her actions and behav-

ior (similar to Kobsas usage data), knowledge about the users social network, and

knowledge about users goals and plans. Other types of knowledge are specifically

related to the adaptation process and regard the model of presentation and naviga-

tion of the hypermedia, which can be static or dynamic, the model named activity,describing activities that the user has to perform (e.g., models to represent learning

activities), and the model ofadaptation, which, according to the main reference mod-

els for adaptive hypermedia systems, specifies the rules that describe the systems

adaptive behavior. It is worth noting that rules could be used to implement the adap-

tation behavior or just to describe the model of the adaptive behavior of the system,

letting the real implementation use other approaches. For example, the representation

by means of rules could be useful to the goal of trusting the exchanged resources, as we

will see in Sect. 8. Finally, we identified a last type of knowledge named integration,

which refers to the integration models that can be used to support the integrationof heterogeneous user models or domain content or services from different SASs. It

can be likened to the component referred, in Berkovsky et al. (2008), as an auxiliary

knowledge base used for the integration of knowledge (integration of User Models,

in their approach), allowing, for example, reconciliation of the ontologies used by

various recommender systems.

4 Classification of strong and weak SASs

In this section, we describe the matrix we have defined to analyze SASs, which is

shown in Fig. 2. The x-axis (columns) denotes the semantic techniques used by the

adaptive system (as defined in Sect. 2) and the y-axis (rows) gives the tasks and

sub-tasks defined in the SAS Functional Model (Sect. 3). Therefore, the x-axis repre-

sents a discontinuous-continuum from weak to strong semantic technologies.9 Recall

that strong semantic techniques are those using languages and technologies for the

Semantic Web. It includes ontologies, ontological languages, rule languages, query and

reasoning engines. The specific technologies we considered are described in Appen-

dix A. Weak semantic techniques are those techniques and approaches that somehow

add meaning to web resources. They include Web 2.0 social annotations, with partic-

ular attention to tags and folksonomies, and what we called middle-ground and mixed

approaches, namely approaches that lie between the two extremes of the discontinu-

ous-continuum and encompass approaches that integrate Semantic Web (SW, in the

matrix) and Web 2.0 and emerging technologies such as microformats and RDFa. On

the y-axis, we have placed the SAS Tasks and Sub-Tasks described in Sect. 3, with a

slight difference regarding Trust and Social Networks. Given that trust and social

networks are currently mainly used in SASs as an approach to model the user social

context and to improve recommendation algorithms using this model, we decided

to simplify the matrix by including this task just under Adaptation of content andrecommendation.

9 It is a continuum since these technologies are on a line from low to high semantics but this line is

discontinuous since the steps toward the higher level are not simple upgrades.

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Fig. 2 Matrix representing the distribution of SASs with respect to the tasks they perform and the semantic

technique they use

Each cell contains the SASs that exploit the technology specified on the x -axis to

accomplish the task specified on the y-axis.10 Some remarks:

(a) To be placed in a cell, a SAS must accomplish the specified task by using the

specified semantic technology. Thus, for example, a system that provides user

modeling functionality but mainly uses the semantic representation of domain

knowledge for interoperability (not for user modeling) is placed under Domain

10 SASs have been selected using, as sources, the main journal and conferences in the Adaptive Hypermedia

and User Modeling community (see www.um.org) and subsequently workshops, conferences and journals

regarding semantic technologies and the combination of semantic technologies and personalization. The

list of reviewed systems is in Appendix B.

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knowledge and service exchange and integration and not under User and con-

text modeling and management.

(b) Similarly, if a SAS uses semantic techniques to accomplish a task and this choice

indirectly improves another task, the system is classified according to the task

that, in our opinion, most benefits from the technique.(c) SASs that use mixed approaches, integrating Semantic Web techniques and Web

2.0 social annotations, are placed in the column named middle-ground and mixed

approaches and the specific semantic techniques they use are indicated in brack-

ets. However, they can also be placed in other columns if, for a specific task, they

use folksonomies or ontologies alone.

Note that the goal was not to produce an exhaustive classification but, rather, to give

an overview of the main trends in this field of research and an indication of promising

approaches. In particular, it is important to observe that:

(i) the non-homogeneous description of systems and, clearly, the subjectivity of the

author of this review, influenced the classification,

(ii) this review focuses special attention on weak semantic adaptive systems, since

this topic has not, to date, received much attention.

Figure 3 integrates the matrix, providing a snapshot of the distribution of SASs with

respect to the type of knowledge they represent using the semantic techniques on the

(x -axis). Therefore, on the y-axis we placed the main types of knowledge that canbe used by an adaptive system to accomplish its tasks and which a semantic adaptive

system can represent by means of semantic techniques. In order to place these various

types of knowledge on the y-axis of the map, we have approximately used the criterion

of descriptive vs operative knowledge, making reference to the specific use of these

types of knowledge in typical adaptive systems. Thus, very roughly, we have placed

the domain knowledge at the upper edge of the y-axis (descriptive knowledge) and the

knowledge regarding adaptation at the lower edge (operative knowledge). Between

these extremes, we have all the other types of knowledge, and all of them can be

represented by using weak or strong semantic techniques. For example, SASs thatannotate the domains items by means of semantic technologies (such as OntoAIMS,

PRF, OntobUM), have been placed in the upper right part of the map, while SASs that

annotate them by means of social annotations (such as AnnotatED, TBCF, etc.) have

been placed on the upper left part of the map.11

Notice that all the SASs in Fig. 3 are represented in this map too, placed in corre-

spondence of at least one type of knowledge. The reason is that semantic techniques

can only be used for a task if a semantic representation of the required knowledge is

available .

The following of the paper will analyze each task and sub-task with respect to bothstrong and weak semantic techniques.

11 We intentionally use the term map, and not Cartesian plane, to emphasize that the position of the various

types of knowledge is just very approximately based on the criterion above.

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Fig. 3 Distribution of SASs with respect to the semantic techniques they use to represent the different

types of knowledge (domain model, user model, adaptation model, etc.) in order to accomplish the differenttasks

5 Domain modeling and management

As explained, this task concerns the representation and management of the domain

knowledge, including domain concepts and specific content items. It represents the

concepts that will be used to annotate and index resources. With respect to our focus of

analysis, resources can be annotated by using concepts that refer to an ontology/taxon-

omy or by annotations defined by end-users. The sub-task most relevant to this analysis

regards the possibility to extending the content (and even services) that will be person-

alized, by using external sources, building the so called Open Adaptive Hypermedia

System (OAHS) introduced in Sect. 3.

5.1 Domain knowledge and service exchange and integration

Figure 4 provides an overview ofissues relevant to this sub-task in strong (upper part

of the figure) and weak (lower part of the figure) SASs. It is important to notice that

the table includes only issues to which strong or weak semantic techniques have been

applied in the SASs we analyzed. Issues that can be relevant to the task itself but are

not related to the use of semantic web techniques and issues not addressed by any

SAS are not included in the figure. More in detail, the first column identifies relevant

issues for the task, the second column identifies possible approaches to face them and

the third column provides examples of SASs that follow such approaches.

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Fig. 4 Domain knowledge and service exchange and integration sub-task

5.1.1 Strong semantic techniques

In the past, different XML-based interoperability standards have been developed to

allow organizations to work together, in different domain fields. In the field of eLearn-

ing, for example, several standards describe the learning objects (LO), such as IMS

LIP, IEEE LOM and SCORM. Appendix A provides details of the most used stan-

dards in adaptive systems and a discussion can be found in Dolog and Nejdl (2003),

Aroyo et al. (2006), and Kravcik and Gasevic (2007). These standards, which specify

import and export format, are very useful for enabling interoperability between stable

and centrally managed systems, but they are not the solution in many other situations,

since they do not admit flexibility in knowledge representation. The Semantic Web,

on the other hand, allows this flexibility mainly because, while XML Schemas are

used for modeling XML documents, RDF is used to model Knowledge: XML/XML

Schema is a data modeling language, while RDF is a meta-data modeling language

(Nilsson 2001).

However, representing data with ontologies has several problems too, when the

objective is to automatically and dynamically exchange and integrate data. In a very

flexible and open scenario, each application can define its own ontology. As a conse-

quence, to exchange knowledge, these applications have to manage the semantic heter-

ogeneity of the annotations that refer to the concepts of their different ontologies. This

means facing the problem of ontology alignment, or matching, and, when necessary,

of ontology merging. Several techniques have been proposed to face these problems

in the Semantic Web community. Shvaiko and Euzenat (2005) provide a survey of

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methods and algorithms for the former problem, others, such as Stumme (2005), deal

with the latter. Up to the time of writing, we did not find significant examples of

SASs that perform an automatic alignment of domain ontologies. Instead, there are

SASs that perform manual or semi-automatic mappings. HERA (Vdovjak et al. 2003),

for example, defines an integration model ontology which is used by the designer tospecify the links between ontologies. Other SASs use ad hoc transformations. iFanzy

(Bellekens et al. 2007), for example, integrates several sources transforming them into

OWL/RDF and using SKOS to describe relations between concepts.

Other approaches propose to bypass the problem of ontology alignment by using

reference ontologies, onto which all applications that wish to interoperate must map

their own ontology (Bodoff et al. 2005). Some of these ontologies could also be

based on the RDF bindings of pre-existing interoperability standards.12 Using refer-

ence ontologies (the most typical approach in current SASs), could seem similar to

using XML-based standards. However, even if the flexibility of defining its ontologyis reduced, each application can describe instances and their relationships (e.g., RDF

statements) freely. Moreover, mixed approaches are also possible, for example by

extending the reference ontology to model specific domains.

One of the most representative systems with respect to this issue is the Personal

Reader Framework, PRF (Dolog et al. 2004a; Henze 2005). The authors explicitly

state the necessity of using the existing standards as much as possible, to favor inter-

operability. PRF is conceived to provide an environment for designing and running

Personalization Services which are able to understand RDF-annotated resources

using standard ontologies for learning materials, such as LOM and IMS, and alsospecific domain ontologies which describe the particular subject being taught.

Besides the issue of knowledge representation and alignment, another issue con-

cerns the exchange management of annotated resources. Interoperability is usually

provided using Web Service technology13 and there are several examples in the field

of adaptive systems as well. The combination with semantic representation of services

is at the basis of the Semantic Web Services that can be considered a landmark tech-

nology for semantic interoperability (Martin et al. 2004). In PRF, the Personalization

Services are defined as Semantic Web Services registered in a service registry using

the Web Services Description Language (WSDL, see Appendix A). Each one pro-

vides some adaptive functionality, such as, recommending learning objects, suggest-

ing exercises, etc. At run-time, Personal Reader instances are generated by plugging

together a number of these Personalization Services. PRF was developed as a frame-

work for personalized eLearning but, over time, the concept of Web Services which

offer personalization functionality has been generalized to other domains such as pub-

lication viewing and music recommendation. MyEar (Henze and Krause 2006), for

example, provides a Personalization Service which searches the Web for podcasting

RSS feeds that fit the users taste in music and combines items of this feed to produce

12 Notice that the definition and improvement of the RDF binding of most of these standards is an ongoing

work.

13 A Web Service is defined by the W3C as a software system designed to support interoperable machine

to machine interaction over a network. Frequently, they are implemented simply as Web APIs hosted on

servers accessible to clients.

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a personalized podcasting feed. Notice that RSS feeds are becoming a common way

to collect and integrate frequently updated information on the Web.

Other examples of projects that use Web Services are COMPASS and SmartWeb.

COMPASS (Van Setten et al. 2004) is a mobile tourist application. It is defined as a

Web Service based on an open platform, which allows third parties to integrate newservices into the platform, to publish them in the public web service registry and to cor-

relate them to the registry ontologies. The descriptions of the Web Services, expressed

in WSDL, are extended with OWL. SmartWeb (Sonntag et al. 2007), a follow-up of

SmartKom (Wahlster 2006), is a wide-range project that focuses on the development

of multimodal user interfaces to distributed and composable Semantic Web Services

on mobile devices. The ontological infrastructure is composed of a Top-Level Ontol-

ogy (SUMO plus DOLCE), coupled to domain-specific ontologies. The Web Services

are described by using OWL-S. Ontologies are used for the semantic representation of

user queries (ontology-driven question analysis) and for the composition of the answerby querying different Web Services.

5.1.2 Weak semantic techniques

In Sect. 2 we defined the weak semantic web to be those approaches that semantically

enrich Web resources but, for different reasons, do not use strong semantic techniques.

A typical reason for not using ontologies is the need to annotate resources in insta-

ble domains, subject to frequent changes. In this case, instances could require to be

annotated with new and evolving metadata categories. Another reason to choose weakapproaches is that the definition of ontologies is directly influenced by the authors sub-

jectivity, which can conceive the domain concepts and their relationship in a different

way from the users of the annotated resources. This gap can be a problem for informa-

tion retrieval. For example, non-specialists often find it hard to retrieve resources from

art collections that have been classified by specialists, so several researchers, such as

Trant (2006), have started to introduce social tagging as a complementary approach.

However, considering the specific task of interoperability, using only Web 2.0 social

annotations is surely less powerful than Semantic Web techniques. Much more oppor-

tunities come from middle-ground and mixed approaches, but, up to now, very few

adaptive systems use them. As Fig. 4 shows, the main issues for this task are similar

to the issues in the previous section. A difference is that in weak SASs, heterogeneity

managementis not necessarily a problem but it can be a goal, consisting of obtaining

multifaceted annotations for a given resource.

As in the previous section, we provide a brief description of some systems that

face the issues in the figure using different approaches. However, note in advance that

we did not find any very representative Semantic Adaptive Systems. Some are very

slightly semantic, others very slightly adaptive and others slightly related to the task

of exchange and integration. Moreover, we did not find examples of social annota-

tions imported into the SAS and used to enrich resources (on the contrary, we will

see examples of this approach in Sect. 6.1.2, concerning the interoperability of user

models). We only found examples of systems where social annotations are used to

index the imported resources (as in Knowledge Sea II) or are managed to be exported

using strong semantic formalisms (as in the other two examples).

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In more detail, Knowledge Sea II (Ahn et al. 2006; Brusilovsky and Henze 2007)

is a slightly semantic open adaptive system (a very representative OAHS), which auto-

mates the hypertext construction and the indexing of learning resources on the Web.

Social annotations, together with other statistical techniques, are used to achieve this

integration of resources. The automatic indexing is accomplished by calculating andvisualizing, for each page, some indicators based on social annotations provided by

users to pages. In this way, new documents can be added to the system as soon as a

user encounters them during navigation, providing, at the same time, adaptive naviga-

tion support. GroupMe! (Abel et al. 2007c) is an open slightly adaptive system which

allows users to import multimedia Web resources, tag and group them. The peculiarity

of the system is that resources, tags and groups are transformed and stored as RDF

descriptions and made available for export. For example, a photo imported by Flickr

is integrated into GroupMe!, transforming the Flickr image title into the Dublin Core

element title and annotating the user tags using a public Tag ontology. Like GroupMe!,Revyu (Heath and Motta 2008) is an example of bridging between Web 2.0 and the

Semantic Web. It is a recommender that is slightly related to the issue of exchange

and integration. The main reason to mention it here is that it is an example of SAS that

uses the microformats technology (the hReview microformat, in particular) to export

data, while it uses an RDF binding of hReview to store data in its RDF repository. In

this way, applications that use the hReview microformat can import such data and data

in the Revyu system can be internally managed using strong semantic techniques.

Concluding, we note that most of the social tagging systems have APIs for import-

ing and exporting tagged resources. In this way, it could be possible to achieve severalforms of data integration and exchange between applications. Besides APIs, a simpler

way to exchange data is using RSS feeds from tagging systems. These data can then

be imported and integrated with other resources and tags, but currently in this task we

have only examples of SASs that export RSS feeds.

6 User and context modeling and management

The distinction between User and Context models is widely debated. Some authors

include some features regarding users, in particular short-term features, into the Con-

text model, while others include all features regarding the user into the User Model.

Still others model the user as a component of the Context. A recent discussion of this

issue is in Brusilovsky and Milln (2007). The authors state that there is no definite

agreement about what should be included into the area of context. It depends on the

community to which the researcher belongs. The user modeling community has a

user-centric perspective while the mobile and ubiquitous computing community has

a context/device-centric perspective. According to the Functional Model in Sect. 3,

the User and context modeling and management task is structured in two sub-tasks

that specify the main ways of extending knowledge about users and context by using

semantic techniques, namely: acquiring knowledge from external sources and appli-

cations and reasoning on the currently available knowledge to infer new knowledge.

In the Ubiquitous Computing community, the main approach is the former, while in

the User Modeling community the foremost approach is the latter.

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6.1 User and context models exchange and integration

The term syndication, which generically is used in the sense of making available a

feed for an information source, has been applied to the field of user modeling too, in

an environment of cooperating and decentralized user and context models (Houbenet al. 2005). An overview of issues and approaches to face this task using strong and

weak semantic techniques is provided in Fig. 5.

Fig. 5 User and context models exchange and integration sub-task

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In addition to the issues related to the exchange of Domain Knowledge, seen in the

previous section, we find two new problems in this task. The first is resolving possible

conflicts and contradictions between values of the user models of different systemsfor the same user, due, for example, to the different update time, different inference

method, imprecise manipulation, etc. (Aroyo et al. 2006). The second is managing

privacy related to the exchange and visibility of user data (Kay et al. 2002). Notice

that, according to the specific application scenario, some issues could be more or

less critical. For example, the issue related to user identification is less relevant if the

applications have a common identification system, if the identification is carried out

by a client-side agent or, finally, if a User Model Server is used to share user models

(e.g.,Heckmann and Krger 2003; Mehta et al. 2005; Henze 2005).

Considering the issue of representing users features, we can find both examplesof SASs that use reference ontologies to annotate the user features that have to be

exchanged and examples that use their own ontologies. The proposal of a general

reference ontology for modeling users comes from Heckmann and Krger (2003),

who defined a General User Model Ontology, GUMO, to represent the user model

in a ubiquitous environment in a standard way. To this aim they introduce the defini-

tion ofsituational statements. A situational statement carries information about user

model features, context data or low-level sensor data. A specific language, UserML,

is defined to represent and exchange these statements. It extends the RDF triples with

predefined semantic slots, so that the triple is in fact a 7-tuple which includes temporaland spatial restrictions, meta-data about ownership, privacy, evidence and confidence.

The idea is to allow more powerful, but still structured, statements about situations.

They also define the UbisWorld model which, besides the GUMO ontology, includes

a set of other six specific ontologies that aim to model context. The PEACH museum

guide, as described in Kruppa et al. (2005), implements the situational statements

model and the U2M UserModelService of UbisWorld which is used to inspect the

user model.

Following the approach of Heckmann and colleagues, most problems that may

happen when integrating partial user models concern conflicts14 about the exchanged

values of the user models between the systems involved, while the problem of seman-

tic heterogeneity management of annotations is not relevant.15 The main drawback of

this approach, also acknowledged in Heckmann (2005), is the necessity of

domain-specific features to properly model the user. Each domain may require specific

features to model the user and, moreover, the same user feature can be perceived and

classified in different ways in different domains.

Compromise solutions have been investigated to use well-established, but non omni-

comprehensive, ontologies using already existing standards, when available, in differ-

ent domains. The Learner API in the ELENA project (Dolog and Schaefer 2005), for

14 An accurate categorization of conflicts which may occur at different levels of the user models interop-

erability process is provided in Heckmann (2005).

15 Actually, the model includes the possibility of managing semantic conflicts due to annotations based on

different ontologies, however this issue is not deeply analyzed in their approach.

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example, uses a RDFS learner profile ontology, configured from fragments based on

three specifications: the learning performance of a student is defined with the IEEE

PAPI standard, the competencies that have to be achieved are represented with the

IMS RDCEO standard and, finally, the learner assessment is defined with the IMS

QTI standard. The problem the authors highlight is that none of the referenced stan-dards present their metadata in a way that makes it possible to use them in combination

as depicted above. Therefore, an RDF translation of these standards had to be devel-

oped, which made it possible to use them in combination. Moreover they add that this

RDF translation is unofficial, and they therefore view it as an important direction

for future standardization work that the standards use a common framework such as

RDF and the Semantic Web, to enable the added value of using the standards together

(Aroyo et al. 2006).

A different approach to user model interoperability is allowing each user modeling

system to define the user model ontology that better fits its needs. In this scenario,techniques are necessary to manage the semantic heterogeneity of the distributed user

models. A general framework, non-specific for SASs, for the integration of heter-

ogeneous User Models is provided in (Berkovsky et al. 2008). The authors define

this approach mediation of user models and explain that it requires a UM mediator

responsible for resolving conflicts and heterogeneities in the obtained user mod-

eling data using various reasoning and inference mechanisms. GUC, the Generic

User model Component of van der Sluijs and Houben (2006), is an example of this

approach. It creates mappings between the user models schemas of the SASs involved

in the exchange. These mappings are created manually by designers and expressedas SWRL rules that are executed every time user model data are exchanged between

these SASs. SWRL rules are also used to specify how to manage conflicts between

data imported from the user model of other SASs. In Carmagnola and Dimitrova

(2008), the exchange of user data is automatically performed through an algorithm

which computes the semantic similarities among classes of different users and domain

ontologies, combining measures borrowed from Word Sense Disambiguation theory

with other syntactic similarity measures.

Considering now the user identification issue, we note that it is critical only for

SASs that operate in open scenarios which require discovering the available adaptive

systems the user interacted with, and in particular those which stored some informa-

tion about that user. The framework ofCarmagnola and Dimitrova (2008), mentioned

above, automatically identifies users over different systems by considering them to

be a collection of properties and then comparing the values of common properties.

Such a comparison is held by the identification algorithm described in Carmagnola

and Cena (2009). A simpler approach to user identification uses reference ontologies

to represent the user. It is adopted, for example, in ELENA and works by matching

the annotations that identify a user on the basis of the ontology that describe learners

in a standard way. FOAF (Appendix A), could be another reference ontology useful

to this aim.

Considering the exchange management issue, we found two main approaches that

regard, respectively, the use of (Semantic) Web Services with public APIs that export

user features/values and the use of a semantic infrastructure for querying user fea-

tures/values, such as Edutella. These solutions are described in the work of Dolog

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and Schaefer (2005). The approach of using Web Services to support interoperability

has been proposed also in CHIME (Chepegin et al. 2003), a framework for adaptive

systems which extends AHA! Each component is seen as a service and each service

is defined on the basis of a specific ontology. Of particular relevance to this task is the

implementation of a Broker-based Discovery Service (Chepegin et al. 2005), whichis defined as an adaptive Web Service that allows AHA! applications to exchange

user models, and that uses a specific markup language for user modeling (MLUM).

Finally, another example of application that makes use of Web Services is the work of

Cena and Furnari (2008). They propose an approach for achieving user model inter-

operability by means of formally described semantic dialogues. In case of different

ontologies, the exchange is performed by means of negotiation techniques based on

dialogue models (Cena and Aroyo 2007).

Not only user model data, but also context data can be exchanged. In this case,

besides all the problems already seen for user models exchange, another critical prob-lem is the rapid update of data. These issues are faced, for example, in the MobiLife

project (Kernchen et al. 2006), where different recommenders share and exchange

information about the current context conditions of the user. The objective is to inte-

grate these data, cover any lack of information and make this kind of knowledge more

precise and complete. In Luther et al. (2005), the authors explain how OWL ontologies

and OWL-DL reasoning methods are used to enrich the description of contextual situa-

tions defined in terms of location, time, people and sensor information. Previously, also

other researches proposed service architectures based on context ontologies (e.g.,Chen

et al. 2003; Power et al. 2004).Related to context, another approach to managing interoperability and cross-

systems personalization is the definition of a Context Passport (Mehta et al. 2005)

that accompanies users on their travels through the information space. When interact-

ing with a system, the relevant context of use is extracted from this context passport,

related to a Unified User Context Model (UUCM), and is used for improving the

support to the respective information-related activity. In a similar way to GUMO,

the ontology at the basis of the UUCM tries to provide an integrated representation

of the user and the context model. In the proposed scenario, all applications must use

the same unified user and context model (UUCM).


Up to now, only a few weak semantic adaptive systems exchange and integrate user

and context features and all use middle-ground and mixed approaches. In more detail,

they exchange user-generated tags and use strong semantic techniques to map them

with respect to the data in their applications. User generated-tags can concern the user

herself or resources the user tagged. In the second case, tags describe the resource and

not the user, but they will be used by the importer SAS as a some kind of evidence

about the user, for example as a feedback about her knowledge or her interest in the

resource.

Considering the issue of automatic identification of the user on different SASs,

we found just one SAS (Szomszor et al. 2008) that tries to compare features of the

user on different systems, to uniquely identify it. In more detail, the authors use the

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Google Social Graph API, which includes a matching technique for cross-profiling

based on the user homepage, when available. Other SASs, such as CHIP and iCITY,

use a practical, semi-automatic approach, that is asking users a subscription to the

RSS feeds made available by the systems that interoperate.

RSS feeds are also an economical approach for the exchange management ofresources and annotations. iCITY (Carmagnola et al. 2007b), for instance, is a social

mobile guide with Importer and Exporter modules that allow the exchange of

user tags in the form of RSS feeds. Notice, however, that the authors have recently

developed some public APIs to increase the flexibility of the exchange. An exam-

ple of interoperability between iCITY and another, completely independent, adaptive

system, CHIP (Aroyo et al. 2007), is provided in (Wang et al. 2008). It presents an

approach to using iCITY user tags about cultural events in order to integrate dis-

tributed user profiles, with the ultimate goal of addressing the cold-start problem

in adaptive systems. The basic approach involves the re-use of user interaction data(tags) generated by one application in another application in a similar domain, for

providing cross-system recommendations. In this respect, it follows the previously

mentioned approach defined as mediation of user models (Berkovsky et al. 2008).

CHIP is not a tagging system. Artworks are annotated by using external vocabu-

laries and thesauri and some its own concepts. Therefore, a mapping with iCITY

tags is necessary. This heterogeneity of annotations is managed by the authors by

using the Simple Knowledge Organization System (SKOS) Core Mapping Vocabu-

lary Specification, created for linking thesauri to each other with relationships such

as equivalent concept, broader, etc. The mapping between the iCITY user tags andthe CHIP concepts is realized in two steps: (i) identification of the type (e.g., creator,

place, material, etc.) of tags; (ii) tag mapping by using SKOS Core mapping relations.

Another approach to face this issue is to use a reference ontology to understand

and use the exchanged resources. This approach is at the basis of the proposal of

Heckmann et al. (2007) of extending their user and context ontology GUMO by tak-

ing into account Web 2.0 user annotations, and in particular tags, adopting approaches

of tag-space mining described in Sect. 6.2.2. Instead, Szomszor et al. (2008) col-

lect the user tag cloud on different systems, unify them in a unique profile and use

the Wikipedia oltology as the reference for the mapping process, as we will see in

Sect. 6.2.2.

Finally, regarding privacy management, to date, we have not found any SASs that

implement privacy management. Some ideas come from Schwarzkopf et al. (2007),

that will be presented in Sect. 8.2, while we did not find any SAS that describe

approaches for conflict resolutions of user data (as on the contrary we saw in the

previous section).

6.2 User and context models extension by reasoning

An overview of issues and approaches to face this task using strong and weak semantic

techniques is provided in Fig. 6.

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Fig. 6 User and context models extension by reasoning sub-task


The issue of inferring user and context knowledge by reasoning over ontologies is

tricky and contentious, even from a terminological point of view. The basic way of

reasoning over an ontology is to perform basic deductions to make explicit some

implicit ontological knowledge. However, this kind of reasoning is not always suf-

ficient. For example, it can be necessary to define complex relationships among

concepts, requiring composition operators for properties, or specify rules that infer

non-implicit knowledge. In these cases a rule language is necessary. Semantic rule

languageshave been defined; however, when the interoperability of rules is not required,

another possibility is to use common non-semantic rule languages. Hatala and Wakkary

(2005), for example, explain that, in ec(h)o, they load their DAML+OIL ontologies

into Jess to infer all the missing relations in the RDF graph and note that semantic

rule languages like RuleML were not considered since other requirements such as

performance had a priority over the interoperability (Hatala et al. 2005).

In the field of eLearning, ontological reasoning has been often used to infer the

users level of knowledge of concepts. In these systems the user model is usually rep-

resented as an overlay of the domain ontology. A discussion about this issue can be

found in Brusilovsky and Milln (2007). The ontological representation is particularly

suitable to model concepts and relationships between concepts and to reason about

them to update the user model and allow the SAS to present the student suitable activ-

ities. Kay and Lum (2004) provide examples and interesting experiments on inferring

core terms from the finer grained terms in an ontology.

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An example of a SAS that uses ontologies and ontological reasoning to build the

user model is OntoAIMS (Denaux et al. 2005) and its interactive user modeling com-

ponent OWL-OLM. OntoAIMS is an ontology-based version of a previous system,

AIMS, and provides an information searching and browsing environment for learners

that recommends the most appropriate task to work on and aids them to explore domainconcepts and read resources related to the task. The user model, the knowledge about

concepts, resources and learning tasks are represented by means of ontologies. The

user model is built through a graphical dialog with the user. Each utterance is analyzed

to infer if concepts and relationships between concepts are used correctly or if there

is a discrepancy with the domain ontology. Reasoning over the domain ontology and

the conceptual model is also used to determine how to continue the dialog with the

user.

So far, we have dealt with the use of ontologies to model the users domain knowl-

edge, but ontologies and ontological reasoning can be also used to model other features.For example in ec(h)o, an immersive audio museum guide, they are used to model

interests, as a set of weighted concepts from the concept ontology. Ontologies are espe-

cially useful when the concepts to be modelled are related to each other and knowledge

of one concept can be used to make inferences about another. They can be also used to

resolve conflicts between evidences. Niu and Kay (2008, 2009) provide an interesting

example of this in the domain of context modeling. This work represents a significant

shift in the community of mobile and ubiquitous computing, as it uses ontologies for

reasoning, rather than for interoperability. PERSONAF is a framework for ontological

reasoning about smart buildings and the people in them. Reasoning is beased on PECO,a three layered-ontology. The first two layers model the inside of a building, using

mainly is-a and part-of relationships, while the third layer models the dynamics of

people, devices, sensors and location inside such building and is automatically accreted

using information from a range of sources, such as sensors. ONCOR is the reasoning

module, built as a set of resolvers, for interpreting all this knowledge. In particular, it

includes a set of resolvers that exploit subsumption relationships to tackle the problem

of uncertainty and sensor evidence conflicts in location resolution.

A final consideration concerns the modeling of social context, which has grown

in importance in recent years. To this aim, some systems, such as CHIP use FOAF

(Appendix A) to represent the relationships between users. However, in the reviewed

systems, we did not find examples of reasoning over social networks using FOAF. In

this respect, the recent initiative to enhance FOAF with a Web Rule Language such as

RuleML is interesting.


Considering that social annotation-based systems store tags and annotations inserted

by each user, we could observe that these systems are all intrinsically user modeling

systems. However, more specifically, there are examples of systems that further pro-

cess this knowledge to extend the user model and provide personalized services. In

most of the SASs we analyzed, users tags are used to infer the user interests as a direct

association with them. However, this inference can be compromised if: (i) users tag

resources in which they are not especially interested or (ii) they tag resources by using

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personal tags, opinion tags and so on, rather than tags that describe the resource. The

first problem is not very relevant if resources are bookmarks, since, typically, a user

bookmarks resources she is interested in. The second problem is not very relevant if

we consider the whole set of tags of a resources,16 but it can become a problem if

we consider the specific users tags. In this case, it would be necessary to identify thecategory the tags belong to. If a song about violence is tagged with music, crime

and sad, we cannot automatically assume that the user is interested in these things.

This issue probably represents the main problem in this task, however, up to now, it

has not been analyzed in depth with results that can be generalized. Sometimes it has

been simply ignored, other times it has been partially bypassed by adopting collab-

orative approaches. It has been recognized as a basic problem in only a few cases

(e.g.,Szomszor et al. 2008) and studies have been done to validate the approach.

The main approaches we found in current SASs to reason on tags and extend the

user and context models are summarized in Fig. 6. They can be split in approachesthat use cognitive analysis of tags and approaches that use tag space mining tech-

niques. Considering the first approach, Szomszor et al. (2008) semantically model

user interests by mapping user tags on a multi-domain model. The basic assumption,

which is supported by the authors studies, is that the tags used to describe resources

mostly reflect their interests. Given that, firstly they clean the users tag cloud collected

from different tagging systems (see Sect. 6.1.2 for details), then they attempt to match

each tag to a Wikipedia category. Finally, they generate a FOAF file to express the

users interests using references to Wikipedia category URIs. In our view, choosing

the Wikipedia ontology as a multi-domain model is a pragmatic and effective solutionsince it is constantly updated by the community and includes acronyms and slang

terms, typically used in tags. Moreover, it allows to exploit the relationships between

categories and thus to make inferences that generalize or specialize the user interests.

While the above work used tags to infer user interests, Carmagnola et al. (2007a)

analyse how other user properties can be inferred from tagging. In particular, the

authors consider tagging from two points of view. The act of tagging gives informa-

tion about the users interest in the tagged content, her inclination to interact with the

system, her tendency to organize contents, etc. Instead, the meaning of tags (analyzed

by means of WordNet), is used to infer other user properties, such as her knowledge of

concepts, her trust in the community and her creativity. The approach is under devel-

opment in iCITY (Carmagnola et al. 2008), where these features have to be combined

with a user model represented as an RDF ontology, where the user interests are an over-

lay of the domain ontology and the user tags are an overlay of tagged instances of the

domain. Nauerz et al. (2007) also sketch ways to infer users interests, preferences and

knowledge from their annotations. They also introduce the idea of building or refining

user models by using user-to-user annotations, which can be useful to model passive

users. In contrast, Schwarzkopf et al. (2007) use mining algorithms to automatically

learn a taxonomy of tags and then to determine how strongly the user is associated

with a specific tag of the taxonomy. The taxonomy groups several tags around a set of

16 As we observed in Sect. 2, after some time, a stable pattern of tags tend to emerge for each tagged

resource (Golder and Huberman 2006) so that the entire set of tags provides a multifaceted description of

the resource, where personal and non-descriptive tags tend to have lower occurrence.

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concepts, therefore by mapping her tags to the taxonomy, they can infer the concepts

the user is interested in, even if these concepts are not explicit in the users tags. Even

though the authors admit that the approach simplifies the complexity of the linguistic

variations, the results seem promising. Another approach to mining the tag space is

to use collaborative techniques to find similarities between users, as sketched in vanSetten et al. (2006). The basic idea is using similarities between tags entered by differ-

ent users to find similarities between these users. This idea is interesting, though there

are risks of basing recommendations solely on these similarities: classical collabora-

tive filtering infers sets of users with common interests, based on the ratings they give

to express their interest in items; differently, this approach obtains sets of users who

categorize items in the same way, showing a common view of items. Depending on

the meaning and category of the inserted tags, this common mental view could also

reveal common interests, but this could be not always the case.

7 Adaptation and personalization

The objective of this section is to provide some examples of systems that enhance

the adaptation and personalization task by using semantic technologies. Therefore,

it does not include references to SASs that mainly use semantic techniques for user

modeling, even if this results in better personalization. Such systems were discussed in

the previous section. Before analyzing this task, we refer the reader to other analyses

of personalization in the Semantic Web such as Baldoni et al. (2005) and Dolog and

Nejdl (2007).

7.1 Adaptation of content and recommendation

As Fig. 7 shows, we have identified two issues relevant with respect to the use of

semantic techniques: resource retrieval and information filtering. Resource retrieval

is not considered to be the same as general information retrieval, rather, it is a step in

the process of content adaptation, which can be enhanced using specific techniques.

Considering information filtering, the so-called content-based approach (see Sect. 3)

is the natural form of content adaptation when domain items are represented semanti-

cally. Despite that, semantic techniques have also been used to enhance collaborative

algorithms. In addition, we describe a new approach, which we call the social network-

based approach, that performs content personalization by exploiting relationships in

social networks.


The first step of content adaptation is typically querying the domain ontology (Dolog

and Nejdl 2007), often based on an explicit user query. The query can be expanded or

restricted, as in the Personal Reader Framework PRF, introduced above, depending

on the user and context profile and on the retrieval strategy (e.g., looking for super-

concepts if the query does not return enough results; looking for instances with the

lowest deviation from the user preferences, as in PPSS).

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Fig. 7 Adaptation of content and recommendation sub-task

Before the content can be selected or specific items recommended, reasoning can be

performed. This may be ontological reasoning (i.e., reasoning upon an ontology), or,

more generally, ontology-based reasoning (i.e., reasoning on knowledge that refers to

an ontology). CHIP uses ontology querying and ontological reasoning to exploit the

ontological relationships between concepts. Users are asked to rate as many artworks

as they wish on a five-point scale. Each artwork is described by several properties,

such as creator and theme. When the user rates the item, the ratings are propagated

to the properties. These ratings are used by the system to recommend other artworks

of potential interest to the user and related topics in the semantic network. A similar

approach is followed in PNS (Conlan et al. 2006).

The Personal Reader Framework, PRF, offers several examples of content adapta-

tion provided by its Personalization Services. For instance, the Personal Curriculum

Planner (Baldoni et al. 2006) is a Personalization Service that uses ontology-based

reasoning. Users knowledge, learning goals and courses are represented semantically.

The curriculum is planned by a reasoning engine whose input are RDF statements about

user knowledge and learning goals and which uses course prerequisites and effects

as a set of rules whose terms refer to the domain knowledge ontology. The result is

an RDF document that contains the sequences of courses that fit the users learning

goals and profile. Further examples of SASs can be found in Abel et al. (2007a) which

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describes several personalized applications developed within the REWERSE Network

of Excellence.

7.1.1.1 Content adaptation using trust modeling and social networks Following the

Functional Model in Sect. 3, Trust modeling and social networks is an auxiliarytask which can support the Adaptation and personalization and User and context

modeling and management tasks. In current SASs, this task is mostly used to support

content adaptation. Typically, it is used to define the users trust in other users and these

trust values are then used by recommendation algorithms. For this reason we decided

to address it as an approach of the Adaptation of content and recommendation task.

The principle ofpersonalized trustis that users of social software do not care much

about whether the user community trusts a particular user. Rather, users care about

whether people they trust (i.e., people in their own social networks) trust that user. By

propagating trust values through social networks, the system can calculate how muchone user should trust another. Personalized trust has been used to define recommen-

dation algorithms. The TrustProject (Golbeck 2005; Golbeck and Mannes 2006), for

example, uses a TrustOntology based on the FOAF vocabulary and calculates person-

alized trust for each user and specific topics. Effectiveness of its recomendations has

been tested in FilmTrust.17 Similar works are Ziegler and Lausen (2004) and Massa

and Avesani (2004)


Similarly to strong SASs, folksonomy querying is often the a preliminary step for con-

tent adaptation in weak SASs. Tag-based queries can be processed by using, for exam-

ple, meaning disambiguation techniques, tag clustering algorithms (as in Shepitsen

et al. 2008) and query expansion (as in De Meo et al. 2009). As we noted in Sect. 7.1,

content-based approaches are the most natural form of information filtering for sys-

tems that use semantic techniques. Tags are used as keywords that, together, describe a

resource item; user profiles are typically based on tags as well and are matched against

the content profile with different approaches, as summarized in Fig. 7.

The first approach, used for example in the Movie Recommender of Szomszoret al. (2007), calculates the users interest in instances using a sort of folksonomy

reasoning. Users ratings for a movie are propagated to the tag-cloud describing that

movie. In this way, the system can compute the personal tag-cloud of the user associ-

ated to each rating.18 The other approach in the figure, applied for example in Shepitsen

et al. (2008), defines the users interests on the basis of her tags (see the discussion in

Sect. 6.2.2 about the use of such approaches). In this way, both resources and the user

profiles can be represented as vectors of tags and classical techniques of information

retrieval, such as vector space models can be adopted (with the advantage that they are

applied only to the social annotations and not to the whole content of the resource).

17 http://trust.mindswap.org/FilmTrust/.

18 Notice the similarly of this technique to CHIPs ontological reasoning. Notice also that the Movie Rec-

ommender defines an ontology to represent movies and tags associated to each movie, but the technique

described above does not use it.

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So far, we have only mentioned content-basedapproaches. However we also found

an interesting case which uses social annotations and semantic analysis of such anno-

tations to improve collaborative approaches. TBCF, ofZhao et al. (2008), uses social

annotations to improve the effectiveness of neighbor searching in classical collabora-

tive filtering. Therefore, they do not use users annotations to directly compare users(with the problems discussed in Sect. 6.2.2), but to refine the collaborative filtering

algorithm. Two users are considered similar not only if they rated the items similarly,

but also if they tagged the items similarly. The idea is that similar tags reveal that

the users have similar cognitions over these items and similar

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