Metadata for Web-based Information Management through Ontology

Metadata for Web-based Information Management

through Ontology

Dickson K. W. CHIUSenior Member, IEEE & ACMDickson Computer Systems

Hong Kong [email protected],

[email protected]

Poon, Joe Kit Man Lam, Wai ChunTse, Chi Yung

Sui, William Hi TaiPoon, Wing Sze

Department of Computer Science,

University of Hong Kong

Ontology Dickson Chiu - update 2011 Metadata - 2

Towards a Semantic Web

WWW is an impressive success: amount of available information (> 1 Giga-page) number of human users (> 200 Mega-user)

The current Web represents information using natural language (English, Hungarian, Chinese,…) graphics, multimedia, page layout

Humans can process this easily can deduce facts from partial information can create mental associations are used to various sensory information

(well, sort of… people with disabilities may have serious problems on the Web with rich media!)

Where are we now? Web 1.0: info-centric Web 2.0: user-centric Web 3.0: semantic-centric …


www.digitalrhetoric.org/course/web1to3.jpg


Need for understanding Web info Tasks often require to combine data on the Web:

hotel and travel infos may come from different sites searches in different digital libraries Especially too much user provided content on Web 2.0 etc.

Again, humans combine these information easily even if different terminologies are used!


What is the Problem?

Consider a typical web page:

Markup comprise rendering

information (e.g., font size and colour)

Hyper-links to related content

Semantic content is accessible to humans but not (easily) to computers…


What information can we see…WWW2002The eleventh international world wide web conferenceSheraton waikiki hotelHonolulu, hawaii, USA7-11 may 20021 location 5 days learn interactRegistered participants coming fromaustralia, canada, chile denmark, france, germany, ghana, hong kong,

india, ireland, italy, japan, malta, new zealand, the netherlands, norway, singapore, switzerland, the united kingdom, the united states, vietnam, zaire

Register nowOn the 7th May Honolulu will provide the backdrop of the eleventh

international world wide web conference. This prestigious event …Speakers confirmedTim berners-lee Tim is the well known inventor of the Web, …Ian FosterIan is the pioneer of the Grid, the next generation internet …


Information a machine may see…

…

… …


Solution: XML markup with “meaningful” tags?

<name> </name><location>

</location>…

How about…<conf>

</conf>

<place>

</place>

Then how about…< 会议>

</会议 >

< 地点>

</地点 >


What Is Needed?

A resource should provide information about itself

also called “metadata” (data about data) Metadata capture part of the meaning of data metadata should be in a machine processable format agents should be able to “reason” about (meta)data metadata vocabularies should be defined


What Is Needed (Technically)?

To make metadata machine processable, we need:

unambiguous names for resources (URIs) a common data model for expressing metadata (RDF)

and ways to access the metadata on the Web common vocabularies (Ontologies)

The “Semantic Web” is a metadata based infrastructure for reasoning on the Web

It extends the current Web (and does not replace it)


Ontology in Philosophy - a philosophical discipline—a branch of philosophy that deals with the nature and the organization of reality

Science of Being (Aristotle, Metaphysics, IV, 1) studies being or existence as well as the basic

categories thereof trying to find out what entities and what types of

entities exist has strong implications for the conceptions of reality.

Ontology: Origins and History


An ontology is an engineering artifact [Neches91]: defines basic terms and relations comprising the vocabulary

of a topic area the rules for combining terms and relations to define extensions to

the vocabulary “An explicit specification of a conceptualization” [Gruber93] Formal specification of a shared conceptualization (of a certain

domain) [Borst 97]: Shared understanding of a domain of interest Formal and machine manipulable model of a domain of interest

Ontology in Computer Science


History of the Semantic Web Web was “invented” by Tim Berners-Lee (amongst others), a

physicist working at CERN TBL’s original vision of the Web was much more ambitious than

the reality of the existing (syntactic) Web:

TBL (and others) have since been working towards realising this vision, which has become known as the Semantic Web

E.g., article in May 2001 issue of Scientific American…

“... a goal of the Web was that, if the interaction between person and hypertext could be so intuitive that the machine-readable information space gave an accurate representation of the state of people's thoughts, interactions, and work patterns, then machine analysis could become a very powerful management tool, seeing patterns in our work and facilitating our working together through the typical problems which beset the management of large organizations.”


Adding “Semantics” External agreement on meaning of annotations

E.g., Dublin Core (http://dublincore.org/) Agree on the meaning of a set of annotation tags

Problems with this approach Inflexible Limited number of things can be expressed

Use Ontologies to specify meaning of annotations Ontologies provide a vocabulary of terms New terms can be formed by combining existing ones Meaning (semantics) of such terms is formally specified Can also specify relationships between terms in multiple

ontologies

Some Technologies of Semantic Web

RDF XML URI SPARQL XDI XRI SWRL XFN OWL API OAUTH …

Dickson Chiu 2011 Semantic Web-15

Stamp Example – Google Search

Now, suppose I Google for all red stamps Not very intelligent…


Red stampsStamps from Cambodia (Khmer Rouge)Stamps from the Red SeaStamps from the 140th anniversary of the Red CrossStamps with red dragons

Stamp Example – Structural Meaning

Not very intelligent, but how can a computer know what I mean?

When we structurally describe that a stamp is a stamp and red is a color.

Describing data in a structured way can best be done in a database.

Different databases can be connected.


Stamp Example – All about a Stamp


This is a stampThis is a stamp

This stamp is from the United KingdomThis stamp is from the United Kingdom

This stamp is designed by John Bryan DunmoreThis stamp is designed by John Bryan Dunmore

In 1980 you could buy this stamp for 1 centIn 1980 you could buy this stamp for 1 cent

Now it’s worth 3 eurosNow it’s worth 3 euros

This stamp is used between 1978 - 1981This stamp is used between 1978 - 1981

The picture on the stamp is a PO BoxThe picture on the stamp is a PO Box


XML Meaning is about understanding. To understand we need a language. A language starts with words. Things mean something in words. Online, we describe things with XML.

XML - Example


<?xml version="1.0" encoding="ISO-8859-1"?>

<collection name=”My stamp collection"> <stamp> <title>Red dragon</title> <country>China</country> <year>1984</year> </stamp> <stamp> <title>PO Box</title> <country>England</country> <year>1992</year> </stamp></collection>


RDF and RDF Schema

Resource Description Framework (RDF) We can’t understand words alone RDF is a data model for objects and relations between

them RDF Schema is a vocabulary description

language In addition, online grammar is required Describes classes and properties of RDF resources Provides semantics for generalization hierarchies of

properties and classes With RDF Schema we can define concepts and

make simple relations between them.

RDF Example


Predicate

This stamp is from England

subjectobject

hence from Europe.

RDF Schema Example


fromStamp Country

Continent

in


OWL

But, RDF schema is limited. A language needs more expression and logic to

make good reasoning possible. relations between classes

e.g., disjointness cardinality

e.g. “exactly one” richer typing of properties

That’s why OWL (The Web Ontology Language) was invented.

characteristics of properties (e.g., symmetry) BOTH OWL and RDF are standards of

www.w3.org

SWRL

Finally, to reason, you need rules. Rules are formulated in SWRL (Semantic Web

Rule Language)


SWRL Example I got this stamp

from my uncle. The rule for calling

someone my uncle is that one of my parents has a brother.


mother or fatherIson of brother

<ruleml:imp> <ruleml:_rlab ruleml:href="#example1"/> <ruleml:_body> <swrlx:individualPropertyAtom swrlx:property="hasParent"> <ruleml:var>x1</ruleml:var> <ruleml:var>x2</ruleml:var> </swrlx:individualPropertyAtom> <swrlx:individualPropertyAtom swrlx:property="hasBrother"> <ruleml:var>x2</ruleml:var> <ruleml:var>x3</ruleml:var> </swrlx:individualPropertyAtom> </ruleml:_body> <ruleml:_head> <swrlx:individualPropertyAtom swrlx:property="hasUncle"> <ruleml:var>x1</ruleml:var> <ruleml:var>x3</ruleml:var> </swrlx:individualPropertyAtom> </ruleml:_head> </ruleml:imp>


SPARQL Suppose, I want to search for a specific stamp. “I want all the red stamps, designed in Europe,

but used in the U.S.A., between 1980 and 1990”

We can use SPARQL (Protocol and RDF Query Language).

URI Because the web is decentralized and data is in

many places, not only language is important. Exchange of data between different machines is

key. To make a connection a machine needs a source.

For this, we use resource identifiers. Best known resource identifier is the URI

which consists of a name (urn) and a location (url)


XRI & XDI

URIs have international limitations and the need for data-exchange between machines is rapidly growing.

There is a successor: XRI (Extensible Resource Identifier)

There is a standard for sharing, linking and synchronizing data.

This standard is called XDI (XRI Data Interchange).


OAuth API

However, data is often protected. We need consent and a key to gain access. The key to certain data is described in an API

(an application programming interface). An open standard for accessing (authentication)

the API is OAuth.



Berner-Lee’s Architecture

Data Exchange

Semantics+reasoning

Relational Data?

?

???

???

???

• Relationship between layers is not clear• OWL extends of RDF / schema

SWRL

OWL


Ontology Elements Concepts (classes) + their hierarchy Concept properties (slots / attributes) Property restrictions (type, cardinality, domain, etc.) Relations between concepts (disjoint, equality, etc.) Instances

E-R diagram / UML diagram ??? Note: “Property” “Slot” “Relation” “Relationtype”

“Attribute” Semantic link type”


The Role of Ontologies on the Web

Ontologies provide a shared understanding of a domain: semantic interoperability

overcome differences in terminology mappings between ontologies

Ontologies are useful for the organization and navigation of Web sites

Ontologies are useful for improving the accuracy of Web searches

search engines can look for pages that refer to a precise concept in an ontology

Web searches can exploit generalization/ specialization information

If a query fails to find any relevant documents, the search engine may suggest to the user a more general query.

If too many answers are retrieved, the search engine may suggest to the user some specializations.

General e-business automation based on understanding web resource in order to facilitate intelligent (software agent) processing


Case study: Use of Ontology in an e-Marketplace

D.K.W. Chiu, J.K.M. Poon, W.C. Lam, C.Y. Tse, W.H.T. Siu, W.S. Poon. How Ontologies Can Help in an E-marketplace, European Conference on Information Systems 2005 (ECIS 2005), May 2005

Semantic Web vision is probably too ambitious A more realistic current application that has a

potential to become a killer application


Motivation

Compare some general-purposed e-Marketplaces (auction based)

e-Bay (HK): www.ebay.com.hk Yahoo Auction (HK): auctions.yahoo.com.hk Taobao owned by Alibaba.com: http://www.taobao.com

(See also Alibaba.com: http://china.alibaba.com/) Compare special-purposed e-Marketplaces

Airtickets: http://www.qunar.com/ Finding friends (!): http://www.meetu.hk/

Which one is better? Why? Key issue => capturing and applying domain

knowledge


What is an e-Marketplace?

Buyers

Supplierse-Marketplace

Aggregate requests from Buyers, contactpotential Suppliers,

match Suppliersand Buyers, exchange

bids and offers,generate e-Contract

Repository

Ontologies and Concepts

e-Negotiation dataAgreements- …

bids

bids

offers

offers


Problem Statements

Are there currently significant practical use of the Ontology from Semantic Web?

Match-making and beyond Software requirement engineering / negotiation Model and solve practical problems with CS &

ICT Cross-over multi-disciplinary research

IJSSOE: Dickson Chiu, Editor-in-chiefhttp://www.igi-global.com/journals/details.asp?id=34268


Example Ontology Clothing and Sales Negotiation

Quantity

PurpleRed

Discount

Total Amount

Refunding Policy

ColorSize

Appearance

Clothing

Unit Cost

Payee

Insured Amount Insurer Premium

{unordered} attributes: deposit, installment, pay-upon-delivery, ...

{unordered} attributes: brick red, crimson, ...

{ordered} attributes: small, medium, large, extra-large

{unordered} attributes: light purple, magenta, ...

Delivery Date

Sale Order

**

Delivery

Shipping Cost

Payment Terms

Insurance


Objective and Solution Approach How to elicit negotiation requirements? Semantic Web

=> Ontologies => help negotiators’ mutual understanding of issues, alternatives, and tradeoffs

Address semantic requirements of negotiation Reduce cost and improve effectiveness of negotiation

(avoid combinatorial explosion of issues) Development of an effective and efficient negotiation

plan Applications: e-Marketplace, Web-service

negotiation, agent negotiation, requirement negotiation…


Semantic basede-Marketplace Conceptual Model

Accepted Alternative ValueAccepted Offer

Trader

Recommendation

Matchmaking

Negotiation

Offer

Auxiliary Concept

IssueTask1..n

1..n 1..n

1..n

1

1..n1

Decision Plan

11

Ontology

nn

Alternative Value1..n1..n

Concept

1..n

1..n

1..n

1..n

1..n

1

1

n

1..n

nn

Base Concept

n

n

2..n

1..n

1..n

1..n

1..n

1

evaluates

drives1

1

1

nformulates

indivisibly relates to

nn

precedesn

n

1..n

resolves1..n1

1..n

1maps to


Overall e-Negotiation Process Design Methodology

Trader select agreed relevant ontologies

Trader identify issues

System maps issues into ontology concepts

System derive concept relations

System creation of agreement

Trader post (revised) preferences as offer

Trader product selection

[reject all matches/recommendations]

[accept offer]

[need to identify new issues]

System performs recommendation

System supported trader negotiation

[all issues are resolved]

[quit negotiation]

[need to identify new issues]

[need to revise tradeoff model]

[negotiation target chosen]

System check consistency of issues & concepts

[not consistent]

System performs matchmaking

[match not found]

[match found]

Trader specifies alternative values of issues

[trader change requirements]

System identifies alternatives

[consistent]

System formulate decision plan

Requirements elicitation phase

Decision phase

for each collection of co-related

issue

Requirementselicitationphase

Decisionphase


Requirement Elicitation Methodology

1. Traders select agreed ontology.2. Traders relate requirements to concepts in the selected ontology.3. System checks dependencies of concepts that constitute all the

requirements from the (refined) ontology map. Mutually dependent clusters of concepts determine the indivisible groups of requirements that have to be considered together so that effective tradeoff can be evaluated.

4. The system checks the consistency of all the concepts, issues, and their dependencies (Cheung et al. 2002).

5. For a consistent plan, the system can proceed to elicit the possible alternatives; otherwise we have to re-iterate from step 3.

6. According to the dependencies, the system can formulate a precedence graph of the requirements and requirements groups. Based on the precedence graph, an efficient decision plan can be determined.


Decision Phase Methodology The system

searches for the matching offers based on the trader’s preference attempt to rank them for the trader to choose

Trader may accept any matched offers or change his reservation price and attempt a negotiation with

those offers in order to seek for a more favorable one. If no matching offers are found, the system identifies near

misses and also attempts to rank them for the trader to choose. Trader change his mind to accept a near miss

or choose a near miss for negotiation. During negotiation, the system supports the user to make and

evaluate offers / counter-offers based on the decision plan (from previous slide) in a negotiation session as follows (Chiu et al. 2005).

Should new requirement issues arise in the decision phase (say, due to incomplete specification), the trader can we can go back to analyze the new issue and its relationships to the existing ones.

In real-life, the formulation of a decision plan may involve several iterations. This reflects the traders may not be able to understand all the inter-relationships among the issues in one shot.


Understanding Requirements from Ontologies

Perform graph search algorithm on the semantic map

Key requirements are preliminary identified in the first round (e.g., unit price, quantity)

For each identified requirement issue, check if an issue can be mapped directly to a concept. If not, see if an issue can be refined into a set of more

specific concepts a cost is refined into constituent costs that sum up to

it. Incomplete Ontologies

Introduce new concepts into the ontology map Relate it with to existing ones


Understanding Requirements from Ontology (Cont)

Perform graph search algorithm on the semantic map For each identified concept c,

Examine every un-visited node n adjacent to c in the ontology map.

For each such node n, see if the new concept is relevant to the negotiation problem.

Repeat until no more related new concepts can be identified.

Only after successful deal do we need to consider combining newly identified working concepts back to more concise real-life objects in specifying a agreement E.g., component costs need not shown to business

partner


Understanding Dependencies of Requirements from Ontologies

Functional dependency borrowed from fundamental relational database

concepts motivate this research The alternative for an issue is determined by the

alternatives(s) of other issue(s). E.g., delivery date and quantity -> cost of production

Computational dependency more obvious type of functional dependency hardwired computational formula E.g., insurance amount = percentage * cost of goods.


Understanding Dependencies of Requirement from Ontology

Requirement dependency (constraint satisfaction) Only after the determinant value is known can viable

alternatives be determined. E.g., whether a customer may pay by credit card,

bank draft, or remittance is evaluated according to the total amount.

Classification dependency A special type of requirement dependency in which

the classification of another issue is dependent on the outcome of an agreed issue.

E.g., customer tiering


Indivisible Requirement Components for Tradeoff Evaluation

Indivisible Components of Issues Cyclic dependencies among the concepts Tradeoff Evaluation

Topological sort of semantic graph gives negotiation plan

Determine Size

Determine Color

Determine Refund Policy

Determine Unit Cost, Quantity & Delivery Date

Determine Payment Terms

Determine Shipping Cost and Payee

Determine Insurance Premium, Insured Amount & Insurer

Determine Discount

Compute Total Amount


Understanding Possible Requirement Alternatives from Ontology

Alternative for requirements are often in discrete values cannot be expressed in numerical values not quantized in normal practices because of difficulties

in recognizing them, e.g., color for simplicity and convenience (size => S, M, L, XL)

The elicitation of options is streamlined when a complicated issue is decomposed into concepts(appearance => size + color + shapes)

Ontology provide explicit ordering of them (size => S < M < L < XL) implicit ordering

inheritance (“is-a”) hierarchies composition hierarchies


Exploring more trading opportunities

from Ontology

Improve the accessibility of automated agents to match functional specification

Intelligent software agents could represent buyers or sellers

e-marketplace acts as “broker” Consider shared ontology attributes and

constraints Map for cross-sale Group buyers or sellers together for higher

market efficiencies Better hints for data mining


System Implementation Architecture

Multiplatform Support Subsystem

WAP Gateway

SMS Gateway

Internet Messenger

Web Server

e-Negotiation Executing Subsystem

e-Negotiation Session Manager

Ontology Generator

e-Negotiating Matching Subsystem

e-Negotiation Process Generator

Task Organizer

Issue Dependency Editor

issuedependency

taskdependency

Ontology Maintenance Subsystem

Ontology Editor

Search Engine

Criteria & Issues Editor

ontology

CriteriaIssue

bids & offers e-Negotiation process

ontologyIssue

ontology

e-Negotiation process

revised ontology, issues

existing ontology

e-Negotiation Data & Repository

MultiplatformDevices


OWL Listing<owl:Ontology rdf:about="#Clothing"> <rdfs:comment>Sample Clothing

Ontology</rdfs:comment> <owl:Class rdf:ID="Clothing" /> <owl:Class rdf:ID="Appearance" /> <owl:Class rdf:ID="Color"> <rdfs:subClassOf rdf:resource="#Appearance" /> ... </owl:Class> <owl:ObjectProperty rdf:ID="hasAppearance"> <rdfs:domain rdf:resource="#Clothing" /> <rdfs:range rdf:resource="#Appearance" /> </owl:ObjectProperty> <owl:ObjectProperty rdf:ID="hasColor"> <rdfs:subPropertyOf

rdf:resource="hasClothAppearance" /> <rdfs:range rdf:resource="#Color” /> ... </owl:ObjectProperty> <owl:DatatypeProperty rdf:ID="size">  <owl:Restriction> <owl:someValuesFrom

rdf:resource="#Appearance" /> </owl:Restriction> </owl:equivalentClass></owl:Class>…</owl:Ontology>


SummaryFunction Traditional e-marketplace problem Contributions of Ontology

Match-making

Match-making is often ineffective because of the rigid definition of products of limited attributes.

Shared and agreed ontology provides common, flexible, and extensible definitions of products and requirements for match-making and subsequent business processes

It is difficult to specify complex product requirements because the relationships among attributes and values are ignored.

Complicated requirements can be decomposed into simple concepts for streamlining the elicitation of options

User interactions are limited to mainly manually, which is time consuming.

Accessible by automated agents through Semantic Web specifications for more business opportunities

Recom-mendation

Recommendations are often only possible within the same category.

Ontology helps elicit alternatives for recommendation.

Pre-set formulae for every type of product are needed for evaluation.

Ontology help recommendation by evaluating offers in terms of flexible overall scaling

Cross-sale and grouping of buyers and sellers with similar requests are difficult.

Matching grouping of buyers and sellers as well as cross-sale possible by inference with the ontology.

Negotiation No implicit ordering of alternatives. Implicit ordering of alternatives is elicited via inheritance.

Manual negotiation or inadequate negotiation support cause inefficient process and ineffective recognition.

Machine understandable semantics facilitate negotiation and automatic configuration of products and services as specified.


Conclusions Formulation of negotiation plan with maturing of

Semantic Web technologies Elicitation of negotiation issues, issue dependencies,

tradeoff, and alternatives Control the openness of issues Our algorithm verifies the completeness of elicited

negotiation requirements Negotiation processes are properly guided, recorded,

and managed For e-commerce activities are usually more structural

and repeatable (as opposed to political negotiations) Ontologies and plans are therefore reusable Negotiation automation with agents / integration with

EIS


Future Work

Formal models Elicitation of semantic distances enhancement of ontology-based matchmaking and

recommendation algorithms ontology-based cross-sale and up-sale grouping of buyers and sellers for combined

quantity deals mobile clients and constraint-based requirement

specification

Summary

Dickson Chiu 2011

Limitations of Current IM Technologies

Searching information Keyword-based search engines

Extracting information human involvement necessary for browsing,

retrieving, interpreting, combining Maintaining information

inconsistencies in terminology, outdated information. Viewing information

Impossible to define views on Web knowledge


Ontology based IM

Information / knowledge will be organized in conceptual spaces according to its meaning.

Automated tools for information maintenance and knowledge discovery

Semantic query answering Query answering over many documents Defining who may view certain parts of

information (even parts of documents) will be possible.



Agent-base IM An agent is a computer system that is capable of flexible,

autonomous action on behalf of its user or owner in order to meet its design objectives in a designated environment.

Many other definitions … Your own personal (digital) automatic assistant

knows about your preferences builds up knowledge base using your past can combine the local knowledge with remote services:

hotel reservations, airline preferences dietary requirements medical conditions calendaring etc

It communicates with remote information (i.e., on the Web!) All the above can be facilitated with ontology

Intelligent Agents & Ontology


Metadata Identify and extract

information from Web sources

Ontologies Web searches,

interpret retrieved information

Communicate with other agents

Logic Process retrieved

information, draw conclusions

Agent: B2C Electronic Commmerce

A typical scenario: user visits one or several online shops, browses their offers, selects and orders products.

Ideally humans would visit all, or all major online stores; but too time consuming

Current shopbots required too much programming Software agents that can interpret the product

information and the terms of service. Pricing and product information, delivery and privacy

policies will be interpreted and compared to the user requirements.

Information about the reputation of shops Sophisticated shopping agents will be able to

conduct automated negotiations



Example: Database Integration

Databases are very different in structure, in content Lots of applications require managing several

databases after company mergers combination of administrative data for e-Government biochemical, genetic, pharmaceutical research etc.

Most of these data are now on the Web The semantics of the data(bases) should be known

how this semantics is mapped on internal structures is immaterial


Example: Digital Libraries

It is a bit like the search example It means catalogs on the Web

librarians have known how to do that for centuries goal is to have this on the Web, World-wide extend it to multimedia data, too

Ontology encodes metadata But it is more: software agents should also be

librarians! help you in finding the right publications

Content Management via Metadata

album reviews

album pages

artist bios

How to build an inventory for collection and search of content objects?

How to deal with multiple content object types?

What contextual navigation should exist between these content objects?

How can we use metadata technique as the solution?

64

Example Album Ontology


album reviews

album pages artist bios

concert calendar

TV listings

Video Content Ontology Example

BSIM0012 66


Question and Answer

Thank you!Email: [email protected]

Date post:	01-Jan-2016
Category:	Documents
Upload:	yasir-mcpherson
View:	29 times
Download:	2 times

Metadata for Web-based Information Management through Ontology

Documents