Knowledge Building with Semantic Schema in Enterprise: The ARISTOTELE Approach

Knowledge Building with Semantic Schema in Enterprise:The ARISTOTELE Approach

Pierluigi RitrovatoDep. Of Electronic Engineeringand Computer Engineering, University of SalernoCRMPA – Research Center in Pure and Applied Mathematics

Keynote talk CISIS 20124-6 July – Palermo (Italy)

The ARISTOTELE project What ARISTOTELE is about Models & Methodologies Overall Picture

ARISTOTELE Knowledge Building Methodologies overview Technological Solutions

Other Available results The Architecture Already Developed tools

Conclusion and Future Works

Outlines

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FP7 EU funded Integrated Project Coordinated by CRMPA with a key-role of MOMA from technical and exploitation point-of-view Best combination for research and exploitation success

8 partners with end-user involvement Proven complementarities and experience

6.4 M€ (4.5M€ funding by the EC) for 3 years Challenging results with clear value for money

High strategic impact thanks to business orientation Two pilot partners involved in different domains (PHI and AMIS) Starting from an existing solution (Intelligent Web Teacher) Build on top of market leader enterprise & collaborative platform (Microsoft Sharepoint

20120) Presence of a partner with a role of innovator/early adopter (ENG)

Innovative approach to conceive relations among knowledge flows, learningobjectives, and creativity within the organization

General Information

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ScientificCoordinator

ProjectCoordinator

TechnicalManager


ARISTOTELE Partners

ARISTOTELE

Academic

Industrial

Pilot

Research

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In Enterprise contexts, organizational, learning, and social collaboration processes are often managed independently

What ARISTOTELE is about

ARISTOTELE aims to coordinate them through a sort of virtuous cycle where intangible values (creativity, competences, and knowledge) are tracked and collected in order to:

Be exploited in other processes

Improve/innovate other processes

Central to this virtuous cycle are: The worker The enabling role of

technologies

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• ARISTOTELE Key points:• A new way to establish and enhance relations

among knowledge flows, organizational and learning objectives, work practices, and creativity within knowledge intensive organizations

• A novel methodological and modelling ground, consisting of• Conceptual Models representing organizational assets in a

machine-understandable way • formalisation of knowledge using semantic schema and correlation

among key assets

• Innovative Methodologies operating on conceptual models• achievement of organizational and performance objectives


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• Key points of ARISTOTELE (cont.):• a set of “ARISTOTELE business Process Patterns”

related to knowledge intensive organisations work practices

• Processing (i.e. transforming , updating, …) assets available in the semantic models

• Representing the dynamicity of the conceptual models

• Supported by ARISTOTELE methodologies

• An innovative technological platform• Human-centric• Models & methodologies driven (in contrast to technology-

driven)• Built on top of state-of-the-art technologies (i.e. IWT,

Microsoft Sharepoint 2010)


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The top level is the starting point: inputs (organizational objectives and worker needs, preferences, …) “influencing” the key ARISTOTELE processes

The middle level embraces the key ARISTOTELE processes centred on collaboration: Building of personalized learning experiences Creation of Innovation Factory for collaborative innovation boosting Management and sharing of personal knowledge to be “reused” in different domains and tasks

The bottom level includes features supporting update and reuse of organizational knowledge

ARISTOTELE enabling building blocksand research areas (1/2)

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The top level is the starting point: organizational objectives and worker needs, preferences, … to “influence” the key ARISTOTELE processes

The middle level embraces the key ARISTOTELE processes: Building of personalized learning experiences on the basis of the top level inputs Creation of Innovation Factory (for collaborative innovation boosting) leaded by needs arising from

personalized learning experiences and customized according to the organizational objectives Management and sharing of personal knowledge to be “reused” in different domains and tasks

The bottom level includes features supporting update and reuse of organizational knowledge

ARISTOTELE enabling building blocks and research areas (2/2)

Modelling using Semantics

Innovation factory:•Collective intelligence, Innovation Factory:•Web 2.0 paradigm, collective intelligence•Methodologies and process to exploit collaborative network and to foster the innovation factories

Personalized learning:•Methodologies supporting formal/informalcollabroative learning

•Methodologies to access to learning offersstarting from requests in natural language

PWLE:•Methodologies to capitalize knowledgeelicited through informal and informalactivitiesInnovation factory:•Collective intelligence, Knowledge management:Methodologies and algorithms enabling:•Semi-automatic taxonomy construction•Alignment and mapping of domain ontologies•Semi-automatic ontology extraction•Capture and formalisation of knowledgeexchange

Innovation factory:•Collective intelligence, Methodologies for DSS for Human Resource Management

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Knowledge Model (KM) provides constructs for the representation

of enterprise knowledge entities, enterprise domain vocabulary, educational vocabulary

Competence Model (CM) provides constructs for the representation

of competences and their relations to other concepts such as context, activities, and objectives

Worker Model (WM) provides constructs for the representation

of worker including social, learning, working and personal goals

Learning Experience Model (LEM) provides constructs for the learning

experience needed to achieve a new competence or fill a competence gap.

ARISTOTELE Models

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Role of the Models: Integrated Schemas

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Reference Ontologies: used to represent and structure enterprise’s resources (e.g., worker profile, competences, project and activities, etc.) FOAF, DOAP, SKOS, SIOC, etc.

Organisation Ontologies: exploited to provide a shared classification of the resources available in the Knowledge base. to classify knowledge resources according to the context of the

enterprise and to provide a common access layer to heterogeneous resources daily produced by the workers (e.g., document, wiki, blog, etc.).

Characteristics of the ARISTOTELE Models

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How fit everything together

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A Model example: The Knowledge Model

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A model instance example

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Personal Break:




Other Available results The Architecture Already Developed tools


Outlines

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What A proposal for ontology-based knowledge

management platform that integrates methodologies (i.e., Knowledge Extraction, Ontology Matching and Merging) aimed to support life cycle of large and heterogeneous knowledge bases.

How The architecture relies on hybrid methodologies

applying computational intelligence techniques and semantic web technologies to provide a consistent and common access point to organisational resources.

ARISTOTELE Knowledge Building

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ARISTOTELE Knowledge Building

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Overview of Knowledge Building

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Inputs: Documents from a personal or organisational work environment

(e.g., project’s deliverables, publications); User Generated Content (e.g. blog, wiki entries); Curriculum Vitae;

Outputs: Unsupervised Conceptualisation represented by means of SKOS

scheme Classification of input resources according to Unsupervised

Conceptualisation Features and Approach:

Unsupervised approach; Multilingual NLP exploiting Wikipedia & Wikipedia Miner; Conceptual Data Analysis based on Fuzzy Clustering (FCM) and

Fuzzy Formal Concept Analysis (FFCA)

Knowledge Extraction

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Knowledge Extraction: Example

Taking as input Technical Report of an organisation the methodology carriesout taxonomieshierarchicalunsupervisedconceptualisation

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Knowledge Extraction: Workflow & Technologies

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Clustering X X

Hierarchical Clustering X X

Latent Semantic Analysis (LSA) X X X

Formal Concept Analysis (FCA) & FuzzyFCA

X X X X

Knowledge Extraction: Foundation (1)

• To face with time consuming execution of FFCA thefollowing approaches have been taken into account: Hybrid algorithm of FFCA: Incremental and Batch NoSQL DB storage of FFCA Input and Output (i.e., Mongo

DB)

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Knowledge ExtractionFoundation (2)

threshold T=0.6

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To prepare Organisation Ontologies (OO) atARISTOTELE Startup

To keep up to date and to maintain OO

To classify resources using OO

To support feature such as: Tag suggestion;

Find relevant document in PWLE wrt a context;

Find workers with specific expertises;

Dynamic faceted browsing of resources

Knowledge Extraction: Application

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Input: Source: Unsupervised conceptualisation, Target: Organisation

Ontologies Source: available competences, Target: required competences Source: ongoing task, Target: workers, documents

Output: let es and et be entities of <es, et, nst> where nst is a mapping

degree in [0,1] Features and Approach:

Semi-automatic approach (lexical and structural); Link discovery based on SILK technology in order to evaluate

matching degree among concepts or instances; A weighted average of different similarities: string-based

(Levenshtein); web-based (Wikipedia Link-based measure); corpus-based (Explicit Semantic Analysis).

Ontology and Instance Matching

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Ontology and Instance Matching:Example (1)

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Ontology and Instance Matching:Example (2)

Kinds of Deliverable

State of the Art

Methdologies

Project Y

Organization Ontologies

Modelrelated

Knowledge Model

Worker Model

Knowledge Model

TaskProject

Worker

ARISTOTELE Models ARISTOTELE Models

Worker B

Worker Model

Worker

Worker A

Project Y

ARISTOTELE Instances O.O. Instances ARISTOTELE Instances

Deliverable

Document

Context

Task:TitleTask:Description Document:Title

Document:TopicDocument:Description

Goal

Acquire knowledge on Social Network topic

Task:Goal

Topics

Social Network platform

Idea Management System

Project

Task:Project

Read suggested material on

Social Network platform


Ontology and Instance MatchingWorkflow/Technologies

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To align extracted Unsupervised Conceptualisation with respect to enterprise knowledge base (i.e., Organisation Ontologies);

To provide a supervised view of Unsupervised Conceptualisation extracted with methodology for Knowledge Extraction;

To keep up to date classification of new incoming resources with respect to OO;

To find similar resources, such as: Similar tasks in PWLE To support link discovery on heterogeneous resources using

morphisms enabled by OO People relevant for a Project according to required competences

and the available ones; Document, Project or Tasks in PWLE ...

Ontology and Instance Matching:Application

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Input: Source: Unsupervised conceptualisation, Target: Organisation

Ontologies

Output: Updated Organisation Ontologies;

Feature and Approach: Semi-automatic approach; FFCA based approach inspired to FCA Merge; Exploits results from Ontology and Instance Matching features

Ontology Merging

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Ontology Merging: Workflow & Technologies

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Formal context creationunsupervised providedby KE (vectorisation)

Formal context creation OO resourcesrepresented through ARISTOTELE models instances connected to the OO


Ontology Merging: Workflow & Technologies

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Exploiting Ontology Matching betweenUnsupervised Conceptualisation and OO


Close the loop aimed to prepare and maintain OO Allowing to keep up to date OO; Suggesting when and which concepts of Unsupervised

Conceptualisation could be merged with concepts of Organisation Ontologies;

Ontology Merging: Application

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Other Available results The ARISTOTELE High Level Architecture Already Developed tools


Outlines

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ARISTOTELE Architecture Logical view


Semantic Layer high level architecure

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Available tools

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Available tools

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Other Available results The ARISTOTELE High Level Architecture Already Developed tools


Outlines

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Main features: Definition of an ontology-based KM platform that integrates and

orchestrates several methodologies in order to enable knowledge base preparation, maintenance and update;

The knowledge extraction is strongly devoted to exploit and make up a specialised Wikipedia knowledge base;

Future work: to finalise the implementation of intelligent services on the

platform to support several enterprise processes, such as: expert finding, human resource management and experiment in real working environment


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Web site: http://www-aristotele-eu.org twitter: (@Aristotele_ip) URL:

http://twitter.com/#!/Aristotele_ip LinkedIn: ID Group - ARISTOTELE Facebook: ARISTOTELE Project

ARISTOTELE references

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Thank you very muchfor your attention and

stay tuned!

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