Knowledge Building with Semantic Schema in Enterprise: The ARISTOTELE Approach

Knowledge Building
with Semantic
Schema in Enterprise:
The ARISTOTELE
Approach
Pierluigi Ritrovato
Dep. Of Electronic Engineering
and Computer Engineering,
University of Salerno
CRMPA – Research Center in
Pure and Applied Mathematics

Keynote talk CISIS 2012
4-6 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
2

 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, learning
objectives, and creativity within the organization
General Information
3
Scientific
Coordinator
Project
Coordinator
Technical
Manager

ARISTOTELE Partners
ARISTOTELE
Academic
Industrial
Pilot
Research
4

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
5

• 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
6

• 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)
7

 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 blocks
and research areas (1/2)
8

 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/informal
collabroative learning
•Methodologies to access to learning offers
starting from requests in natural language
PWLE:
•Methodologies to capitalize knowledge
elicited through informal and informal
activities
Innovation factory:
Knowledge management:
Methodologies and algorithms enabling:
•Semi-automatic taxonomy construction
•Alignment and mapping of domain ontologies
•Semi-automatic ontology extraction
•Capture and formalisation of knowledge
exchange
Innovation factory:
Methodologies for DSS for Human
Resource Management
9

 Knowledge Model (KM)
 provides constructs for the representation
of enterprise knowledge entities,
enterprise domain vocabulary, educational
vocabulary
 Competence Model (CM)
of competences and their relations to
other concepts such as context, activities,
and objectives
 Worker Model (WM)
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
10

Role of the Models: Integrated Schemas
11

 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
12

How fit everything together
13

A Model example: The Knowledge Model
14

A model instance example
15

Personal Break: 

 Overall Picture
 The Architecture
Outlines
17

 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
18

ARISTOTELE Knowledge Building
19

Overview of Knowledge Building
20

 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
21

Knowledge Extraction: Example
 Taking as input
Technical Report of
an organisation the
methodology carries
out taxonomies
hierarchical
unsupervised
conceptualisation
22

Knowledge Extraction: Workflow &
Technologies
23

Hierarchical
Conceptualisation
IRsupport
Highcostto
updatingthe
structure
Supportfor
classificationof
newdata
Clustering X X
Hierarchical Clustering X X
Latent Semantic Analysis (LSA) X X X
Formal Concept Analysis (FCA) & Fuzzy
FCA
X X X X
Knowledge Extraction: Foundation (1)
• To face with time consuming execution of FFCA the
following 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)
24

Knowledge Extraction
Foundation (2)
threshold T=0.6
25

 To prepare Organisation Ontologies (OO) at
ARISTOTELE 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
26

 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
27

Ontology and Instance Matching:
Example (1)
28

Example (2)
Kinds of
Deliverable
State of
the Art
Methdologies
Project Y
Organization Ontologies
Model
related
Knowledge
Model
Worker Model
Knowledge
Model
Task
Project
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:Title
Task:Description Document:Title
Document:Topic
Document: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 Matching
Workflow/Technologies
30

 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
 ...
Application
31

 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
32

Ontology Merging: Workflow &
Technologies
33

Formal context creation
unsupervised provided
by KE (vectorisation)
Formal context creation OO resources
represented through ARISTOTELE
models instances connected to the OO

Ontology Merging: Workflow &
Technologies
34

Exploiting Ontology Matching between
Unsupervised 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
35

 Overall Picture
 The ARISTOTELE High Level Architecture
Outlines
36

ARISTOTELE Architecture Logical view

Semantic Layer high level architecure
38

Available tools
39

Available tools
40

 Overall Picture
 The ARISTOTELE High Level Architecture
Outlines
41

 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
Conclusion and Future Works
42

 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
43
Thank you very much
for your attention and
stay tuned!

Knowledge Building with Semantic Schema in Enterprise: The ARISTOTELE Approach

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