M1. sem web & ontology introd

Collaborative Ontology

Engineering and Management

May 20-24, 2013 The Sheraton San Diego Hotel &

Marina San Diego, California, USA

The 2013 International Conference on

Collaboration Technologies and Systems

(CTS 2013)

Michele Missikoff Polytechnic University of Marche and LEKS-CNR, Italy

Content

• What is an ontology? Why we need them?

• The Semantic Web and Social Semantic Networks

• On the nature of (computational) knowledge

• Conceptual modeling: principles

• From perception to representation

• Ontology Engineering

• The social dimension of Ontology Building

And ...

• Some practical exercises

2 CTS 2013, San Diego

The Speaker

Michele Missikoff

• Scientific Advisor at Univ Polytechnic of

Marche (Ancona), for the European BIVEE1

Project

• Coordinator of Lab for Enterprise Knowledge

and Systems, Italian National Research Council

• European Task Force Leader of FInES - Future

Internet Enterprise Systems Research

Roadmap

• Professor of Enterprise Information Systems at

International University of Rome

3 CTS 2013, San Diego

(1Business Innovation in Virtual Enteprise Environments)

4

What is an ontology? What is the

Semantic Web? Why we need

them?

Ontology Introduction

Ontology: Origins and History

Ontology Introduction 5

• In Philosophy,

fundamental branch of

metaphysics – Studies “being” or

“existence” and their basic

categories

– Aims to find out what

entities and types of

entities exist

– Identifies and characterises

their properties

(Credits: I. Horrocks)


What is a Computational Ontology?

From Philosophy to practical use of an Ontology

– It is about what exists, and is relevant for our purposes, in our domain of interest;

– Needs the consensus of a group which is representative of the community of interest

– Aims at reaching a shared view of the domain of interest

– Allows for reduction or elimination of terminological and conceptual confusion

An ontology is an evolving repository of relevant concepts, continuously incorporating new meanings from the interaction with the environment


An Ontology is …

“… a theory about the nature of beings” (Philosophical view)

“… a formal, explicit specification of a shared conceptualisation.” (AI view – T.R. Gruber)*

– ‘Formal' refers to the fact that the ontology should be machine understandable.

– 'Explicit' means that the type of concepts used and the constraints on their use are explicitly and fully defined.

– 'Shared' reflects the notion that ontology captures consensual knowledge, that is, it is not restricted to some individual, but accepted by a group / community.

– A 'conceptualisation' refers to an abstract model of some phenomena in the world, it identifies the relevant concepts related to that phenomena.

• In formal terms: Ont = (Conc, Rel, Axioms, Inst)


An Ontology is … (con’d)

"An ontology defines the common terms and concepts (meaning) used to describe and represent an area of knowledge. An ontology can range in expressivity from a Taxonomy (knowledge with minimal hierarchy or a parent/child structure), to a Thesaurus (words and synonyms), to a Conceptual Model (with more complex knowledge), to a Logical Theory (with very rich, complex, consistent and meaningful knowledge)." [www.omg.org]


Conceptual models and ontologies

They have common roots, but ...

Conceptual Model

• Traditionally conceived for inter-human communication

• Typically in diagrammatic form (e.g., UML, BPMN)

• Semi-formal representation

– Formal syntax, but intuitive semantics

• Used with a precise goal (e.g., IS engineering)

Computational Ontologies

• Conceived to be ‘fully’ processed by a computer

• Linear (textual) form (supports equivalent diagrammatic forms)

• Typically represented with a formal language (e.g., RDF(S), OWL, CG, F-Logic, ...)

• Used to represent an application domain, not a specific system


Ontologies as Social Artefacts

• An Ontology is a socio-cultural phenomenon, but we want to describe the concepts in a formal and unambiguous way, processable by a computer

An ontology contains: – a set of concepts (e.g., entities, attributes, processes) seen as

relevant in a given domain

– the definitions and inter-relationships among these concepts

– set of Axioms (e.g., constraints) and, in case, instances

• To be used by computers, ontologies must – have precise definitions, with a formal semantics (Tarski)

– evolve according to an evolving reality and adapt to current needs and usage of both human users and computers

– be supported by an Ontology Management System


Why Ontologies?


First Motivations

When starting a cooperation (to work together

or in interacting in social settings), people

and organizations may have different:

– viewpoints

– assumptions

– needs

about the same domain, due to different

contexts, goals, backgrounds and cultures

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motivations (cont’d)

Furthermore, the frequent use of different: – jargon – terminology

sometimes diverging or overlapping, generate confusion. Even worse,

– concepts

may be mismatched or ill-defined (e.g., delivery_date).

Goal

allow people, organizations, computer applications, smart objects to effectively cooperate, despite the mentioned

differences

• All computers today can communicate, but it does not imply that they cooperate (due to different services & data organization)

• People and organizations do communicate and cooperate, but with low automatic support (and several misunderstandings)


Cooperation Problems

The lack of a shared understanding leads to a poor communication that impacts on:

– effectiveness of people’s cooperation

– flaws in enterprise operations

– even social fragmentation (… tension)

When Information Systems Engineering is involved, further

problems arise on:

– the identification of the requirements for the system specification

– potential reuse and sharing of system components

– interoperability among systems

Then … ONTOLOGIES


Benefits of Reference Ontologies

• Business Opportunity analysis

• Partnering

• Interoperability

• Semantic Knowledge Management

• Business / IT Alignment

• Social / Shared vision

By means of

• A collaboration practice for a shared context understanding

• Ontology management – Building an EO

• Semantic Annotation

• Interoperability among legacy systems

• Sem Search: exact / approximate

• Similarity reasoning


From Terminlogy to Ontology

A First Glimpse on Ontology

Engineering


Progression of Domain specification

Lexicon - Set of terms (also multi-word) representing relevant entities and relationships in the domain

Glossary - Alphabetically ordered terms, with their descriptions, in natural language. First categorizations according to an Ontology Framework (e.g., OPAL)

Taxonomy - hierarchy of terms according to a refinement relation (e.g., ISA)

Thesaurus - First introduction of elationships, such as: synonyms, antonyms; BT, NT, RT

Semantic Net - Full fledged deployment of Concepts and Relations: Gen/Spec, part of/HasPart, Sim, InstOf, … + domRel


From Terminology to Ontology

Ontology

Lexicon Semantic

Net Taxonomy / Thesaurus

Glossary

The Societal Dimension of

Ontologies


20

The Knowedge Society • European Council: Lisbon Strategy for growth

and jobs “Europe needs will achieve the largest and most

competitive knowledge-based economy in the planet”

• Investing in knowledge and innovation is intended to spur the EU's transition to a knowledge-based and creative economy.

• The "fifth freedom" – the free movement of knowledge – should thus be established

• Knowledge is a value if embodied in models and practices of the Society and Production systems (…New Economy).

(europa.eu/legislation_summaries/employment_and_social_policy/eu2020/growth_and_jobs/c11806_en.htm)


World is Changing...

... and we need new:

• Systems of values

• Development models

• Social relationships to guarantee sustainability at:

– Social, economic, environmental levels


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The Advent of the Semantic Web

The collaborative, shared dimension of Knowledge: The Semantic Web

“The Semantic Web is an extension of the current Web in which information is given

well-defined meaning, better enabling computers and people [and Smart Objects] to

work in cooperation.” (Tim Berners-Lee, James Hendler and Ora Lassila, The

Semantic Web, Scientific American, May 2001)


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Traditional Web

DR1

DR2 DR3

Network

Documental Resources

(DR): Data, music,

pictures, …

(HTML, MP3, jpeg, mpeg,…) Computer: management without “understanding”


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Semantic Web

DR2 DR3

Network (HTML)

Knowledge Network - RDF, OWL, Rules - Semantics (Ontologies)

SR1 SR2

Semantic Resources

(SR): Concepts, semantic

nets, ontologies, …

DR1

KR = SR + DR

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Two kind of resources

Documental Resources (DR): Human-oriented information and knowledge

Factual K, such as: the Rome Sheraton Hotel has 250 rooms, the prices are…

Intensional K: An Hotel is composed by: a reception, some rooms, etc…

Procedural K: To make a reservation, prepare first the credit card, then enter the hotel Web site, …

Semantic Resources (SR): Knowledge to be

‘understood’ and processed by a computer.

x H, y: hotel(x) has(x,y) reception(y) … Ontology Introduction

26

Human-readable vs Computer-readable

According to Tim Berners-Lee:

“Today’s web pages are conceived to be human-readable (in terms of content), we need to find solutions to make them computer-readable.”

A technical intuition:

• HTML is the language of the Traditional Web, to represent human-oriented hypermedia docs

• RDF is the language of the Semantic Web, to represent computer-oriented knowledge


What’s computer ‘readability’?


What We Say to Dogs

"Stay out of the garbage!

Understand, Ginger? Stay out

of the garbage!"

What Dogs understand

"... blah blah blah blah GINGER

blah blah blah blah ..."

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Functions of the Traditional Web

• Keyword-based

Information Retrieval

• Hypertext Navigation

• Manual Classification

• Specialised search robots

(Softbots, crawlers, ..)

!

?

Retrieval quality

(precision & recall)

inversely proportional

to data quantity Ontology Introduction

29

Functions of the the Semantic Web

• Semantic Information

Retrieval

• Machine Reasoning

• Machine-machine

advanced cooperation

• Shared

Conceptualisations

(shared ontologies)

with std knowledge

representation

Retrieval quality directly

proportional to knowledge

quantity (and reasoning capabilities)


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Semantic Web vision

(http://www.w3.org/2007/Talks/0130-sb-W3CTechSemWeb/)


But... What is Knowledge?

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The dimensions of knowledge

• Level of explicitness (Nonaka, theory of Ba): Tacit, Implicit, Explicit

• Addressee (Human, Machine, both)

• Level of declarative (vs procedurale) approach

• Level of formalization (from NL text to algebra/logics)

• Level of abstraction (from factual to conceptual to metaCon)

• Synchronic vs Diachronic (Structural vs Behavioural)

33

Representing Knowledge? For whom, for what

It depends on the:

• Who is the Addressee

– for people (easy to read and manipulate)

– for machines (easy to process automatically)

– to exchange K between people and machines

• What Activity it supports (for people and/or computers)

– preliminary domain investigation and analysis

– decision support and recommender systems

– Data mining

– detail analysis, design and (sw) implementation

– Business transactions

– Knoweledge storage and retrieval

– Semantic query processing (with reasoning)

– Semantic interoperability

– Intelligent user interfaces

34

Level of declarativeness According to the OMG-MDA vision:

• Descriptive (Computational Independent Model)

– Ex. Class Diagram, abstract Business Process model (EPC, UML, …)

• Prescriptive (Platform IM)

– Workflow Management System (Savvion, TeamWare, OpenFlow, …), no transaction exec

• Operational (P Specific M)

– Process/action exec specification (e.g., BPEL, BPMN)

– Enterprise Information System, ERP, SCM, …

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The three formalisation levels

- Informal: typically textual documents (free or

loosely structured text)

- Semiformal: diagrams, tables, forms (rigorous

structure/syntax, intuitive semantics: UML,

EPC, Purchase order, invoice, etc.)

- Formal: rigorous specification languages

(rigorous syntax and semantics: RDF, OWL,

KIF, Z++, PSL/Pi Calculus, Ontolingua, etc.)

36

The Knowledge Tiers

- Factual knowledge: ground information,

representing individuals (DB technology)

- Conceptual knowledge: representing abstract

entities and operations (Enterprise models and

IS design blueprints)

- Methodological knowledge: representing

languages and guidelines for KB construction

(knowledge engineering languages methods,

metamodels, modeling ideas)

37

RWO (doc, people,…)

Entity

Actor Business Object

Business Process ISA

person

employee

ISA Purchase

Order

Procurement

Luigi Bianchi

Mario Rossi

PO#21

purchasingX

Intensional Level

( conceptual Model)

Extensional Level

(factual model)

MetaLevel

( modeling

Metaconcepts)

...

Activity Action

purchasingY

...

IDEA

instantiation

instantiation

Three Abstraction Levels

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The Ontology “Chestnut”

Upper

Domain Ontology

Application

Ontology

Lower Domain Ontology

Sp

ecializa

tion

Agg

reg

ati

on

The hierarchical organization of an Ontology

Collaborative Dimension in

Ontology Engineering


Social Ontology Building

and Evolution (SOBE)

SOBE supports the building of shared

ontologies through:

• Automatic knowledge extraction

– Analysis of textual documents by using NLP

techniques

• Social participation

– Voting and discussing (forum) for validating

and enriching extracted knowledge


SOBE Methodology

• Step-wise approach through five incremental

steps (Milestones)

– Lexicon (M1): plain list of terms

– Glossary (M2): terms + natural language definition

– Concept Categorization (M3): in accordance with

the OPAL (e.g., Object, Process, Actor)

– Taxonomy (M4): definition of ISA hierarchy

– Ontology enrichment (M5): additional

relationships (e.g., predication, relatedness)


SOBE ‘snake’


GLOSSARY

LEXICON

TAXONOMY /

ONTOLOGY

Enterprise Docs

Terms Extractor

E-Lexicon

Terms Validator

N-Lexicon

Pre-Lexicon

M1

Gloss Validator

N-Glossary M2

Concept

Categorization

environment

M3

N-Ontology

Ontology Enrich.

Initial Ontology

M5

Taxonomy Extractor

Em-Taxonomies

Taxonomy Proposer & Validator

Pre-Taxonomy

M4 N-Taxonomy

E-Glossary

Gloss Extractor

Google define

Wordnet ...

42/20

Collaborative Dimension

Driven by Web 2.0 and social communities

philosophy

• Voting: accept/discard results of the

automatic extraction (lexicon and glossary)

• Proposing: new terms and definitions to be

validated by participants

• Discussing: for reaching an agreement on

glossary definitions (dedicated forums)


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Conclusions

• Semantic Web applications will involve humans (H),

smart objects and devices (O), mainly improving:

– O2O communication and cooperation, when devices interact

to support human activities and goals achievments

– H2O and H2H (tech-enhanced), with digital technology that

will progressively disappear, allowing ‘natural’ interactions

• Semantic Web needs Ontologies to interpret meanings of

(digital) resources

• Ontologies effectiveness depends on representation

languages, reasoning, and collaborative consensus

reaching Ontology Introduction

M1. sem web & ontology introd

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