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M1. sem web & ontology introd

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M1. sem web & ontology introd

  1. 1. Collaborative OntologyEngineering and ManagementMay 20-24, 2013The Sheraton San Diego Hotel &MarinaSan Diego, California, USAThe 2013 International Conference onCollaboration Technologies and Systems(CTS 2013)Michele MissikoffPolytechnic University of Marche and LEKS-CNR, Italy
  2. 2. Content• What is an ontology? Why we need them?• The Semantic Web and Social SemanticNetworks• On the nature of (computational) knowledge• Conceptual modeling: principles• From perception to representation• Ontology Engineering• The social dimension of Ontology BuildingAnd ...• Some practical exercises2CTS 2013, San Diego
  3. 3. The SpeakerMichele Missikoff• Scientific Advisor at Univ Polytechnic ofMarche (Ancona), for the European BIVEE1Project• Coordinator of Lab for Enterprise Knowledgeand Systems, Italian National Research Council• European Task Force Leader of FInES - FutureInternet Enterprise Systems ResearchRoadmap• Professor of Enterprise Information Systems atInternational University of Rome3CTS 2013, San Diego(1Business Innovation in Virtual Enteprise Environments)
  4. 4. 4What is an ontology? What is theSemantic Web? Why we needthem?Ontology Introduction
  5. 5. Ontology: Origins and HistoryOntology Introduction 5• In Philosophy,fundamental branch ofmetaphysics– Studies “being” or“existence” and their basiccategories– Aims to find out whatentities and types ofentities exist– Identifies and characterisestheir properties(Credits: I. Horrocks)
  6. 6. Ontology Introduction 6What is a Computational Ontology?From Philosophy to practical use of an Ontology– It is about what exists, and is relevant for ourpurposes, in our domain of interest;– Needs the consensus of a group which isrepresentative of the community of interest– Aims at reaching a shared view of the domain ofinterest– Allows for reduction or elimination ofterminological and conceptual confusionAn ontology is an evolving repository of relevantconcepts, continuously incorporating new meaningsfrom the interaction with the environment
  7. 7. Ontology Introduction 7An Ontology is …“… a theory about the nature of beings” (Philosophical view)“… a formal, explicit specification of a sharedconceptualisation.” (AI view – T.R. Gruber)*– ‘Formal refers to the fact that the ontology should bemachine understandable.– Explicit means that the type of concepts used and theconstraints on their use are explicitly and fully defined.– Shared reflects the notion that ontology capturesconsensual knowledge, that is, it is not restricted to someindividual, but accepted by a group / community.– A conceptualisation refers to an abstract model of somephenomena in the world, it identifies the relevant conceptsrelated to that phenomena.• In formal terms: Ont = (Conc, Rel, Axioms, Inst)
  8. 8. Ontology Introduction 8An Ontology is … (con’d)"An ontology defines the common terms andconcepts (meaning) used to describe andrepresent an area of knowledge. An ontology canrange in expressivity from a Taxonomy(knowledge with minimal hierarchy or aparent/child structure), to a Thesaurus (wordsand synonyms), to a Conceptual Model (withmore complex knowledge), to a Logical Theory(with very rich, complex, consistent andmeaningful knowledge)." [www.omg.org]
  9. 9. Ontology Introduction 9Conceptual models and ontologiesThey 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 diagrammaticforms)• Typically represented with a formal language (e.g., RDF(S),OWL, CG, F-Logic, ...)• Used to represent an application domain, not a specific system
  10. 10. Ontology Introduction 10Ontologies as Social Artefacts• An Ontology is a socio-cultural phenomenon, but wewant to describe the concepts in a formal andunambiguous way, processable by a computerAn ontology contains:– a set of concepts (e.g., entities, attributes, processes) seen asrelevant 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 currentneeds and usage of both human users and computers– be supported by an Ontology Management System
  11. 11. Ontology Introduction 11Why Ontologies?
  12. 12. Ontology Introduction 12First MotivationsWhen starting a cooperation (to work togetheror in interacting in social settings), peopleand organizations may have different:– viewpoints– assumptions– needsabout the same domain, due to differentcontexts, goals, backgrounds and cultures
  13. 13. 13motivations (cont’d)Furthermore, the frequent use of different:– jargon– terminologysometimes diverging or overlapping, generate confusion.Even worse,– conceptsmay be mismatched or ill-defined (e.g., delivery_date).Goalallow people, organizations, computer applications, smartobjects to effectively cooperate, despite the mentioneddifferences• All computers today can communicate, but it does not implythat they cooperate (due to different services & dataorganization)• People and organizations do communicate and cooperate, butwith low automatic support (and several misunderstandings)
  14. 14. Ontology Introduction 14Cooperation ProblemsThe lack of a shared understanding leads to a poorcommunication that impacts on:– effectiveness of people’s cooperation– flaws in enterprise operations– even social fragmentation (… tension)When Information Systems Engineering is involved, furtherproblems arise on:– the identification of the requirements for the systemspecification– potential reuse and sharing of system components– interoperability among systemsThen … ONTOLOGIES
  15. 15. Ontology Introduction 15Benefits of Reference Ontologies• Business Opportunity analysis• Partnering• Interoperability• Semantic Knowledge Management• Business / IT Alignment• Social / Shared visionBy means of• A collaboration practice for a shared contextunderstanding• Ontology management – Building an EO• Semantic Annotation• Interoperability among legacy systems• Sem Search: exact / approximate• Similarity reasoning
  16. 16. Ontology Introduction 16From Terminlogy to OntologyA First Glimpse on OntologyEngineering
  17. 17. Ontology Introduction 17Progression of DomainspecificationLexicon - Set of terms (also multi-word) representingrelevant entities and relationships in the domainGlossary - Alphabetically ordered terms, with theirdescriptions, in natural language. Firstcategorizations according to an OntologyFramework (e.g., OPAL)Taxonomy - hierarchy of terms according to arefinement relation (e.g., ISA)Thesaurus - First introduction of elationships, such as:synonyms, antonyms; BT, NT, RTSemantic Net - Full fledged deployment of Conceptsand Relations: Gen/Spec, part of/HasPart, Sim,InstOf, … + domRel
  18. 18. Ontology Introduction 18From Terminology to OntologyOntologyLexiconSemanticNetTaxonomy /ThesaurusGlossary
  19. 19. The Societal Dimension ofOntologiesOntology Introduction 19
  20. 20. 20The Knowedge Society• European Council: Lisbon Strategy for growthand jobs“Europe needs will achieve the largest and mostcompetitive knowledge-based economy in the planet”• Investing in knowledge and innovation isintended to spur the EUs transition to aknowledge-based and creative economy.• The "fifth freedom" – the free movement ofknowledge – should thus be established• Knowledge is a value if embodied in models andpractices of the Society and Productionsystems (…New Economy).(europa.eu/legislation_summaries/employment_and_social_policy/eu2020/growth_and_jobs/c11806_en.htm)Ontology Introduction
  21. 21. World is Changing...... and we need new:• Systems of values• Development models• Social relationships to guaranteesustainability at:– Social, economic, environmental levelsOntology Introduction 21
  22. 22. 22The Advent of the Semantic WebThe collaborative, shared dimension ofKnowledge: The Semantic Web“The Semantic Web is an extension of thecurrent Web in which information is givenwell-defined meaning, better enablingcomputers and people [and Smart Objects] towork in cooperation.”(Tim Berners-Lee, James Hendler and Ora Lassila, TheSemantic Web, Scientific American, May 2001)Ontology Introduction
  23. 23. 23Traditional WebDR1DR2DR3NetworkDocumental Resources(DR): Data, music,pictures, …(HTML, MP3, jpeg, mpeg,…)Computer: managementwithout “understanding”Ontology Introduction
  24. 24. 24Semantic WebDR2DR3Network(HTML)Knowledge Network- RDF, OWL, Rules- Semantics (Ontologies)SR1 SR2Semantic Resources(SR): Concepts, semanticnets, ontologies, …DR1KR = SR + DR
  25. 25. 25Two kind of resourcesDocumental Resources (DR): Human-orientedinformation and knowledgeFactual K, such as: the Rome Sheraton Hotel has250 rooms, the prices are…Intensional K: An Hotel is composed by: areception, some rooms, etc…Procedural K: To make a reservation, prepare firstthe 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. 26. 26Human-readable vs Computer-readableAccording to Tim Berners-Lee:“Today’s web pages are conceived to be human-readable (in terms of content), we need to findsolutions 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, torepresent computer-oriented knowledgeOntology Introduction
  27. 27. What’s computer ‘readability’?Ontology Introduction 27What We Say to Dogs"Stay out of the garbage!Understand, Ginger? Stay outof the garbage!"What Dogs understand"... blah blah blah blah GINGERblah blah blah blah ..."
  28. 28. 28Functions of the Traditional Web• Keyword-basedInformation Retrieval• Hypertext Navigation• Manual Classification• Specialised search robots(Softbots, crawlers, ..)!?Retrieval quality(precision & recall)inversely proportionalto data quantityOntology Introduction
  29. 29. 29Functions of the the Semantic Web• Semantic InformationRetrieval• Machine Reasoning• Machine-machineadvanced cooperation• SharedConceptualisations(shared ontologies)with std knowledgerepresentationRetrieval quality directlyproportional to knowledgequantity(and reasoning capabilities)Ontology Introduction
  30. 30. 30Semantic Web vision(http://www.w3.org/2007/Talks/0130-sb-W3CTechSemWeb/)Ontology Introduction
  31. 31. But... What is Knowledge?31
  32. 32. 32The 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 toalgebra/logics)• Level of abstraction (from factual to conceptual tometaCon)• Synchronic vs Diachronic (Structural vsBehavioural)
  33. 33. 33Representing Knowledge? For whom, for whatIt 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. 34. 34Level of declarativenessAccording to the OMG-MDA vision:• Descriptive (Computational IndependentModel)– Ex. Class Diagram, abstract Business Processmodel (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, …
  35. 35. 35The three formalisation levels-Informal: typically textual documents (free orloosely structured text)-Semiformal: diagrams, tables, forms (rigorousstructure/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. 36. 36The Knowledge Tiers- Factual knowledge: ground information,representing individuals (DB technology)- Conceptual knowledge: representing abstractentities and operations (Enterprise models andIS design blueprints)- Methodological knowledge: representinglanguages and guidelines for KB construction(knowledge engineering languages methods,metamodels, modeling ideas)
  37. 37. 37RWO(doc, people,…)EntityActor BusinessObjectBusinessProcessISApersonemployeeISA PurchaseOrderProcurementLuigi BianchiMario RossiPO#21purchasingXIntensional Level(conceptual Model)Extensional Level(factual model)MetaLevel(modelingMetaconcepts)...ActivityActionpurchasingY...IDEAinstantiationinstantiationThree Abstraction Levels
  38. 38. 38The Ontology “Chestnut”UpperDomain OntologyApplicationOntologyLower Domain OntologySpecializationAggregationThe hierarchical organization of an Ontology
  39. 39. Collaborative Dimension inOntology EngineeringOntology Introduction 39
  40. 40. Social Ontology Buildingand Evolution (SOBE)SOBE supports the building of sharedontologies through:• Automatic knowledge extraction– Analysis of textual documents by using NLPtechniques• Social participation– Voting and discussing (forum) for validatingand enriching extracted knowledgeOntology Introduction 40
  41. 41. SOBE Methodology• Step-wise approach through five incrementalsteps (Milestones)– Lexicon (M1): plain list of terms– Glossary (M2): terms + natural language definition– Concept Categorization (M3): in accordance withthe OPAL (e.g., Object, Process, Actor)– Taxonomy (M4): definition of ISA hierarchy– Ontology enrichment (M5): additionalrelationships (e.g., predication, relatedness)Ontology Introduction 41
  42. 42. SOBE ‘snake’Ontology Introduction 42GLOSSARYLEXICONTAXONOMY /ONTOLOGYEnterpriseDocsTermsExtractorE-LexiconTermsValidatorN-LexiconPre-LexiconM1GlossValidatorN-Glossary M2ConceptCategorizationenvironmentM3N-OntologyOntologyEnrich.InitialOntologyM5TaxonomyExtractorEm-TaxonomiesTaxonomyProposer &ValidatorPre-TaxonomyM4N-TaxonomyE-GlossaryGlossExtractorGoogle defineWordnet ...42/20
  43. 43. Collaborative DimensionDriven by Web 2.0 and social communitiesphilosophy• Voting: accept/discard results of theautomatic extraction (lexicon and glossary)• Proposing: new terms and definitions to bevalidated by participants• Discussing: for reaching an agreement onglossary definitions (dedicated forums)Ontology Introduction 43
  44. 44. 44Conclusions• Semantic Web applications will involve humans (H),smart objects and devices (O), mainly improving:– O2O communication and cooperation, when devices interactto support human activities and goals achievments– H2O and H2H (tech-enhanced), with digital technology thatwill progressively disappear, allowing ‘natural’ interactions• Semantic Web needs Ontologies to interpret meanings of(digital) resources• Ontologies effectiveness depends on representationlanguages, reasoning, and collaborative consensusreachingOntology Introduction

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