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Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group KALIF Sharing Day on Ontologies Ontology.

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Presentation on theme: "Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group KALIF Sharing Day on Ontologies Ontology."— Presentation transcript:

1 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group KALIF Sharing Day on Ontologies Ontology Work in the DFKI Knowledge Management Group Andreas Abecker, Ansgar Bernardi, Joachim Hackstein, Ludger van Elst, Markus Junker, Otto Kühn, Tino Sarodnik, Michael Sintek, Jamel Zakraoui

2 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Questions from the Agenda – K1: Objectives of the project – K2: How are ontologies used in the project? – K3: What requirements implied for ontologies and specification language? – K4: Is XML an option for using the ontologies? – K5: What requirements does this use imply for ontology development tools? – K6: Are there tools you developed or use and which tools are this? (Metselaar)

3 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K1: KnowNet’s Objectives To develop and exploit an innovative holistic paradigm for KM by: – Developing, testing and evaluating an ICT-based KM tool –Which aims to integrate two approaches: the “process-centered” view the “product-centered” view – Developing, testing and evaluating a set of methods –Which will enable the creation, retention, sharing and leveraging of knowledge assets – Applying and assessing the use of KNOWNET in the three user partners: –PLANET: a Greek management consultancy company –Gooch Webster: a UK chartered surveyors company –UBS, AG: the Credit Risk Valuation Department of UBS AG (KNowNet Consortium)

4 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Analyse KnowNet Tool Development WPs 2, 3, 4, 5 Application WP 11 Method Stage I: Strategic Planning Development WP 6 Application WP 10 Method Stage II: Develop K.Organisation Development WP 7 Application WP 11 Knowledge Assets Measurement System Development WP 8 Application WP 11 KnowNet Framework Development WP 1 Application WP 9 (KNowNet Consortium) K1: Some KnowNet Project WPs & Results

5 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group – Proposition: knowledge can be represented as a thing that can be located and manipulated as an independent object it is possible to capture, distribute, measure and manage knowledge n Proposition: it is only feasible to promote, motivate, encourage, nurture or guide the process of knowing l the idea of trying to capture and distribute knowledge seems senseless The “Product” View The “Process” View – Focus: on products and artifacts containing and representing knowledge usually, this means managing documents, their creation, storage, and reuse in computer-based corporate memories. n Focus: on KM as a social communication process l which can be improved by various aspects and tools of collaboration and cooperation support. (Kühn & Abecker / KnowNet Consortium) Product View and Process View

6 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K2: Use of Ontologies: The Know-Net Tool KNOWLEDGER as the main instrument for collaboration and knowledge capture KNOWLEDGER as one of several sources to be accessed via multi- criteria knowledge maps & meta data declarative knowledge item descriptions abstract from concrete storage details and integrate different sources declarative ontology for knowledge organization ensures system flexibility (KNowNet Consortium)

7 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K2: Logic-Based Information Retrieval – [vanRijsbergen89] identified the three basic constituents of intelligent information retrieval: –a semantic representation of documents –a semantic representation of queries –an inference procedure mapping the latter one onto the former ones – logic-based formalisms have a clear semantics and powerful processing mechanisms which can incorporate background knowledge – possible benefits of ontologies: –use inheritance (and other relations? Including text-based features) for query relaxation, expansion, or rephrasing –use background knowledge and complex representations for sophisticated relevance assessment and similarity assessements between, e.g., contexts –agreed-upon indexing structures foster communication and stimulate discussion about categories –hope: reusable indexing structures (Abecker et al.)

8 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Knowledge Object Store Knowledge Asset / Object Directory Navigator Ontology / Classification Models Km applications K2: The Know-Net Advanced Search Interface Documents from arbitrary sources are described / searched for referring to metadata records containing information about: –standard information (author, date etc.) –creation context: respective Knowledger database –document content: arbitrary domain models for multi-criteria categorization –text features (KnowNet Consortium / Abecker et al.)

9 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K2: Ontology Use in Know-Net: Steps to be Performed – Build metadata schema and indexing ontologies –Ontology editor plus method – Index / categorize documents –embedded in document input interface – Search / retrieve using metadata and ontologies –provided by the KASI (KnowNet advanced search interface)

10 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: Which tools? The KASI Ontology Editor – Basic support for graphically editing metadata schema and index categories / indexing ontologies. – Retrieval interface dynamically built from actual ontology. – Some basic import mechanisms to integrate with Lotus Notes. – Propagates changes of indexing vocabulary into the metadata store / documents. – Method, implementation at user sites, and usage experience still in its early stage. – Integration with Know-Net method more important than technical support. – Ontology construction still weak in theory, even more in practice. (Zakraoui et al.)

11 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: Which Tools do you use? - The Know-Net Ontology Editor (Zakraoui et al.)

12 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K2: Document Indexing (Hackstein et al.)

13 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K2: The KASI Query Interface – Browse through metadata space. – „Fast-find“ gimmick: text search in attribute and value space. – Select relevant document description attributes. – Attribute value range is offered in an appropriate manner: lists, input fields, graphical representations, folder structure. – Positive and negative search conditions can be specified. – Lotus fulltext search integrated. – All GUI interactions result in an immediate incremental update of the answer set. – Direct access to document via URL possible. (DFKI KM Group)

14 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K2: Document Retrieval (DFKI KM Group)

15 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: Requirements for Representation and Development – Distinguish between document models and indexing ontologies –for document models additional requirements like order of chapters, concepts as attribute fillers, specific links in hypertexts, „higher-order“ expressiveness for complex content descriptions – For indexing ontologies: some basic object-centered modeling formalism: concepts, attributes, instances – Some link to thesaurus information – Possibilities to freely define other relationships equipped with special inference mechanisms (domain and task specific) – Maybe later: special procedures for vague relationships: „has-to-do-with“ – Maybe later: a clearer understanding of indexes or the „about“ relation?? – Maybe later: uncertainty and vagueness?? (Sintek & Abecker)

16 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: Specification Language: OCRA – OCRA integrates the object-oriented and the relational paradigms by unifying – classesfrom OOP – relationsfrom logic programming languages and RDBs – typesfrom functional and imperative programming languages – Designed for the Intelligent Fault Recording project – Designed for efficient processing on an RDBMS – Allows special inferences for specific link types – OCRA integrates the object-oriented and the relational paradigms by unifying – classesfrom OOP – relationsfrom logic programming languages and RDBs – typesfrom functional and imperative programming languages – Designed for the Intelligent Fault Recording project – Designed for efficient processing on an RDBMS – Allows special inferences for specific link types (Sintek)

17 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: OCRA: The KnowMore Representation Formalism is an Object-Oriented and Relational Language The KnowMore representation formalism integrates the object-oriented and the relational paradigms by unifying classesfrom OOP relationsfrom logic programming languages and RDBs typesfrom functional and imperative programming languages The KnowMore representation formalism integrates the object-oriented and the relational paradigms by unifying classesfrom OOP relationsfrom logic programming languages and RDBs typesfrom functional and imperative programming languages classes, inheritance, objects, methodsinherited from OOP set orientationfrom RDBs rulesfrom logic languages classes, inheritance, objects, methodsinherited from OOP set orientationfrom RDBs rulesfrom logic languages (Sintek)

18 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: The OCRA is Strictly Typed Class declarations: human(name : string, // string and number are age : number, // built-in classes father : human, mother : human ) man : human() woman : human(maidenName : string) Alternative definition of human with set type: human(name : string, age : number, children : {human} ) Class declarations: human(name : string, // string and number are age : number, // built-in classes father : human, mother : human ) man : human() woman : human(maidenName : string) Alternative definition of human with set type: human(name : string, age : number, children : {human} ) (Sintek)

19 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: OCRA: Annotations Allow Complex Semantic Nets to be Modeled human( name : string,..., competences : {competence} / strength ) competence(name : string,...) ann() // the top class of all annotations strength : ann(value : string) // e.g. "good", "medium", "bad" human( name : string,..., competences : {competence} / strength ) competence(name : string,...) ann() // the top class of all annotations strength : ann(value : string) // e.g. "good", "medium", "bad" human name competences strength name competence name JohnEnglish FrenchMary bad good medium classes objects (Sintek)

20 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: OCRA: Objects Have a Textual Representation woman( name = "Mary", competences = {competence(name = "French") / strength(value = "medium")} ) User-defined object identifiers: man( name = "John", competences = {english / good, french / bad} ) english : competence(name = "English") french : competence(name = "French") good : strength(value = "good") bad : strength(value = "bad") Note: predicate symbols and function symbols are not distinguished  they are both class names. woman( name = "Mary", competences = {competence(name = "French") / strength(value = "medium")} ) User-defined object identifiers: man( name = "John", competences = {english / good, french / bad} ) english : competence(name = "English") french : competence(name = "French") good : strength(value = "good") bad : strength(value = "bad") Note: predicate symbols and function symbols are not distinguished  they are both class names. (Sintek)

21 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Origin of OCRA: The Intelligent Fault Recording System Realizes a Model-Based Recording Approach for Machine Diagnosis Experiences fault modelaction modeldiagnosis process The models can be extended at any time. – guarantee unambiguous expressions – describe similarities and dependencies – enforce and support structuring machine model fault and maintenance activity fault and maintenance activity (Bernardi et al.)

22 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Intelligent Fault Recording: The Machine Model Describes Structure and Components of the Equipment – structural overview – unambiguous names of the components – similarities and interconnections between machines – technical details MACHINE MODEL

23 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Intelligent Fault Recording: Each Step is Documented in a Structured Way role of the step during diagnosis affected component fault / action description supplementary text, if needed The reference to the models realizes a weak formalization. machine model diagnosis process fault/action model

24 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Intelligent Fault Recording: Flexible Queries Enable Specific Information Access – symptoms “Error: Missing Parameters” – affected components “all faults at the SL-500” – states “open event sequences” – time interval “during the last 24 hours” – arbitrary text Search for (Bernardi et al.)

25 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Ontology Use: The Machine Model Facilitates Query Expansion search for faults in the 1kV station in the gallery machine model:CCM computer is part of the 1kV station result: faults concerning the CCM computer are retrieved, too Similarities between components and functional dependencies are handled analogously. (Bernardi et al.)

26 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K4: Is XML an Option? – Yes :-)XML plus RDF-Schema (Sintek)

27 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K3: Some Critical Reflections – Idea difficult to understand for „normal users“ – In the first instance, the expressiveness wished by the users is somewhat „between lists and trees“ :-( – Who really exploits background knowledge and complex inferences? – Who builds ontologies? How? (future work in text-based construction support planned) – Indexing takes time: automatic text categorization (current work at DFKI) – More theoretically: are index structures really ontologies? (Welty, van der Vet, Schmiedel made the point already) – Also: are ontologies what we need for retrieval and information access (think about „navigation ontologies“ and individual information spaces as projections from the underlying ontologies) (Abecker)

28 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K5: Requirements for Ontology Development Tools – Method is also critical in practice! –Case studies in an upcoming IST project (DECOR) –Interleave with Business Process Modeling –Build an „Enterprise Information Ontology“ – What about maintenance? – Our approach: „knowledge acquisition from texts“ –First ideas developed in the FAKT and KnowMore projects –Shall be further developed in Frodo (DFKI KM Group)

29 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: Ontology Development: An Approach for Acquisition+Maintenance was Developed Based on Automatic Thesaurus Generation – documents –routinely created during work processes –contain relevant terms in task contexts – automatic thesaurus generation –efficient processing of large document corpora –extract important terms and relations based on frequency and co-occurrence – interactive knowledge acquisition –perform a semantic classification of identified similarity relations –update knowledge base and ontology thesaurus documents similarity thesaurus thesaurus generator TRex ontology thesaurus + knowledge base interactive knowledge acquisition and update tool (Kühn)

30 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group Documents Are a Plentiful Source of Information Available in any Application Domain that can be Automatically Processed EXPLOITING THESAURUS GENERATION FOR KNOWLEDGE ACQUISITION Example from FAKT: similar terms to ‘backup’ tape mount device not ready restore data safety Example from FAKT: similar terms to ‘backup’ tape mount device not ready restore data safety Example from ‘Die WELT’ Articles on the German spelling reform similar terms to ‘Rechtschreibung’ Reform Kultusministerkonferenz Duden Regeln (112 Regeln) Orthographie Example from ‘Die WELT’ Articles on the German spelling reform similar terms to ‘Rechtschreibung’ Reform Kultusministerkonferenz Duden Regeln (112 Regeln) Orthographie – In an Organizational Memory it is important to handle large amounts of knowledge – First results confirm our expectations that thesaurus generation methods may be profitably exploited for knowledge acquisition – Even a rough analysis of word frequencies and correlations......identifies core topics in a new domain...offers guidance for subsequent knowledge acquisition – An analysis of term similarities points out interesting relationships and dependencies – More sophisticated analyses based on additional knowledge are needed to separate meaningful from spurious results (Kühn)

31 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group The Thesaurus Generation Tool TRex was Extended and Enhanced documents - schemata - stopwords - non-text important terms (from ontology) generation parameters document parsing term and phrase generation construction of term-context-matrix similarity thesaurus computation of term-similarities analysis of term-context-matrix – TRex can be easily adapted to domain- specific document collections –specification of document schemata, stopwords and non-text –adjustment of term and phrase generation parameters – TRex offers a variety of techniques for computing term-similarities –different term contexts (document, window) –various weighting schemes –singular value decomposition of term- context matrix –numerous similarity scores – TRex can exploit lists of important terms (extracted from an ontology) –for focussing term and phrase generation –for weighting of term similarities term-context matrix to be updated (Kühn)

32 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: An Editor Realizes the Thesaurus-Based Ontology Construction Method to Support the Cumbersome Task of Ontology Generation – An automatically created similarity thesaurus provides correlations between terms – Terms indicate possible concepts – Term correlations indicate possible links – The editor visualizes these relations – Identification of concepts and creation of links is done manually (Sintek, Sarodnik & Kühn)

33 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: The User is Responsible for the Selection of Concepts and Link Types (Sintek, Sarodnik & Kühn)

34 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: Ontology Usage: The Document Description Relates Information Items to Concepts From the Domain Ontology

35 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group The TCW Tool for Learning Text Classification has been Integrated to Automatically Create Meta Information for Text Documents in the OM – The Text Classification Workbench TCW is trained with manually categorized example documents – The system learns characteristic complex text patterns – After training, new documents can be categorized automatically – This can be applied to all text documents added to the OM – TCW originated from the READ and Virtual Office document analysis projects Automatic creation of more detailed formal descriptions relies on further information extraction results from Virtual Office (Junker & Sintek)

36 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group K6: Ontology Usage: Text Documents are Categorized Automatically Using TCW

37 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group An Overall Perspective: A Comprehensive Toolbox Supports all Modeling Tasks in an Organizational Memory Setting – Modeling of the business process using the ADONIS BPM tool – Creating the KIT descriptions –select from a library of info agents –specify relevant WF variables –specify search heuristics – The Ontology Editor employs thesaurus information to support the construction of ontologies – Arbitrary information items can be integrated and manually annotated – A learning text classification tool automatically creates meta information for text documents domain ontology enterprise ontology information ontology support information processing and retrieval process ADONIS © — Business Process Modeling Tool KIT Modeling Facilities Ontology Editor TRex — Similarity Thesaurus Generator TCW — Text Classification Workbench Information Item Editor (DFKI KM Group)

38 Deutsches Forschungszentrum für Künstliche Intelligenz GmbH Nov 30th, 1999 Knowledge Management Research Group The End


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