1 / 40

CS566 – Semantic Web

CS566 – Semantic Web. Knowledge Management & Semantic Web. Παπαγγελής Μάνος , Κοφφινά Ιωάννα , Κοκκινίδης Γιώργος. Computer Science Department - UoC Heraklion 5 June , 2003. Overview. Introduction to Knowledge Management Knowledge Management Weaknesses

cboucher
Download Presentation

CS566 – Semantic Web

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. CS566 – Semantic Web Knowledge Management & Semantic Web Παπαγγελής Μάνος, Κοφφινά Ιωάννα, Κοκκινίδης Γιώργος Computer Science Department - UoC Heraklion 5 June, 2003

  2. Overview • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  3. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  4. What is Knowledge Management (KM) • There is no universal definition of KM • KM could be defined as the process through which organizations generate value from their intellectual and knowledge-based assets • KM is often facilitated by IT • Not all information is valuable • Two categories of knowledge • Explicit - Anything that can be documented, archived and codified, often with the help of IT • Tacit - The know-how contained in people's heads Knowledge Management & Semantic Web

  5. Technologies that support current KM Systems • Knowledge repositories • Expertise access tools • E-learning applications • Discussion and chat technologies • Synchronous interaction tools • Search and data mining tools. Knowledge Management & Semantic Web

  6. KM System Weaknesses • Searching Information • Word keywords don’t express the semantics • Extracting Information • Agents are not abletoextract knowledge from textual representations and tointegrate information spread over different sources • Maintaining • Sustaining weakly structured text sources is difficult and time-consuming • Such collections cannot be easily consistent, correct and up-to-date • Automating Document Generation • Adaptive Websites that enable dynamic reconfiguration based on user profiles require machine–accessible representation of the semi-structured data Knowledge Management & Semantic Web

  7. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  8. Ontology-based KM systems • Methodology for developing ontology-based KM systems • Ontologies can help formalize the knowledge shared by a group of people, in contexts where knowledge has to be modeled, structured and interlinked • Distinction between knowledge process and knowledge meta-process • Two orthogonal Processes with Feedback Loops • Knowledge Process • Knowledge Meta-process Knowledge Management & Semantic Web

  9. The Knowledge Process (1/4) • Knowledge Creation • Knowledge Import • Knowledge Capture • Knowledge Retrieval and Access • Knowledge Use Knowledge Management & Semantic Web

  10. The Knowledge Process (2/4) • Knowledge Creation • Computer-accessible knowledge moves between formal and informal • In order to have knowledge in the middle of the two extremes the idea is to embed the structure of knowledge items into document templates Knowledge Management & Semantic Web

  11. The Knowledge Process (3/4) • Knowledge Import • Importing knowledge into KM system has the same or more importance than creating it • For imported knowledge, accurate access to relevant items plays an even more important role than for homemade knowledge • Knowledge Capture • Knowledge capturing refers to the way that knowledge items, their essential contents and their interlinks are accessed (OntoAnnotate) Knowledge Management & Semantic Web

  12. The Knowledge Process (4/4) • Knowledge Retrieval and Access • Typically through a conventional GUI • Ontology can be used to derive further views of the knowledge (e.g. Navigation) and additional links and descriptions • Knowledge Use • It is not the knowledge itself that is of most interest, but the derivations made from it • No single knowledge item can be useful, but the overall picture derived the total analysis Knowledge Management & Semantic Web

  13. The Knowledge Meta-Process (1/3) • Feasibility Study • Kickoff phase • Refinement Phase • Evaluation Phase • Maintenance Phase Knowledge Management & Semantic Web

  14. The Knowledge Meta-Process (2/3) • Feasibility Study • Identification of problems and opportunity areas • Selection of the most promising focus area and target solution • Kick off phase • Requirement specification • Analysis of input sources • Development of baseline taxonomy Knowledge Management & Semantic Web

  15. The Knowledge Meta-Process (3/3) • Refinement phase • Concept Elicitation with domain experts • Development of baseline taxonomy • Conceptualization and Formalization • Evaluation Phase • Revision and Expansion based on feedback • Analysis of usage patterns • Analysis of competency questions • Maintenance Phase • Management of organizational maintenance process Knowledge Management & Semantic Web

  16. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  17. A Framework for KM on the SW • Knowledge Capturing • Knowledge Repository • Knowledge Processing • Knowledge Sharing • Using of Knowledge Knowledge Management & Semantic Web

  18. Knowledge Capturing • Knowledge can be collected from various sources and in different formats • Four Types of Knowledge Sources • Expert knowledge • Legacy Systems • Metadata Repositories • Documents • Need for Knowledge Capturing Tools Knowledge Management & Semantic Web

  19. Knowledge Repository • Use of Relational Databases • Efficient storing • Efficient Access to RDF metadata • It is an RDF Repository like RDFSuite or RDF Gateway Knowledge Management & Semantic Web

  20. Knowledge Process • Efficient manipulation of the stored knowledge • Graph-based processing for knowledge represented in the form of rules • E.g Deriving a dependency graph Knowledge Management & Semantic Web

  21. Knowledge Sharing • Knowledge Integration of different sources (Knowledge Base) and its utilization • Realized by searching for rules that satisfy the query conditions • Searching is realized as an inferencing process • Ground assertions (RDF triples) and domain axioms are used for deriving new assertions Knowledge Management & Semantic Web

  22. Using of Knowledge • Finding appropriate documents is essential, but the derivation made of them adds value to KM applications • Composition of documents • Use of conditional statements • Conditional Statements leads to efficient searching for knowledge • Precondition-Action Knowledge Management & Semantic Web

  23. Proposed KM Framework Knowledge Management & Semantic Web

  24. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  25. Knowledge Representation • Knowledge should be expressed by explicit semantics in order to be understood by automated tools • Select schemas and express knowledge through them • Knowledge sharing,merging and retrieval are possible if the categories used in the knowledge representation are connected by semantic links, expressed in ontologies Knowledge Management & Semantic Web

  26. Elements of Knowledge Representation • Ontologies and Knowledge Bases • Ontologies are catalogues of categories with their associated complete or partial formal definitions of necessary and sufficient conditions • A knowledge base is composed of one ontology (or several interconnected ontologies) plus additional statements using these ontologies • Ontology Servers • Permit Web users to modify the ontology part of the KB • Knowledge within Web Documents • Permit the insertion of knowledge inside HTML documents Knowledge Management & Semantic Web

  27. Challenges of Semantic Web • Scale of information • The information found on the Web is orders of magnitude larger than any traditional single knowledge-base • Change rate • Information is updated frequently • Lack of referential integrity • Links may be broken and information may not be found • Distributed authority • Trust of knowledge is not standard because data are obtained through different users • Variable quality of knowledge • Knowledge may differ in quality and should not be treated the same Knowledge Management & Semantic Web

  28. Challenges of Semantic Web (cont.) • Unpredictable use of knowledge • Knowledge base should be task-independent • Multiple knowledge sources • Knowledge is not provided by a single source • Diversity of content • The focus of interest is wider • Linking, not copying • The size of information forbid the copy of data • Robust inferencing • The degrees of incompleteness and unsoundness must be functions of the available resources • Answers could be approximate Knowledge Management & Semantic Web

  29. Ontology • Processing and sharing of knowledge between programs in the Web • Definitions • Representation of a shared conceptualization of a particular domain • A consensual and formal specification of a vocabulary used to describe a specific domain • A set of axioms designed to account for the intended meaning of a vocabulary • An ontology provides • A vocabulary for representing and communicating knowledge about some topic • A set of relationships that hold among the terms in that vocabulary Knowledge Management & Semantic Web

  30. Ontology Driven KR • Knowledge sharing and reuse • Enable machine-based communication • Reusable descriptions between different services • No more keyword-based approach… • …but syntactic- and semantic-based discovery of knowledge • Hierarchicaldescription of important concepts and definition of their properties (attribute-value mechanism) Knowledge Management & Semantic Web

  31. Languages for KR • XML • RDF / RDF Schema • DAML+OIL • OWL Knowledge Management & Semantic Web

  32. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  33. On-To-Knowledge • On-To-Knowledge was a European project that built an ontology-based tool environment to speed up knowledge management • Results aimed were • Toolset for semantic information processing and user access • OIL, an ontology-based inference layer on top of the Web • Associated Methodology • Validation by industrial case studies Knowledge Management & Semantic Web

  34. On-To-Knowledge Architecture Knowledge Management & Semantic Web

  35. On-To-Knowledge Technical Architecture Knowledge Management & Semantic Web

  36. Tools Used • RDFferret • Combines full text searching with RDF quering • OntoShare • Storage of the information in an ontology and querying, browsing and searching that ontology • Spectacle • Organizes the presentation (ontology-driven) of information and offers an exploration context • OntoEdit • Inspect, browse, codify and modify ontologies Knowledge Management & Semantic Web

  37. Tools Used (cont.) • Ontology Middleware Module (OMM) • Deals with ontology versioning, security (user profiles and groups), meta-information and ontology lookup and access via a number of protocols (Http, RMI, EJB, CORBA and SOAP) • LINRO • Offers reasoning tasks for description logics, including realization and retrieval • Sesame • Persistent storage of RDF data and schema information and online querying of that information Knowledge Management & Semantic Web

  38. Tools Used (cont.) • CORPORUM toolset • OntoExtract and OntoWrapper • Information Extraction and ontology generation • Interpretation of natural language texts is done automatically • Extraction of specific information from free text based on business rules defined by the user • Extracted information is represented in RDF(S)/DAML+OIL and is submitted to the Sesame Data Repository Knowledge Management & Semantic Web

  39. Contents • Introduction to Knowledge Management • Knowledge Management Weaknesses • Knowledge Management for Semantic Web • Ontology-based KM systems • A Framework for KM on the Semantic Web • Knowledge Representation • Knowledge Management System Example • Conclusion Remarks Knowledge Management & Semantic Web

  40. Conclusion Remarks • Current Knowledge Management technologies need to be revised • There are some architectures of Knowledge Management Systems for Semantic Web but there are only few KM applications available • Knowledge Representation has to meet the challenges that Semantic Web poses • On-to-knowledge proposes a fine architecture on which KM systems for SW can be based Knowledge Management & Semantic Web

More Related