University of Crete HY566-Semantic Web CS566 – Semantic Web Computer Science Department - UoC Heraklion 5 June, 2003 Παπαγγελής Μάνος, Κοφφινά Ιωάννα, Κοκκινίδης Γιώργος Knowledge Management & Semantic Web

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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.

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 6 KM System Weaknesses  Searching Information Word keywords don’t express the semantics  Extracting Information Agents are not able to extract knowledge from textual representations and to integrate 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 9 The Knowledge Process (1/4)  Knowledge Creation  Knowledge Import  Knowledge Capture  Knowledge Retrieval and Access  Knowledge Use

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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)

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 13 The Knowledge Meta-Process (1/3)  Feasibility Study  Kickoff phase  Refinement Phase  Evaluation Phase  Maintenance Phase

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 17 A Framework for KM on the SW 1.Knowledge Capturing 2.Knowledge Repository 3.Knowledge Processing 4.Knowledge Sharing 5.Using of Knowledge

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 23 Proposed KM Framework

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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  Hierarchical description of important concepts and definition of their properties (attribute- value mechanism)

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 31 Languages for KR 1.XML 2.RDF / RDF Schema 3.DAML+OIL 4.OWL

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 34 On-To-Knowledge Architecture

University of Crete HY566-Semantic Web Spring‘03Knowledge Management & Semantic Web 35 On-To-Knowledge Technical Architecture

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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

University of Crete HY566-Semantic Web Spring‘03Knowledge 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