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University of CreteUniversity of Crete HY566-Semantic WebHY566-Semantic Web
CS566 – Semantic Web
Computer Science Department - UoC
Heraklion 5 June, 2003
Παπαγγελής Μάνος, Κοφφινά Ιωάννα, Κοκκινίδης Γιώργος
Knowledge Management& Semantic Web
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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
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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
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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
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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.
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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
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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
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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
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The Knowledge Process (1/4)
Knowledge Creation
Knowledge Import Knowledge
Capture Knowledge
Retrieval and Access
Knowledge Use
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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
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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)
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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
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The Knowledge Meta-Process (1/3)
Feasibility Study Kickoff phase Refinement
Phase Evaluation Phase Maintenance
Phase
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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
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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
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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
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A Framework for KM on the SW
1. Knowledge Capturing2. Knowledge Repository3. Knowledge Processing4. Knowledge Sharing5. Using of Knowledge
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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
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Knowledge Repository
Use of Relational Databases• Efficient storing• Efficient Access to RDF metadata
It is an RDF Repository like RDFSuite or RDF Gateway
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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
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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
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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
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Proposed KM Framework
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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
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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
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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
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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
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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
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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
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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)
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Languages for KR
1. XML
2. RDF / RDF Schema
3. DAML+OIL
4. OWL
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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
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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
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On-To-Knowledge Architecture
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On-To-Knowledge Technical Architecture
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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
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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
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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
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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
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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