University of Crete
HY566-Semantic Web
CS566 – Semantic Web
Knowledge Management
& Semantic Web
Παπαγγελής Μάνος, Κοφφινά Ιωάννα, Κοκκινίδης Γιώργος
Computer Science Department - UoC
Heraklion 5 June, 2003
University of Crete
HY566-Semantic Web
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
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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 metaprocess
 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)
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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.
2.
3.
4.
5.
Knowledge Capturing
Knowledge Repository
Knowledge Processing
Knowledge Sharing
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
•
•
•
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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 (attributevalue 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
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