University of Crete HY566-Semantic Web CS566 – Semantic Web Computer Science Department - UoC...

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

Spring‘03 Knowledge Management & Semantic WebKnowledge 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



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




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



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.



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



Contents






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



The Knowledge Process (1/4)

Knowledge Creation

Knowledge Import Knowledge

Capture Knowledge

Retrieval and Access

Knowledge Use




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 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 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



The Knowledge Meta-Process (1/3)

Feasibility Study Kickoff phase Refinement

Phase Evaluation Phase Maintenance

Phase




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




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



Contents






A Framework for KM on the SW

1. Knowledge Capturing2. Knowledge Repository3. Knowledge Processing4. Knowledge Sharing5. Using of Knowledge



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 Repository

Use of Relational Databases• Efficient storing• Efficient Access to RDF metadata

It is an RDF Repository like RDFSuite or RDF Gateway



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 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



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



Proposed KM Framework



Contents






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



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



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



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



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



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)



Languages for KR

1. XML

2. RDF / RDF Schema

3. DAML+OIL

4. OWL



Contents



Knowledge Representation Knowledge Management System

Example Conclusion Remarks



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



On-To-Knowledge Architecture



On-To-Knowledge Technical Architecture



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



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



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



Contents






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

University of Crete HY566-Semantic Web CS566 – Semantic Web Computer Science Department - UoC...

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