25/04/'07 updated 15/04708CmpE 588 Spring 2008 EMU1 Developing Ontologies for Knowledge Management Atilla ELÇİ Dept. of Computer Engineering Eastern Mediterranean

25/04/'07 updated 15/04708 CmpE 588 Spring 2008 EMU 1

Developing Ontologies for Knowledge Management

Atilla ELÇİDept. of Computer Engineering

Eastern Mediterranean University


Knowledge Management Topics

Motivation Terms & Definitions Roles of ontologies PROTON ontology as bases for KM /

SemWeb Apps


Motivation Knowledge Representation (KR):

= A world view: Building models of a domain/problem which allow for automatic reasoning and interpretation.=> Formal semantics (Ontology!)=> Machine-interpretable meaning

Semantic repository:Storage, querying, and management of structured data

DBMS vs Ontology-based O-B provides depth of meaning not available through

DBMS


Terminology: KM views What Is Knowledge Management by the The

Knowledge Management Forum (KMForum): Read through these personal views on K & KM Note the diversity of views & interests Contrast & cross-check definitions of some viewers.


Terminology Dublin Core Metadata Initiative (DCMI, DC):

interoperable online metadata standards Dataset: a set of structured data (list, table, DB,

etc.) useful for direct software processing Ontology:

= Paradigm for KR in AI. Conceptual schemata Formal ontology as logical formalism as in OWL Schemata or ‘inteligent’ views over information

resources: For indexing, querying, and referencing non-ontological

datasets For DB, Document Mngt Sys, Catalog, OLAP,


Terminology (continued)

Ontology classification based on generality of conceptualization: Upper-level ontology:

A general model suitable for large variety of tasks, domains, and application areas. Can be used to line up independently developed ontologies if linked to it.

Domain ontology:For ‘specific’ domain of interest

App / Task ontology:For a specific range of applications / tasks.

Knowledge base (KB): A dataset with formal semantics and knowledge representation

allowing automatic inference. Ontology: O=<C, R, I, A> where:

C: is the set of classes R: is the set of relations among the classes I: is the set of instances from the domain. Instances belong to classes A: is the set of axioms (say, business rules).



Ref ontology definition as O-grammar, the issue of what is instance what is schema definition may not always easily resolved.

Data qualia: A data quale is an orthogonal quality of data that may be used for independent classification:

Semantics: whether it is formally represented Structure: if the data is formally structured Schema: data that determines shape and/or meaning of ontology data.

Sorts of data (“_” stands for ‘any value’ not determined): Data: (_,_,_), ie. Any sort of collection of data

Dataset: (_,structured,_) Knowledge Base: (semantic,structured,_) Ontology: (semantic,structured,schema) Non-semantic schemata: (nonsemantic,structured,schema) Database: (nonsemantic,structured,schema) Mixed datasets: (_,structured,schema&non-schema) Content: (_,non-structured,_)

Metadata: data on data, annotation, ... How to represent in (?,?,?)? Semi-structured data:



Sorts of data (continued): Semi-structured data:

KR/NLP Docs containing free text fragments in structured according to some schema

DB Data of non-relational data model.

Ref. Fig. 7.2- Structured vs semantic positioning of various sorts of data.


Roles of Ontologies Ontology as Database Schema:

May not contain instance data. Such as RDBMS schema.

Ontology as Topic Hierarchy: Classification for various purposes:

DCMI and library classification Yahoo & DMoz taxonomies for Web data See Section 4 in this for depth of Yahoo! Directory. Compare Topic-Ontology versus Schema-Ontology (Sect.

7.5)

Ontology as Enterprise Resource Model: Ref.: Ontolog Database & Ontology Mini-Series.


Mapping & Querying Disparate Knowledge Bases Self study: Davies §6.3


PROTON (PROTo ONtology) Ontology A light-weight uppper-level ontology to serve as

model bases for information science community for, for example: Seed for ontology generation Automatic entity recognition & information extraction Metadata generation / semantic annotation.

Design Rationale: For usage in KM & SemWeb appls Light-weight: for being unrestrictive Prefers not to deal with time & space Low-cost of adoption & maintenance Scalable reasoning


PROTON (contiuned)

Consists of ~300 classes & 100 properties for: Semantic annotation Indexing, and retrieval.

Design principles: Domain independence Light-weight logical definitions Alignment with popular metadata standards Good collection of named entity types (people,

organizations, locations, numbers, dates, addresses. Structure:

In OWL Lite In four modules: System, Top, Upper, and Knowledge

Management (KM) Organized á la DILIGENT Methodology,


PROTON (contiuned)

Scope: Developed in the SEKT Project through sampling of a corpus of

general news. General entity types appearing commonly (Person, Location,

Organization, Money, Date, ...) are in PROTON Top. KM aspects stems from:

KIMO of KIM Project OpenCyc Wordnet DOLCE EuroWordnet

Voluntary compliance with: Dublin Core Automatic Content Extraction annotation types Alexandria Digital Library Feature Type Thesaurus Future compliance with: FOAF and other popular standars &

ontologies.


PROTON (contiuned)

Architecture: Site at Semanticweb.org Organized in three levels: Basic, Top, Upper In four modules:

System (basic; protons:...): application ontology meant for use by ontology-based software

Top (top; protont:...): abstractions Upper (upper; protonu:...): specific cases KM (upper; protonkm:...): specific cases


PROTON (contiuned)

KM module: for application-specific extension of PROTON: Information Space:collection of themed info resources Software Agent: specialized Agent User: User and UserProfile Profile User Profile Mention: name droppings, references to (private)

instances Weighted Term: relates objects to numbers Device: references to user devices.

Organizations The Knowledge Management Forum (KMForum)

Virtual community of practice focused on furthering fundamental theories, methods and practices. Features archives and news.

What Is Knowledge Management KM Forum

Boston Knowledge Management Forum: A Community of Practice: Learning and Working in the Knowledge Management Community

KnowledgeBoard Forum to establish a community and to support and identify

commonality in terminology, application and implementation. Features news, workshops, a library, ...


Conferences Knowledge Representation Ontology

Workshop (KROW 2008). Eleventh International Conference on

Principles of Knowledge Representation and Reasoning (KR 2008), Sydney, Australia, September 16 - 19, 2008



Commercial Conferences Knowledge Base Publishing course series of the Montague

Institute includes articles: Introduction to Knowledge Base Publishing Taxonomies, search & Sharepoint Metadata and search Integrating taxonomies Information modeling and metadata management

See also Roundtables, for example the following: Benchmarking Sharepoint for KM (December 12, 2007) Six weeks to the Semantic Web (November 7, 2007) Integrating folksonomies with Google (October 17, 2007) Migrating metadata to the Semantic Web (September 5, 2007)


References John Davies, Rudi Studer, Paul Warren (Editors):

Semantic Web Technologies: Trends and Research in Ontology-based Systems, John Wiley & Sons (July 11, 2006). ISBN: 0470025964. Ch. 7.: pp. 115-138.

W3C Semantic Web Tools Wiki page: Check ...

Documents

25/04/'07 updated 15/04708CmpE 588 Spring 2008 EMU1 Developing Ontologies for Knowledge Management Atilla ELÇİ Dept. of Computer Engineering Eastern Mediterranean