Høgskolen i Oslo Topic maps, a technology for knowledge organisation Presentation at ELAG 2004 Nils Pharo.

Slides:



Advertisements
Similar presentations
Dr. Leo Obrst MITRE Information Semantics Information Discovery & Understanding Command & Control Center February 6, 2014February 6, 2014February 6, 2014.
Advertisements

BS 8723 : a new British Standard for structured vocabularies Stella G Dextre Clarke Information Consultant.
© UNIVERSITETETS SENTER FOR INFORMASJONSTEKNOLOGI UNIVERSITETET I OSLO USIT Side 1 Knowledge organization with TopicMaps Thomas Flemming, web-gruppa USIT.
XML and Enterprise Computing. What is XML? Stands for “Extensible Markup Language” –similar to SGML and HTML –document “tags” are used to define content.
PoolParty Vasiljevic Vladica,
Implementing folderless document management using metadata.
Information and Business Work
Standards for networked knowledge organisation systems Ron Davies European Library Automation Group Bucharest, April 2006.
Human Language Technologies. Issue Corporate data stores contain mostly natural language materials. Knowledge Management systems utilize rich semantic.
METS What is METS ? What is METS ? A schema that provides a flexible mechanism for encoding descriptive, administrative, and structural metadata for a.
Bieber et al., NJIT © Slide 1 Digital Library Integration Masters Project and Masters Thesis Summer and Fall 2005 CIS 786 / CIS Fall.
1 Conceptual Modeling of Topic Maps with ORM Versus UML Are D. Gulbrandsen The XML group, Center for Information Technology Services, University of Oslo,
Classifications, Taxonomies, Ontologies, Thesauri The following three terms: classifications, taxonomies and ontologies are often confused. This is caused.
Knowledge organisation and information architecture, Nils Pharo Knowledge organisation and the Web Nils Pharo, 6th November 2002.
Disscussion about the FIPA Interaction Protocols FIPA IP Technical Committee (IP-TC) Gabriel Hopmans Morpheus Software Maastricht, the Netherlands.
Management of IT Environment (3)
XML TOPIC MAP JUNG J. W.. SNU OOPSLA Lab. contents What ’ s XTM? Why XTM? Element of XTM XTM Conceptual Model DTD Introduction to XTM Syntax.
Introduction to Geospatial Metadata – FGDC CSDGM National Coastal Data Development Center A division of the National Oceanographic Data Center Please .
Chapter 6: The Traditional Approach to Requirements
Metadata Standards and Applications 4. Metadata Syntaxes and Containers.
SDC JE-xxxx. Bruce Bargmeyer EPA/OIRM/EIM Division Tel: (202) WWW URL:
Euler, Topic Maps and Revolution An Introduction to ISO/IEC Topic Maps Tapsa Pippuri Senior Information Architect STEP Infotek A.S, Oslo.
PREMIS Tools and Services Rebecca Guenther Network Development & MARC Standards Office, Library of Congress NDIIPP Partners Meeting July 21,
Teaching Metadata and Networked Information Organization & Retrieval The UNT SLIS Experience William E. Moen School of Library and Information Sciences.
In The Name Of God. Jhaleh Narimisaei By Guide: Dr. Shadgar Implementation of Web Ontology and Semantic Application for Electronic Journal Citation System.
Indexing Knowledge Daniel Vasicek 2014 March 27 Introduction Basic topic is : All Human Knowledge Who Cares? Simple Examples.
Of 39 lecture 2: ontology - basics. of 39 ontology a branch of metaphysics relating to the nature and relations of being a particular theory about the.
Emnekart Oslo 2006 Henrik Laursen 1 - Copenhagen University Library since The Kings Library since National Deposit Library since Public.
Nancy Lawler U.S. Department of Defense ISO/IEC Part 2: Classification Schemes Metadata Registries — Part 2: Classification Schemes The revision.
The Agricultural Ontology Service (AOS) A Tool for Facilitating Access to Knowledge AGRIS/CARIS and Documentation Group Library and Documentation Systems.
SNU OOPSLA Lab. Chapter3: A Perspective on the Quest for Global Knowledge Interchange Steven R. Newcomb Edited by Jongnam Kim.
Definition of a taxonomy “System for naming and organizing things into groups that share similar characteristics” Taxonomy Architectures Applications.
10/22/2015ACM WIDM'20051 Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web Giannis Varelas Epimenidis Voutsakis.
Moving from a locally-developed data model to a standard conceptual model Jenn Riley Metadata Librarian Indiana University Digital Library Program.
And Coolheads Consulting A Processing Model for Topic Maps Knowledge Technologies 2001 Austin, 6 March 2001 Steven R. Newcomb Michel.
Content analysis and CERN Roman Chyla. Artificial intelligence Natural language processing Web of data Content analysis.
1 What is an Ontology? n No exact definition n A tool to help organize knowledge n Or a way to convey a theory on how to represent a class of things n.
Controlled Vocabulary & Thesaurus Design Resources & Future Directions.
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Knowledge Representation Semantic Web - Fall 2005 Computer.
How to express MARC in XML ELAG Workshop 10 Report.
Building a Topic Map Repository Xia Lin Drexel University Philadelphia, PA Jian Qin Syracuse University Syracuse, NY * Presented at Knowledge Technologies.
Information Architecture & Design Week 5 Schedule -Planning IA Structures -Other Readings -Research Topic Presentations Nadalia your Presentations.
Strategies for subject navigation of linked Web sites using RDF topic maps Carol Jean Godby Devon Smith OCLC Online Computer Library Center Knowledge Technologies.
Mining the Biomedical Research Literature Ken Baclawski.
Topic Maps introduction Peter-Paul Kruijsen CTO, Morpheus software ISOC seminar, april 5 th 2005.
Metadata “Data about data” Describes various aspects of a digital file or group of files Identifies the parts of a digital object and documents their content,
IA Tools to Inform IA Summit 2003 Madonnalisa G. Chan.
1 Value of Taxonomies in Knowledge Management Joe Schehr VP Knowledge Management and Technology Solutions LexisNexis.
SNU OOPSLA Lab. Chapter 4 The Rise and Rise of Topic Maps Sam Hunting.
XML Topic Maps (XTM) The GPS of the Web Eric Freese Chair, TopicMaps.Org.
Working with XML. Markup Languages Text-based languages based on SGML Text-based languages based on SGML SGML = Standard Generalized Markup Language SGML.
IRS Tax Map Electronic Research Tool David Brown Internal Revenue Service Media and Publications Division David Brown Internal Revenue Service Media and.
Topic Maps for Cultural Heritage Collections Conal Tuohy Senior Developer New Zealand Electronic Text Centre
2/10/2016Semantic Similarity1 Semantic Similarity Methods in WordNet and Their Application to Information Retrieval on the Web Giannis Varelas Epimenidis.
Using Bayesian Networks to Predict Plankton Production from Satellite Data By: Rob Curtis, Richard Fenn, Damon Oberholster Supervisors: Anet Potgieter,
Government Answers © Knowledge models for government information Marco Aarts Oslo, april
ISO TC37/SC4 N435 Nov 12, 2007 Presented by Miran Choi/ETRI Written by Jae Sung Lee/Chungbuk National Univ.
2 1 Database Systems: Design, Implementation, & Management, 7 th Edition, Rob & Coronel Data Models Why data models are important About the basic data-modeling.
Enable Semantic Interoperability for Decision Support and Risk Management Presented by Dr. David Li Key Contributors: Dr. Ruixin Yang and Dr. John Qu.
The Agricultural Ontology Server (AOS) A Tool for Facilitating Access to Knowledge AGRIS/CARIS and Documentation Group Food and Agriculture Organization.
Ontologies COMP6028 Semantic Web Technologies Dr Nicholas Gibbins
Conal Tuohy Topic NZETC Conal Tuohy
Database Systems: Design, Implementation, and Management Tenth Edition
ece 627 intelligent web: ontology and beyond
Knowledge Management Systems
Lifecycle Metadata for Digital Objects
Topic Maps - an introduction
PREMIS Tools and Services
Semantic Markup for Semantic Web Tools:
Giannis Varelas Epimenidis Voutsakis Paraskevi Raftopoulou
Presentation transcript:

Høgskolen i Oslo Topic maps, a technology for knowledge organisation Presentation at ELAG 2004 Nils Pharo

Schedule l Topic maps is a standard for organising digital content l ISO certified in 2002 (ISO 13250) l Used for structuring web sites, knowledge management…

The basic elements of topic maps Topic maps consist of l Topics l Associations l Occurrences i.e. the TAO of topic maps (Pepper, 2002)

Ontologies l An ontology contains the topic map’s input l Examples of ontologies are thesauri, taxonomies, word-nets, etc l Ontologies are not necessarily structured hierarchically

Topic map technology l XTM – XML Topic Maps l HyTM – SGML based l Topic map engines using databases of various kinds l XTM + XSL

Example of XTM 1

Example of XTM 2

Example of XTM 3

Characteristics of topic maps 1. topics have names 2. topics are knit together using associations 3. a topic may be categorised by an unlimited number of topic types 4. topic types are topics on a higher level of abstraction 5. association types are association on a higher level of abstraction 6. occurrences may be external or internal to the topic map 7. topics can be disambiguated using subject indicators

Topic maps for knowledge organisation Two challenges: l Hierarchical versus associative structuring l Published Subject Indicators

Hierarchies and networks l Hierarchical in the thesaurus tradition with strict rules on what associative relationships should be allowed or l Open up for “unlimited” implementation of associative relationships

Published Subject Indicators l PSIs contain “definitions” of subjects l Who should provide such definitions? l Centralised control or principle of natural selection? l LIS traditionally aimed for control l WWW of anarchistic nature