© 2001 empolis UK1 Topic Maps, NewsML and XML: Possible Integration and Implementations. By Soelwin Oo.

© 2001 empolis UK2 Introduction  Integration of Topic Map Technologies  K42 & NewsML  Implementation of Topic Map Technologies  Pitfalls of Topic Map Ontology Merging

© 2001 empolis UK3 Topic Maps  Capture concepts  Impose Knowledge on to Data  Create Knowledge Based Relationships  Powerful Knowledge & Resource Retrieval

© 2001 empolis UK4 NewsML  Developed by the IPTC  XML Based  Possess Topic Ontology Metadata  Content Driven Approach For Topic Map Navigation

© 2001 empolis UK5 NewsML & Topic Map Integration  Example IPTC Topic Set Relative significance of the metadata applied to a NewsComponent. High The metadata is very important. Medium The metadata is quite important. Low The metadata is of low importance.

© 2001 empolis UK6 NewsML & Topic Map Integration  Topic Properties: –Formal Name –Description –Topic Type  NewsML Topic ‘High’: High The metadata is very important.

© 2001 empolis UK7  FormalName  XTM representation: NewsML & Topic Map Integration High IptcImportance High

© 2001 empolis UK8 NewsML & Topic Map Integration  Description  XTM representation: The metadata is very important. High The metadata is very important.

© 2001 empolis UK9  TopicType  XTM representation: High The metadata is very important. NewsML & Topic Map Integration

© 2001 empolis UK10 NewsML & Topic Map Integration  Instance/Type Relationships of ‘Importance’ Topic Set Topic BaseName:”Importance” Scope:”IptcTopicType” Topic BaseName:”High” Scope:” IptcImportance” Topic BaseName:”Medium” Scope:” IptcImportance” Topic BaseName:”Low” Scope:” IptcImportance” Instance

© 2001 empolis UK11 NewsML Topic Map Implementation  Currently only possess TopicType/Instance relationships  Topics can possess Occurrences to ‘Addressable Resources’  Relate NewsML articles as Occurrences of Ontology Topics  Implement Resource Retrieval Mechanism

© 2001 empolis UK12 NewsML Topic Map Implementation  Methodology for TopicType/Instance driven Resource Retrieval 1. Obtain a NewsML document that is located in a uniquely addressable location. (For example an URL) 2. Create this document as an Occurrence of all the Ontology Topics that occur within it. 3. From this document, list all the Topics that have this document as an Occurrence. 4. The user can then choose a particular Topic of interest from the list and select to retrieve other addressable Occurrences of that Topic.

© 2001 empolis UK13 NewsML Topic Map Implementation  Content Driven Approach to Topic Map Navigation  User’s starting point is the Base Ontologies instantiated within NewsML article  Presentation of sets of related Topics  Retrieval of sets of related Resources

© 2001 empolis UK14 NewsML Topic Map Implementation  Topic Association Driven Resource Retrieval  Topic Association Powered Occurrence ‘Filtering’  Resource Channel creation

© 2001 empolis UK15  Create Topic Association Template ‘Channel’  Members:  Arcs: Role >> Ontology Topic to be played by Topic Type >> Ontology Topic Role >> Channel Topic to be played by Topic Type >> Channel Topic NewsML Topic Map Implementation from Channel Topic to Ontology Topic: includes the topic of For example: Sport Channel Football from Ontology Topic to Channel Topic: is assigned to For example: Football Sport Channel

© 2001 empolis UK16 NewsML Topic Map Implementation  Instantiate ‘Channel’ Topic Association Template

© 2001 empolis UK17 NewsML Topic Map Implementation  Process incoming NewsML documents  Create documents as Occurrences of Ontology Topics  Segregate Documents according to Ontology Topic Channel association  Display Channelled Documents

© 2001 empolis UK18 NewsML Topic Map Implementation Football FT Index Knitting Cricket Nasdaq NewsML News Feed NewsML Processor Sport Channel Business Channel John's Channel Football Cricket Nasdaq FT Index Nasdaq Football Knitting Process news feed Filter documents according to associated Channel Topic

© 2001 empolis UK19 NewsML Topic Map Implementation  Create the individual documents as Occurrences of their respective Ontology Topics.  Segregate the Occurrences of the Ontology Topics according to their associated Channel Topics. Sports Channel Football Cricket Channel TopicOntology Topics NewsML Resources Football Cricket

© 2001 empolis UK20 Integration of Multiple Topic Maps  Existence of additional Custom Vocabularies  Potential for duplication of Topics Water/H 2 O H2H2 H2OH2OWater IceSteamO2O2 consists of IceSteamO2O2 H2H2 can exist as consists of Topic Association

© 2001 empolis UK21 Integration of Multiple Topic Maps  Ontology Merging provides a richer network of Knowledge  Solutions for duplicate Topic identification –Unique Topic ID –Unique basename-scope pairs –Name/ID Mapping Tables –Common Reference Ontology  No panacea for Ontology Merging

© 2001 empolis UK22 Conclusion  Steps for XML Ontology capturing  Simple TopicType/Instance based Resource Retrieval  Topic Association driven Occurrence Filtering  Topic Map Ontology Merging