SlideShare a Scribd company logo
Building and Integrating Competitive IntelligenceReports Using the Topic Map Technology Vojtěch Svátek, Tomáš Kliegr, Jan Nemrava, Martin Ralbovsý,  Vojtěch Roček ,Jan Rauch University of Economics, Winston Churchill Sq. 4, Prague, Czech Republic Jiří Šplíchal, Tomáš Vejlupek Tovek s.r.o., Chrudimská 1418, Prague, Czech Republic
CI and Business Clusters CI – Competitive Intelligence is a sub-field of business intelligence that supports decision makers in understanding the competitive environment by means of reports prepared based on (public) resources. Cluster is a set of companies in related fields operating in the same geographical area How to link and search multiple CI reports? Envisaged Solution: Create a complementary topic map  that would put the important facts into context
TheTopic Map 1] Ontology: putting concepts into context Instances Associations TopicTypes 2] Annotate important bits of text  with ontology concepts
Testbed A case study assignment at an introductory knowledge engineering course, attended by 150- 200 students each semester The goal is to get a picture of  the whole industry Students work in groups of 5 ,[object Object],Two assignments: Students write CI reports of about 25 pages based on publicly available sources of information.  2)  Important pieces of information are expressed in a machine-readable way with topic maps. Each semester we tested a slightly different setting (S1-S3) of tools and techniques… now running for the fourth semester
S1: Individual ontologies, merge Each team wrote the CI report (in  a text editor) Consequently, they obtained a copy of a startup ontology Students extended the ontology with new topic types using Tovek Topic Mapper (TTM): an ontology editor and annotating tool (desktop application) Students used TTM to annotate bits of text with a topic type.  Annotated text became an internal occurrence in the topic map The ontologies enriched with new topic types and annotations were collected from all teams We used OKS to merge the topic maps Extend ontology Annotate DOC HTML The result is a linking file between the document and the shared topic map XTM Startup Ontology Result is a linking file conneting document with the topic map
Topic Maps Merging Merging of: Business cluster topic map, All unstructured documents, Linking files Linking files CI reports HTML XTM DOC Shared industry topic map
Issues Annotated text fragmented, since each fragment is stored as internal occurrence Laborious Duplicate topic types Effective merging requires unique identifiers, which was achieved only for companies (registration numbers used in subject indicators)
S2: Collaborative Ontology Population  Goal: remove duplicate topic types Startup ontology was placed on a PostgreSQL server Student teams collaboratively enriched the ontology with topic types, association  types  and occurrence types they assumed to use during the annotation in Topic Mapper The ontology was then frozen: each team got its copy.  TTM was used only for annotation, and then OKS for merging Collaborative Ontology Creation remote repository Topic Maps for Merging Import ontology Shared topic map students Annotate only
Issues Separation of ontology enrichment and document annotation is not natural and requires an experienced annotator Annotations still kept as internal occurrences Multiple concurrent instances of OKS servers resulted in corruption in the topic map, probably due to caching in OKS Two topic map tools used, original documents not easily accessible
S3: Annotation by linking Goal: move annotation fully to the web All students used one instance of OKS server CI reports were placed into a CMS (Joomla!) Each structural unit was assigned an id (via HTML’s <a name>) Annotation was done via external occurrences External occurrences point at a specific bookmark at the document, where the annotated fragment starts. The annotated fragment is assumed to span up to the nearest following bookmark.
Issues … and finally advantages Issues: OKS Ontopoly was not stable enough in concurrent setting X-Pointer technology, which could be used to mark spans in the document, is not supported by current browsers Advantages: The text with full content (including even figures or links) in the CMS is more intelligible than fragments in internal occurrences Further editing of an article is possible in the CMS without invalidating the annotation Full-text search feature of the CMS can be exploited Bringing the best from the CMS world and OKS
Summary& Plans On the competitive intelligence use case, we tested several approaches for collaborative ontology design and document annotation with some 500 users altogether. OKS is a great tool, which gets additional edge by being web-based We deem the last approach taken: documents stored in a CMS linked through external occurrences with OKS as usable - contingent on improvements in Ontopoly and Joomla! Ontopoly wishes Greater stability in case of concurrent user access We missed user management and versioning in Ontopoly Joomla! wishes Support for „tagging“ arbitrary bits of text A tool for creating XPointer  URLs  based on user selection A functionality that would highlight part of the document based on a URL containing XPointer span

More Related Content

What's hot

International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)
ijfcst journal
 
ModelWriter Presentation International 01-07-2015
ModelWriter Presentation International 01-07-2015ModelWriter Presentation International 01-07-2015
ModelWriter Presentation International 01-07-2015
Ferhat Erata
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
A Mathematical Approach to Ontology Authoring and Documentation
A Mathematical Approach to Ontology Authoring and DocumentationA Mathematical Approach to Ontology Authoring and Documentation
A Mathematical Approach to Ontology Authoring and Documentation
Christoph Lange
 
Real Time Competitive Marketing Intelligence
Real Time Competitive Marketing IntelligenceReal Time Competitive Marketing Intelligence
Real Time Competitive Marketing Intelligence
feiwin
 
Tensor Networks and Their Applications on Machine Learning
Tensor Networks and Their Applications on Machine LearningTensor Networks and Their Applications on Machine Learning
Tensor Networks and Their Applications on Machine Learning
Kwan-yuet Ho
 
Mining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire DataMining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire Data
feiwin
 
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect match
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect matchLinked Open (Geo)Data and the Distributed Ontology Language – a perfect match
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect match
Christoph Lange
 
Ontology Design Patterns for the Semantic Business Processes
Ontology Design Patterns for the Semantic Business ProcessesOntology Design Patterns for the Semantic Business Processes
Ontology Design Patterns for the Semantic Business Processes
Violeta Damjanovic-Behrendt
 
International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)
ijfcst journal
 
Parallel text extraction from multimodal comparable corpora
Parallel text extraction from multimodal comparable corporaParallel text extraction from multimodal comparable corpora
Parallel text extraction from multimodal comparable corpora
Haithem Afli
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcst journal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
ijfcstjournal
 

What's hot (19)

International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)
 
ModelWriter Presentation International 01-07-2015
ModelWriter Presentation International 01-07-2015ModelWriter Presentation International 01-07-2015
ModelWriter Presentation International 01-07-2015
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
A Mathematical Approach to Ontology Authoring and Documentation
A Mathematical Approach to Ontology Authoring and DocumentationA Mathematical Approach to Ontology Authoring and Documentation
A Mathematical Approach to Ontology Authoring and Documentation
 
Real Time Competitive Marketing Intelligence
Real Time Competitive Marketing IntelligenceReal Time Competitive Marketing Intelligence
Real Time Competitive Marketing Intelligence
 
Tensor Networks and Their Applications on Machine Learning
Tensor Networks and Their Applications on Machine LearningTensor Networks and Their Applications on Machine Learning
Tensor Networks and Their Applications on Machine Learning
 
Mining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire DataMining from Open Answers in Questionnaire Data
Mining from Open Answers in Questionnaire Data
 
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect match
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect matchLinked Open (Geo)Data and the Distributed Ontology Language – a perfect match
Linked Open (Geo)Data and the Distributed Ontology Language – a perfect match
 
Ontology Design Patterns for the Semantic Business Processes
Ontology Design Patterns for the Semantic Business ProcessesOntology Design Patterns for the Semantic Business Processes
Ontology Design Patterns for the Semantic Business Processes
 
International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)International Journal in Foundations of Computer Science & Technology(IJFCST)
International Journal in Foundations of Computer Science & Technology(IJFCST)
 
Parallel text extraction from multimodal comparable corpora
Parallel text extraction from multimodal comparable corporaParallel text extraction from multimodal comparable corpora
Parallel text extraction from multimodal comparable corpora
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 
International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)International Journal on Foundations of Computer Science & Technology (IJFCST)
International Journal on Foundations of Computer Science & Technology (IJFCST)
 

Viewers also liked

Real-time Generation of Topic Maps from Speech Streams
Real-time Generation of Topic Maps from Speech StreamsReal-time Generation of Topic Maps from Speech Streams
Real-time Generation of Topic Maps from Speech Streams
tmra
 
Why not scoping Subject Identifiers?
Why not scoping Subject Identifiers?Why not scoping Subject Identifiers?
Why not scoping Subject Identifiers?
tmra
 
XML Holland 2008
XML Holland 2008XML Holland 2008
XML Holland 2008
tmra
 
ActiveTM - A Topic Maps - Object Mapper
ActiveTM - A Topic Maps - Object MapperActiveTM - A Topic Maps - Object Mapper
ActiveTM - A Topic Maps - Object Mapper
tmra
 
Dense Topic Maps
Dense Topic MapsDense Topic Maps
Dense Topic Maps
tmra
 
Paraconsistent Reasoning in Ontopedia
Paraconsistent Reasoning in OntopediaParaconsistent Reasoning in Ontopedia
Paraconsistent Reasoning in Ontopedia
tmra
 
Topic Maps Web Service: Case Examples and General Structure
Topic Maps Web Service: Case Examples and General StructureTopic Maps Web Service: Case Examples and General Structure
Topic Maps Web Service: Case Examples and General Structure
tmra
 
Connecting Topincs - Using transclusion to connect proxy spaces
Connecting Topincs - Using transclusion to connect proxy spacesConnecting Topincs - Using transclusion to connect proxy spaces
Connecting Topincs - Using transclusion to connect proxy spaces
tmra
 
What is a subject?
What is a subject?What is a subject?
What is a subject?
tmra
 
Topic Maps in ‘Not working on the web shock!’
Topic Maps in ‘Not working on the web shock!’Topic Maps in ‘Not working on the web shock!’
Topic Maps in ‘Not working on the web shock!’
tmra
 
TM/XML - Representing Topic Maps in XML
TM/XML - Representing Topic Maps in XMLTM/XML - Representing Topic Maps in XML
TM/XML - Representing Topic Maps in XML
tmra
 
Temporal Qualification in Topic Maps
Temporal Qualification in Topic MapsTemporal Qualification in Topic Maps
Temporal Qualification in Topic Maps
tmra
 
Semantic Mashups with Wandora
Semantic Mashups with WandoraSemantic Mashups with Wandora
Semantic Mashups with Wandora
tmra
 
A PHP library for Ontopia-CMS Integration
A PHP library for Ontopia-CMS IntegrationA PHP library for Ontopia-CMS Integration
A PHP library for Ontopia-CMS Integration
tmra
 
National Data Standardization: A Place for Topic Maps?
National Data Standardization: A Place for Topic Maps?National Data Standardization: A Place for Topic Maps?
National Data Standardization: A Place for Topic Maps?
tmra
 
Putting topic maps to rest.tmra2010
Putting topic maps to rest.tmra2010Putting topic maps to rest.tmra2010
Putting topic maps to rest.tmra2010
tmra
 
Topic Maps for improved access to and use of content in relational databases ...
Topic Maps for improved access to and use of content in relational databases ...Topic Maps for improved access to and use of content in relational databases ...
Topic Maps for improved access to and use of content in relational databases ...
tmra
 
TMCL and OWL
TMCL and OWLTMCL and OWL
TMCL and OWL
tmra
 
Event based modelling
Event based modellingEvent based modelling
Event based modelling
tmra
 

Viewers also liked (19)

Real-time Generation of Topic Maps from Speech Streams
Real-time Generation of Topic Maps from Speech StreamsReal-time Generation of Topic Maps from Speech Streams
Real-time Generation of Topic Maps from Speech Streams
 
Why not scoping Subject Identifiers?
Why not scoping Subject Identifiers?Why not scoping Subject Identifiers?
Why not scoping Subject Identifiers?
 
XML Holland 2008
XML Holland 2008XML Holland 2008
XML Holland 2008
 
ActiveTM - A Topic Maps - Object Mapper
ActiveTM - A Topic Maps - Object MapperActiveTM - A Topic Maps - Object Mapper
ActiveTM - A Topic Maps - Object Mapper
 
Dense Topic Maps
Dense Topic MapsDense Topic Maps
Dense Topic Maps
 
Paraconsistent Reasoning in Ontopedia
Paraconsistent Reasoning in OntopediaParaconsistent Reasoning in Ontopedia
Paraconsistent Reasoning in Ontopedia
 
Topic Maps Web Service: Case Examples and General Structure
Topic Maps Web Service: Case Examples and General StructureTopic Maps Web Service: Case Examples and General Structure
Topic Maps Web Service: Case Examples and General Structure
 
Connecting Topincs - Using transclusion to connect proxy spaces
Connecting Topincs - Using transclusion to connect proxy spacesConnecting Topincs - Using transclusion to connect proxy spaces
Connecting Topincs - Using transclusion to connect proxy spaces
 
What is a subject?
What is a subject?What is a subject?
What is a subject?
 
Topic Maps in ‘Not working on the web shock!’
Topic Maps in ‘Not working on the web shock!’Topic Maps in ‘Not working on the web shock!’
Topic Maps in ‘Not working on the web shock!’
 
TM/XML - Representing Topic Maps in XML
TM/XML - Representing Topic Maps in XMLTM/XML - Representing Topic Maps in XML
TM/XML - Representing Topic Maps in XML
 
Temporal Qualification in Topic Maps
Temporal Qualification in Topic MapsTemporal Qualification in Topic Maps
Temporal Qualification in Topic Maps
 
Semantic Mashups with Wandora
Semantic Mashups with WandoraSemantic Mashups with Wandora
Semantic Mashups with Wandora
 
A PHP library for Ontopia-CMS Integration
A PHP library for Ontopia-CMS IntegrationA PHP library for Ontopia-CMS Integration
A PHP library for Ontopia-CMS Integration
 
National Data Standardization: A Place for Topic Maps?
National Data Standardization: A Place for Topic Maps?National Data Standardization: A Place for Topic Maps?
National Data Standardization: A Place for Topic Maps?
 
Putting topic maps to rest.tmra2010
Putting topic maps to rest.tmra2010Putting topic maps to rest.tmra2010
Putting topic maps to rest.tmra2010
 
Topic Maps for improved access to and use of content in relational databases ...
Topic Maps for improved access to and use of content in relational databases ...Topic Maps for improved access to and use of content in relational databases ...
Topic Maps for improved access to and use of content in relational databases ...
 
TMCL and OWL
TMCL and OWLTMCL and OWL
TMCL and OWL
 
Event based modelling
Event based modellingEvent based modelling
Event based modelling
 

Similar to Building and Integrating Competitive Intelligence Reports Using the Topic Map Technology

A Metamodel For Web Page Design
A Metamodel For Web Page DesignA Metamodel For Web Page Design
A Metamodel For Web Page Design
Joe Osborn
 
Ck32985989
Ck32985989Ck32985989
Ck32985989
IJMER
 
Searching Repositories of Web Application Models
Searching Repositories of Web Application ModelsSearching Repositories of Web Application Models
Searching Repositories of Web Application Models
Marco Brambilla
 
Iot ontologies state of art$$$
Iot ontologies state of art$$$Iot ontologies state of art$$$
Iot ontologies state of art$$$
Sof Ouni
 
GATE, HLT and Machine Learning, Sheffield, July 2003
GATE, HLT and Machine Learning, Sheffield, July 2003GATE, HLT and Machine Learning, Sheffield, July 2003
GATE, HLT and Machine Learning, Sheffield, July 2003
butest
 
Modular Documentation Joe Gelb Techshoret 2009
Modular Documentation Joe Gelb Techshoret 2009Modular Documentation Joe Gelb Techshoret 2009
Modular Documentation Joe Gelb Techshoret 2009
Suite Solutions
 
Semantic Web in Action: Ontology-driven information search, integration and a...
Semantic Web in Action: Ontology-driven information search, integration and a...Semantic Web in Action: Ontology-driven information search, integration and a...
Semantic Web in Action: Ontology-driven information search, integration and a...
Amit Sheth
 
Understanding Information Architecture
Understanding Information ArchitectureUnderstanding Information Architecture
Understanding Information Architecture
Scott Abel
 
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...
A N  E XTENSION OF  P ROTÉGÉ FOR AN AUTOMA TIC  F UZZY - O NTOLOGY BUILDING U...A N  E XTENSION OF  P ROTÉGÉ FOR AN AUTOMA TIC  F UZZY - O NTOLOGY BUILDING U...
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...
ijcsit
 
Survey on article extraction and comment monitoring techniques
Survey on article extraction and comment monitoring techniquesSurvey on article extraction and comment monitoring techniques
Survey on article extraction and comment monitoring techniques
Anunaya
 
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
ecij
 
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
ecij
 
Industry-Academia Communication In Empirical Software Engineering
Industry-Academia Communication In Empirical Software EngineeringIndustry-Academia Communication In Empirical Software Engineering
Industry-Academia Communication In Empirical Software Engineering
Per Runeson
 
PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...
PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...
PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...
ijitcs
 
DODDLE-OWL: A Domain Ontology Construction Tool with OWL
DODDLE-OWL: A Domain Ontology Construction Tool with OWLDODDLE-OWL: A Domain Ontology Construction Tool with OWL
DODDLE-OWL: A Domain Ontology Construction Tool with OWL
Takeshi Morita
 
Software development effort reduction with Co-op
Software development effort reduction with Co-opSoftware development effort reduction with Co-op
Software development effort reduction with Co-op
lbergmans
 
Collaborative Modeling of Processes and Ontologies with MoKi
Collaborative Modeling of Processes and Ontologies with MoKiCollaborative Modeling of Processes and Ontologies with MoKi
Collaborative Modeling of Processes and Ontologies with MoKi
Mauro Dragoni
 
Cora For ITDG
Cora For ITDGCora For ITDG
Cora For ITDG
Carlo Vaccari
 
Ontology Engineering for Systems Engineering
Ontology Engineering for Systems EngineeringOntology Engineering for Systems Engineering
Ontology Engineering for Systems Engineering
Anatoly Levenchuk
 
Ju3517011704
Ju3517011704Ju3517011704
Ju3517011704
IJERA Editor
 

Similar to Building and Integrating Competitive Intelligence Reports Using the Topic Map Technology (20)

A Metamodel For Web Page Design
A Metamodel For Web Page DesignA Metamodel For Web Page Design
A Metamodel For Web Page Design
 
Ck32985989
Ck32985989Ck32985989
Ck32985989
 
Searching Repositories of Web Application Models
Searching Repositories of Web Application ModelsSearching Repositories of Web Application Models
Searching Repositories of Web Application Models
 
Iot ontologies state of art$$$
Iot ontologies state of art$$$Iot ontologies state of art$$$
Iot ontologies state of art$$$
 
GATE, HLT and Machine Learning, Sheffield, July 2003
GATE, HLT and Machine Learning, Sheffield, July 2003GATE, HLT and Machine Learning, Sheffield, July 2003
GATE, HLT and Machine Learning, Sheffield, July 2003
 
Modular Documentation Joe Gelb Techshoret 2009
Modular Documentation Joe Gelb Techshoret 2009Modular Documentation Joe Gelb Techshoret 2009
Modular Documentation Joe Gelb Techshoret 2009
 
Semantic Web in Action: Ontology-driven information search, integration and a...
Semantic Web in Action: Ontology-driven information search, integration and a...Semantic Web in Action: Ontology-driven information search, integration and a...
Semantic Web in Action: Ontology-driven information search, integration and a...
 
Understanding Information Architecture
Understanding Information ArchitectureUnderstanding Information Architecture
Understanding Information Architecture
 
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...
A N  E XTENSION OF  P ROTÉGÉ FOR AN AUTOMA TIC  F UZZY - O NTOLOGY BUILDING U...A N  E XTENSION OF  P ROTÉGÉ FOR AN AUTOMA TIC  F UZZY - O NTOLOGY BUILDING U...
A N E XTENSION OF P ROTÉGÉ FOR AN AUTOMA TIC F UZZY - O NTOLOGY BUILDING U...
 
Survey on article extraction and comment monitoring techniques
Survey on article extraction and comment monitoring techniquesSurvey on article extraction and comment monitoring techniques
Survey on article extraction and comment monitoring techniques
 
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
 
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT
 
Industry-Academia Communication In Empirical Software Engineering
Industry-Academia Communication In Empirical Software EngineeringIndustry-Academia Communication In Empirical Software Engineering
Industry-Academia Communication In Empirical Software Engineering
 
PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...
PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...
PROPOSAL OF AN HYBRID METHODOLOGY FOR ONTOLOGY DEVELOPMENT BY EXTENDING THE P...
 
DODDLE-OWL: A Domain Ontology Construction Tool with OWL
DODDLE-OWL: A Domain Ontology Construction Tool with OWLDODDLE-OWL: A Domain Ontology Construction Tool with OWL
DODDLE-OWL: A Domain Ontology Construction Tool with OWL
 
Software development effort reduction with Co-op
Software development effort reduction with Co-opSoftware development effort reduction with Co-op
Software development effort reduction with Co-op
 
Collaborative Modeling of Processes and Ontologies with MoKi
Collaborative Modeling of Processes and Ontologies with MoKiCollaborative Modeling of Processes and Ontologies with MoKi
Collaborative Modeling of Processes and Ontologies with MoKi
 
Cora For ITDG
Cora For ITDGCora For ITDG
Cora For ITDG
 
Ontology Engineering for Systems Engineering
Ontology Engineering for Systems EngineeringOntology Engineering for Systems Engineering
Ontology Engineering for Systems Engineering
 
Ju3517011704
Ju3517011704Ju3517011704
Ju3517011704
 

More from tmra

External Schema for Topic Map Database
External Schema for Topic Map DatabaseExternal Schema for Topic Map Database
External Schema for Topic Map Database
tmra
 
Weber 2010 brn
Weber 2010 brnWeber 2010 brn
Weber 2010 brn
tmra
 
Subject Headings make information to be topic maps
Subject Headings make information to be topic mapsSubject Headings make information to be topic maps
Subject Headings make information to be topic maps
tmra
 
Inquiry Optimization Technique for a Topic Map Database
Inquiry Optimization Technique for a Topic Map DatabaseInquiry Optimization Technique for a Topic Map Database
Inquiry Optimization Technique for a Topic Map Database
tmra
 
Topic Merge Scenarios for Knowledge Federation
Topic Merge Scenarios for Knowledge FederationTopic Merge Scenarios for Knowledge Federation
Topic Merge Scenarios for Knowledge Federation
tmra
 
JavaScript Topic Maps in server environments
JavaScript Topic Maps in server environmentsJavaScript Topic Maps in server environments
JavaScript Topic Maps in server environments
tmra
 
Modelling IMS QTI with Topic Maps
Modelling IMS QTI with Topic MapsModelling IMS QTI with Topic Maps
Modelling IMS QTI with Topic Maps
tmra
 
Hatana - Virtual Topic Map Merging
Hatana - Virtual Topic Map MergingHatana - Virtual Topic Map Merging
Hatana - Virtual Topic Map Merging
tmra
 
Designing a gui_description_language_with_topic_maps
Designing a gui_description_language_with_topic_mapsDesigning a gui_description_language_with_topic_maps
Designing a gui_description_language_with_topic_maps
tmra
 
Maiana - The social Topic Maps explorer
Maiana - The social Topic Maps explorerMaiana - The social Topic Maps explorer
Maiana - The social Topic Maps explorer
tmra
 
Tmra2010 matsuuraposter
Tmra2010 matsuuraposterTmra2010 matsuuraposter
Tmra2010 matsuuraposter
tmra
 
Automatic semantic interpretation of unstructured data for knowledge management
Automatic semantic interpretation of unstructured data for knowledge managementAutomatic semantic interpretation of unstructured data for knowledge management
Automatic semantic interpretation of unstructured data for knowledge management
tmra
 
Presentation final
Presentation finalPresentation final
Presentation final
tmra
 
Evaluation of Instances Asset in a Topic Maps-Based Ontology
Evaluation of Instances Asset in a Topic Maps-Based OntologyEvaluation of Instances Asset in a Topic Maps-Based Ontology
Evaluation of Instances Asset in a Topic Maps-Based Ontology
tmra
 
Defining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
Defining Domain-Specific Facets for Topic Maps With TMQL Path ExpressionsDefining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
Defining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
tmra
 
Mappe1
Mappe1Mappe1
Mappe1
tmra
 
Et Tu, Brute? Topic Maps and Discourse Semantics
Et Tu, Brute? Topic Maps and Discourse SemanticsEt Tu, Brute? Topic Maps and Discourse Semantics
Et Tu, Brute? Topic Maps and Discourse Semantics
tmra
 
Live Integration Framework
Live Integration FrameworkLive Integration Framework
Live Integration Framework
tmra
 
Hatana tmra 2010
Hatana tmra 2010Hatana tmra 2010
Hatana tmra 2010
tmra
 
Designing a GUI Description Language with Topic Maps
Designing a GUI Description Language with Topic MapsDesigning a GUI Description Language with Topic Maps
Designing a GUI Description Language with Topic Maps
tmra
 

More from tmra (20)

External Schema for Topic Map Database
External Schema for Topic Map DatabaseExternal Schema for Topic Map Database
External Schema for Topic Map Database
 
Weber 2010 brn
Weber 2010 brnWeber 2010 brn
Weber 2010 brn
 
Subject Headings make information to be topic maps
Subject Headings make information to be topic mapsSubject Headings make information to be topic maps
Subject Headings make information to be topic maps
 
Inquiry Optimization Technique for a Topic Map Database
Inquiry Optimization Technique for a Topic Map DatabaseInquiry Optimization Technique for a Topic Map Database
Inquiry Optimization Technique for a Topic Map Database
 
Topic Merge Scenarios for Knowledge Federation
Topic Merge Scenarios for Knowledge FederationTopic Merge Scenarios for Knowledge Federation
Topic Merge Scenarios for Knowledge Federation
 
JavaScript Topic Maps in server environments
JavaScript Topic Maps in server environmentsJavaScript Topic Maps in server environments
JavaScript Topic Maps in server environments
 
Modelling IMS QTI with Topic Maps
Modelling IMS QTI with Topic MapsModelling IMS QTI with Topic Maps
Modelling IMS QTI with Topic Maps
 
Hatana - Virtual Topic Map Merging
Hatana - Virtual Topic Map MergingHatana - Virtual Topic Map Merging
Hatana - Virtual Topic Map Merging
 
Designing a gui_description_language_with_topic_maps
Designing a gui_description_language_with_topic_mapsDesigning a gui_description_language_with_topic_maps
Designing a gui_description_language_with_topic_maps
 
Maiana - The social Topic Maps explorer
Maiana - The social Topic Maps explorerMaiana - The social Topic Maps explorer
Maiana - The social Topic Maps explorer
 
Tmra2010 matsuuraposter
Tmra2010 matsuuraposterTmra2010 matsuuraposter
Tmra2010 matsuuraposter
 
Automatic semantic interpretation of unstructured data for knowledge management
Automatic semantic interpretation of unstructured data for knowledge managementAutomatic semantic interpretation of unstructured data for knowledge management
Automatic semantic interpretation of unstructured data for knowledge management
 
Presentation final
Presentation finalPresentation final
Presentation final
 
Evaluation of Instances Asset in a Topic Maps-Based Ontology
Evaluation of Instances Asset in a Topic Maps-Based OntologyEvaluation of Instances Asset in a Topic Maps-Based Ontology
Evaluation of Instances Asset in a Topic Maps-Based Ontology
 
Defining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
Defining Domain-Specific Facets for Topic Maps With TMQL Path ExpressionsDefining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
Defining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
 
Mappe1
Mappe1Mappe1
Mappe1
 
Et Tu, Brute? Topic Maps and Discourse Semantics
Et Tu, Brute? Topic Maps and Discourse SemanticsEt Tu, Brute? Topic Maps and Discourse Semantics
Et Tu, Brute? Topic Maps and Discourse Semantics
 
Live Integration Framework
Live Integration FrameworkLive Integration Framework
Live Integration Framework
 
Hatana tmra 2010
Hatana tmra 2010Hatana tmra 2010
Hatana tmra 2010
 
Designing a GUI Description Language with Topic Maps
Designing a GUI Description Language with Topic MapsDesigning a GUI Description Language with Topic Maps
Designing a GUI Description Language with Topic Maps
 

Recently uploaded

How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
danishmna97
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Speck&Tech
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Tosin Akinosho
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Edge AI and Vision Alliance
 
Recommendation System using RAG Architecture
Recommendation System using RAG ArchitectureRecommendation System using RAG Architecture
Recommendation System using RAG Architecture
fredae14
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
Jason Packer
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
Jakub Marek
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
Zilliz
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
innovationoecd
 
OpenID AuthZEN Interop Read Out - Authorization
OpenID AuthZEN Interop Read Out - AuthorizationOpenID AuthZEN Interop Read Out - Authorization
OpenID AuthZEN Interop Read Out - Authorization
David Brossard
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
panagenda
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Jeffrey Haguewood
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Matthew Sinclair
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
Ivanti
 
How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
Daiki Mogmet Ito
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Tomaz Bratanic
 

Recently uploaded (20)

How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
 
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
 
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdfMonitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
 
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
 
Recommendation System using RAG Architecture
Recommendation System using RAG ArchitectureRecommendation System using RAG Architecture
Recommendation System using RAG Architecture
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays - June 2024
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 
Fueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte WebinarFueling AI with Great Data with Airbyte Webinar
Fueling AI with Great Data with Airbyte Webinar
 
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of GermanyPresentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
 
OpenID AuthZEN Interop Read Out - Authorization
OpenID AuthZEN Interop Read Out - AuthorizationOpenID AuthZEN Interop Read Out - Authorization
OpenID AuthZEN Interop Read Out - Authorization
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
 
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 202420240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
 
How to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For FlutterHow to use Firebase Data Connect For Flutter
How to use Firebase Data Connect For Flutter
 
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracyGraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
 

Building and Integrating Competitive Intelligence Reports Using the Topic Map Technology

  • 1. Building and Integrating Competitive IntelligenceReports Using the Topic Map Technology Vojtěch Svátek, Tomáš Kliegr, Jan Nemrava, Martin Ralbovsý, Vojtěch Roček ,Jan Rauch University of Economics, Winston Churchill Sq. 4, Prague, Czech Republic Jiří Šplíchal, Tomáš Vejlupek Tovek s.r.o., Chrudimská 1418, Prague, Czech Republic
  • 2. CI and Business Clusters CI – Competitive Intelligence is a sub-field of business intelligence that supports decision makers in understanding the competitive environment by means of reports prepared based on (public) resources. Cluster is a set of companies in related fields operating in the same geographical area How to link and search multiple CI reports? Envisaged Solution: Create a complementary topic map that would put the important facts into context
  • 3. TheTopic Map 1] Ontology: putting concepts into context Instances Associations TopicTypes 2] Annotate important bits of text with ontology concepts
  • 4.
  • 5. S1: Individual ontologies, merge Each team wrote the CI report (in a text editor) Consequently, they obtained a copy of a startup ontology Students extended the ontology with new topic types using Tovek Topic Mapper (TTM): an ontology editor and annotating tool (desktop application) Students used TTM to annotate bits of text with a topic type. Annotated text became an internal occurrence in the topic map The ontologies enriched with new topic types and annotations were collected from all teams We used OKS to merge the topic maps Extend ontology Annotate DOC HTML The result is a linking file between the document and the shared topic map XTM Startup Ontology Result is a linking file conneting document with the topic map
  • 6. Topic Maps Merging Merging of: Business cluster topic map, All unstructured documents, Linking files Linking files CI reports HTML XTM DOC Shared industry topic map
  • 7. Issues Annotated text fragmented, since each fragment is stored as internal occurrence Laborious Duplicate topic types Effective merging requires unique identifiers, which was achieved only for companies (registration numbers used in subject indicators)
  • 8. S2: Collaborative Ontology Population Goal: remove duplicate topic types Startup ontology was placed on a PostgreSQL server Student teams collaboratively enriched the ontology with topic types, association types and occurrence types they assumed to use during the annotation in Topic Mapper The ontology was then frozen: each team got its copy. TTM was used only for annotation, and then OKS for merging Collaborative Ontology Creation remote repository Topic Maps for Merging Import ontology Shared topic map students Annotate only
  • 9. Issues Separation of ontology enrichment and document annotation is not natural and requires an experienced annotator Annotations still kept as internal occurrences Multiple concurrent instances of OKS servers resulted in corruption in the topic map, probably due to caching in OKS Two topic map tools used, original documents not easily accessible
  • 10. S3: Annotation by linking Goal: move annotation fully to the web All students used one instance of OKS server CI reports were placed into a CMS (Joomla!) Each structural unit was assigned an id (via HTML’s <a name>) Annotation was done via external occurrences External occurrences point at a specific bookmark at the document, where the annotated fragment starts. The annotated fragment is assumed to span up to the nearest following bookmark.
  • 11. Issues … and finally advantages Issues: OKS Ontopoly was not stable enough in concurrent setting X-Pointer technology, which could be used to mark spans in the document, is not supported by current browsers Advantages: The text with full content (including even figures or links) in the CMS is more intelligible than fragments in internal occurrences Further editing of an article is possible in the CMS without invalidating the annotation Full-text search feature of the CMS can be exploited Bringing the best from the CMS world and OKS
  • 12. Summary& Plans On the competitive intelligence use case, we tested several approaches for collaborative ontology design and document annotation with some 500 users altogether. OKS is a great tool, which gets additional edge by being web-based We deem the last approach taken: documents stored in a CMS linked through external occurrences with OKS as usable - contingent on improvements in Ontopoly and Joomla! Ontopoly wishes Greater stability in case of concurrent user access We missed user management and versioning in Ontopoly Joomla! wishes Support for „tagging“ arbitrary bits of text A tool for creating XPointer URLs based on user selection A functionality that would highlight part of the document based on a URL containing XPointer span