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tmraPresentation for the open space session of TMRA 2008, International Conference on Topic Maps Research and Applications, Leipzig, Germany.
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This paper reports the efforts to make topic maps from Subject Headings (SHs) and discuss practical use of them for organizing information and knowledge. SHs are often maintained by libraries and used in bibliographic records. SHs are thesauri and they are well organized. Fortunately some SHs are published on the Web. We transformed them to topic maps. Usually each subject in SHs has own ID. It can play PSI role. By keeping the relationships included in SHs such as Broader-Narrower, Related, USE-UF etc in topic maps, information or knowledge can be linked together and organized according to the structure of SHs. In other words, by using SHs information and knowledge can be topic maps easily.
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This document proposes an inquiry optimization technique for topic map databases. It discusses using an object-oriented data model for topic map databases to improve query performance compared to a relational model. The document defines cost estimation formulas to help the database system select the optimal retrieval route, either following associations or searching by topic, when answering queries. An experiment is needed to evaluate the effectiveness of using these cost estimations to optimize queries of a topic map database.
Topic Merge Scenarios for Knowledge Federation
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JavaScript Topic Maps in server environments
1. The document discusses using the tmjs Topic Maps engine, written in JavaScript, for server-side applications like a PSI server.
2. Tmjs allows full Topic Maps processing in JavaScript and can operate on servers via Node.js.
3. A sample PSI server application is shown that uses tmjs and Node.js to serve Topic Map-based information about subjects from an HTTP request.
Modelling IMS QTI with Topic Maps
This document discusses modeling QTI (IMS Question and Test Interoperability) assessments in topic maps. QTI is used to share assessment content between systems but has changing specifications that are challenging to support. Embedding QTI questions and responses as topics within a topic map allows the content to be richer than QTI and supports generating QTI output. An example shows embedding gaps and sounds within a fill-in-the-blank question topic. Authoring tools can generically edit embedded topics. This technique is useful for other content like images, links, and videos. In conclusion, embedding topics solved their needs and is used extensively in their production systems.
Hatana - Virtual Topic Map Merging
The document discusses Hatana, a virtual merging engine that creates a unified view of information from multiple data sources by merging them on demand according to Topic Map standards. Hatana behaves like a topic map layer over the underlying sources, merging topics, associations, and other constructs virtually based on equality rules while maintaining the original sources. This allows related information to be queried and browsed together seamlessly.
Designing a gui_description_language_with_topic_maps
The document proposes a GUI Description Language (GDL) that uses Topic Maps to generate configurable and domain-specific user interfaces. GDL aims to simplify Topic Maps for end users by defining default values, restricting actions, and automatically generating identifiers and layouts corresponding to the semantic meaning of the data domain. However, GDL also inserts an additional layer of processing between the user and the Topic Map engine. The document discusses the goals and features of GDL, and concludes that GDL can bridge users and Topic Map internals without limiting the ontology, while allowing customizable but not hard-coded user interfaces.
Maiana - The social Topic Maps explorer
Maiana is a platform for structured data developed by Lutz Maicher and Uta Schulze at the University of Leipzig. It allows users to manage, browse, query, and validate topic maps. Maiana is social in that it enables users to discuss resources, observe data sources, and follow other users. Data sources on Maiana can be kept private or shared publicly. The platform also includes an API and semantic search capabilities.
Tmra2010 matsuuraposter
1. The document proposes using the Nintendo Wii Remote as an intuitive interface for interacting with web-based learning content, such as a topic map-based science learning website.
2. Specifically, it describes using the Wii Remote as a pointer for real-world interactions like selecting constellations, and as a navigation device for exploring 3D representations and the structure of the topic map.
3. Motions and buttons on the Wii Remote are mapped to navigating different aspects of the topic map and triggering content from the website in an immersive way, allowing students to intuitively explore related science topics.
Automatic semantic interpretation of unstructured data for knowledge management
The document summarizes a demo of an automatic semantic analysis technique for knowledge discovery from unstructured data like Wikipedia articles. The demo shows a linked concept graph and linked data graph created by analyzing astronomy articles. It also discusses how the technique can be used for knowledge representation, discovery, navigation, and intelligence by linking isolated data and deriving a taxonomy. The technical solution takes a bottom-up approach using semantic data integration and analysis to dynamically create and update object and concept graphs in real-time from various data sources.
Putting topic maps to rest.tmra2010
The document discusses putting Topic Maps to REST. It describes existing Topic Map APIs and their limitations. It then introduces Tropics, a proposed RESTful API for Topic Maps. Tropics would support resources like topics, associations, and search results. It advocates the HATEOAS principle to structure navigation between resources. The document outlines Tropics' proposed URI structure and status of implementation.
Evaluation of Instances Asset in a Topic Maps-Based Ontology
The document discusses evaluating the information asset of topics in a topic maps ontology. It describes assigning partial weights to topics based on attribute richness and total weights based on surrounding topic descriptions. The user can set attribute weights and weights for three categories of associations. Normalizing total topic weights results in information asset values that can be used to rank search results based on usefulness to the user.
Defining Domain-Specific Facets for Topic Maps With TMQL Path Expressions
The automatic generation of facets works fairly bad for fine-modeled ontologies, in which not all information concerning a single Topic is available through occurrences and direct associations. In this paper, we share our conception of using TMQL path expressions for the definition of domain-specific facets by means of using standard-based Topic Maps technologies. The generated facets must be evaluated, even though they are defined manually by a domain expert. We therefore propose metrics for automatic evaluation of the defined facets, as well as a mechanism for using automatically stored user feedback.
Mappe1
The document outlines the schedule for a two-day Topic Maps tutorial. Day one includes talks on using Topic Maps for discourse semantics, developing ontologies and facet definitions, and Topic Maps tools and applications. Day two covers semantic integration approaches, integrating Topic Maps with content management systems, interpreting unstructured data, merging topic maps, and modeling learning standards. A poster session is also included on using the Wii remote for an educational website.
Et Tu, Brute? Topic Maps and Discourse Semantics
This paper proposes how to propositional, grammatical, modal and textual meaning may be encoded using a layered approach.
A PHP library for Ontopia-CMS Integration
This document summarizes a PHP library called KBI Library that allows integration between PHP content management systems (CMS) and knowledge bases. The library acts as an information broker between the CMS and knowledge bases, enabling presentation of knowledge contained in knowledge bases through the CMS. It features a generic implementation to support standard operations and specific implementations for Ontopia knowledge bases. It also includes administration and editor interfaces for Joomla to manage remote sources and queries.
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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 ...
Subject Headings make information to be topic maps
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Dense Topic Maps
- 1. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 Dense Topic Maps Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 1 of 7
- 2. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 Use Case: Annotate DNA Lifescience analyzes DNA produces multiple gigabytes per day for each base, following information is produced: type: Is it A,C,G,T? probability: How probable is that it is what we believe it is? Current custom format: 5 bytes per DNA base type: 1 byte probability: 4 bytes Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 2 of 7
- 3. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 as Topic Maps How to represent a DNA base which is a Thymine base with a probability of 99.6710% is at offset 938457 as XTM? CTM? Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 3 of 7
- 4. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 as XTM 2.0 <topic id=quot;base938457quot;> <occurrence> <type><topicRef href=quot;#offsetquot;/></type> <resourceData datatype=quot;http://www.w3.org/2001/XMLSchema#integerquot;>938457</resourceData> </occurrence> </topic> <association reifier=quot;#base938457isaTquot;> <type><topicRef href=quot;#type-instancequot;/></type> <role> <type><topicRef href=quot;#typequot;/></type> <topicRef href=quot;#Tquot;/> </role> <role> <type><topicRef href=quot;#instancequot;/></type> <topicRef href=quot;#base938457quot;/> </role> </association> <topic id=quot;base938457isaTquot;> <occurrence> <type><topicRef href=quot;#probabilityquot;/></type> <resourceData datatype=quot;http://www.w3.org/2001/XMLSchema#decimalquot;>0.996710</resourceData> </occurrence> </topic> 683 bytes per DNA base bloat factor: 136.6 Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 4 of 7
- 5. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 as CTM base938457 base_info(T,0.996710,938457). 42 bytes per DNA base bloat factor: 8.4 FYI: CTM template def base_info($base,$basetype,$probability,$offset) $base offset: $offset. type-instance(type: $basetype,instance: $base) ~ [ probability: $probability ] end Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 5 of 7
- 6. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 as DTM 0x543f7f2863 0x54 = quot;Tquot; 0x3f7f2863 = 0.996710 5 bytes per DNA base bloat factor: 1.0 How is this a Topic Map? Define a Dense Topic Map Format specification for a particular dense format Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 6 of 7
- 7. TMRA 2008: OpenSpace session: Dense Topic Maps 2008-10-17 Why? good-to-perfect compression allows a migration path from many custom data formats to Topic Maps allows a (limited) migration path to many custom data formats from Topic Maps Xuân Baldauf <xuan--dtm--2008--tmra.de@baldauf.org> 7 of 7