Topic Maps as a Virtual Observatory tool

One major component of the VO will be catalogs measuring gigabytes and terrabytes if not more. Some mechanism like XML will be used for structuring the information. However, such mechanisms are not good for information retrieval on their own. For retrieval we use queries. Topic Maps that have started becoming popular recently are excellent for segregating information that results from a query. A Topic Map is a structured network of hyperlinks above an information pool. Different Topic Maps can form different layers above the same information pool and provide us with different views of it. This facilitates in being able to ask exact questions, aiding us in looking for gold needles in the proverbial haystack. Here we discuss the specifics of what Topic Maps are and how they can be implemented within the VO framework. URL: http://www.astro.caltech.edu/~aam/science/topicmaps/Comment: 11 pages, 5 eps figures, to appear in SPIE Annual Meeting 2001 proceedings (Astronomical Data Analysis), uses spie.st

Topic Maps uprooted

Topic Maps is an International Standard, aiming at a “notation for interchangeably representing information about the structure of information resources.â€[4] An informal introduction by S. Pepper goes beyond an orientation at resources, traditionally called documentation. He presents Topic Maps as a “standard for describing knowledge structures and associating them with information resources.†He holds it up as “the Global Positioning System of the information universe,†as Topic Maps permits “to encode arbitrarily complex knowledge structures.â€[9] Topic Maps, is it really the silver bullet for the information society? This paper conducts a critical appraisal. My review is not comprehensive, though. I’ve especially attempted a thorough analysis of conceptual foundations, with a special concern for standardized rules and recommendations for map construction. As theory, or metamodel, in the final section Topic Maps is also briefly compared to Metapattern which is the author’s design for controlling requisite variety in information modeling. Some references on both Topic Maps and Metapattern are included at the end of this paper

Topic Maps and TEI - Using Topic Maps as a Tool for Presenting TEI Documents

This paper describes a method used by the website of the New Zealand Electronic Text Centre (NZETC), in which Topic Maps are used as a tool for presenting TEI-encoded texts in HTML form. Many electronic text archives transform their TEI texts into HTML for publishing their texts on the World Wide Web. Typically each chapter or page is transformed from TEI into a separate web page. Such a method produces websites that have the same structure as a physical book. However, TEI is more expressive than HTML and can encode many other features of interest than just chapters, pages, and paragraphs. For example, TEI is also used to encode information about people and places and events, as well as literary criticism, and linguistic analysis. Indeed, TEI is designed to be extended to suit all kinds of scholarly needs. These more complex aspects of text encoding are more difficult to transform into HTML. Because TEI is designed to be convenient for scholars to encode complex information, rather than for readers to understand it, it is necessary to transform the TEI into another form suitable for display. For instance, where a TEI corpus includes references to people, these references might be collated together to produce an index. For practical purposes, it is often necessary to extract information from TEI into a database, so that it can be queried conveniently and transformed into a web site. The new "Topic Map" standard of the International Standards Organisation is identified as a suitable technology for solving this problem. A topic map is a kind of Web database with an extremely flexible structure. This paper describes a framework for using TEI in conjunction with Topic Maps to produce a large website which can be navigated easily in many directions

Topic maps applied to PubMed

This paper presents a topic map approach to PubMed in order to create a knowledge representation for this information system. PubMed is a free search engine that gives very full coverage of the related biomedical sciences. With more than 17 millions of citations since 1865, PubMed users have several problems to find the papers desired. So, it is necessary to organize these concepts in a semantic network. To achieve this objective, we use the Metamorphosis system, choosing the keywords from MeSH ontology. This way, we obtain an ontological index for PubMed, making easier to find specific papers.(undefined

Supporting Information Visualization Through Topic Maps

We are living a phenomenon of accelerated information production with different accessing sources. Hence users are faced with a growing problem: accessing (navigation) and filtering specific information contained in large datasets, which are increasing in size. Procedures such as data filtering and gathering are now simplified through a new concept known as Topic Maps. The application of Virtual Reality technologies enables to present and interact with multidimensional information in a 3D space. In this paper we present INSPHERE, a new visual metaphor for information visualization, based on both, Virtual Reality techniques, and “geographical information maps” provided by Topic Maps

Comparing topic maps constraint specification languages

Topic Map Constraint Language (TMCL) provides a means to express constraints on topic maps conforming to ISO/IEC 13250. In this article, we will use a test suite and show, step-by-step, the way we handled several kinds of Topic Maps constraints in many different instances in order to answer questions like: Do they do the same job? Are there some kinds of Topic Maps constraints that are easier to specify with one of them? Do you need different background to use the tools? Is it possible to use them in similar situations (the same topic maps instances)? May we use them to produce an equal result? How do AsTMa!, OSL, Toma, and XTche relate to Topic Maps Constraint Language (TMCL)? What kind of constraints each one of these three can not specify? We will conclude this paper with a summary of the comparisons accomplished between those Topic Maps constraint languages over the use case proposed

Oveia: expanding the topic maps frontier

Ontology based websites are one possible implementation of the Semantic Web. There are several languages for ontology specification: RDF, OWL, Topic Maps. Topic Maps follow a structure formally specified what makes them a good choice for semantic website specification. The process of ontology development based in topic maps is complex, time consuming, and it requires a lot of human and financial resources, because they can have a lot of topics and associations, and the number of information resources can be very large. To overcome this problem a new environment is proposed, Oveia. Oveia is composed by four components which have relevant contributions to the Semantic Web area. This paper describes these components in detail. Two components representing a metadata extractor: heterogeneous data integration (through XSDS specifications) and an homogeneous intermediate data representation for the extracted metadata (datasets). The Ontology builder who builds an ontology from metadata stored in a set of datasets (construction rules are specified in a new domain specific language: XS4TM). The Ontology builder stores the result in XTM files or in relational databases according to the Topic Map structure. Finally, Ulisses, the navigational component, generates web interfaces through which is possible to move inside the topic map and among information resources

Navigating through archives, libraries and museums: Topic Maps as a harmonizing instrument

The paper deals with the possibility of creating a topic map based system where different sectors of cultural heritage would interact with users, by monitoring the navigation histories of users and the statistics on the searches, in order to authorize variant form of names. The problem of managing different sectors and harmonizing them both from a structural and a semantic view point, by using Topic Maps, is also discussed. With regards to this, we are introducing two projects, which are largely based on the above mention use of Topic Maps. The original publication is available at www.springerlink.com http://www.springerlink.com/content/6k5473124678k452/fulltext.pd

Document Clustering based on Topic Maps

Importance of document clustering is now widely acknowledged by researchers for better management, smart navigation, efficient filtering, and concise summarization of large collection of documents like World Wide Web (WWW). The next challenge lies in semantically performing clustering based on the semantic contents of the document. The problem of document clustering has two main components: (1) to represent the document in such a form that inherently captures semantics of the text. This may also help to reduce dimensionality of the document, and (2) to define a similarity measure based on the semantic representation such that it assigns higher numerical values to document pairs which have higher semantic relationship. Feature space of the documents can be very challenging for document clustering. A document may contain multiple topics, it may contain a large set of class-independent general-words, and a handful class-specific core-words. With these features in mind, traditional agglomerative clustering algorithms, which are based on either Document Vector model (DVM) or Suffix Tree model (STC), are less efficient in producing results with high cluster quality. This paper introduces a new approach for document clustering based on the Topic Map representation of the documents. The document is being transformed into a compact form. A similarity measure is proposed based upon the inferred information through topic maps data and structures. The suggested method is implemented using agglomerative hierarchal clustering and tested on standard Information retrieval (IR) datasets. The comparative experiment reveals that the proposed approach is effective in improving the cluster quality

SesameTM: Building Topic Maps on RDF

Over the past decade RDF has developed to become the dominant standard for representation and interchange of structured data on the web. In portal development, widely unrecognized by Semantic Web research, subject-centric topic maps are actively used and have evolved from an ancient SGML and intermittent XML-based standard to a pure data model. This data model can be represented as a graph and served various integration strategies, put forward over the past years, as a starting point. However, none of these strategies really appreciates the way in which the technologies are used resulting in a poor tool interoperability. To overcome this state we propose a Topic Maps engine acting as congurable wrapper for Sesame. The software library we develop and describe in this paper implements the Topic Maps Application Programming Interface (TMAPI) enabling the usage of Topic Maps infrastructure instead of working at the level of RDF triples