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  1. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.26 
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    Abstract
    On a scientific concept hierarchy, a parent concept may have a few attributes, each of which has multiple values being a group of child concepts. We call these attributes facets: classification has a few facets such as application (e.g., face recognition), model (e.g., svm, knn), and metric (e.g., precision). In this work, we aim at building faceted concept hierarchies from scientific literature. Hierarchy construction methods heavily rely on hypernym detection, however, the faceted relations are parent-to-child links but the hypernym relation is a multi-hop, i.e., ancestor-to-descendent link with a specific facet "type-of". We use information extraction techniques to find synonyms, sibling concepts, and ancestor-descendent relations from a data science corpus. And we propose a hierarchy growth algorithm to infer the parent-child links from the three types of relationships. It resolves conflicts by maintaining the acyclic structure of a hierarchy.
    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  2. Engels, R.H.P.; Lech, T.Ch.: Generating ontologies for the Semantic Web : OntoBuilder (2004) 0.03 
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    Abstract
    Significant progress has been made in technologies for publishing and distributing knowledge and information on the web. However, much of the published information is not organized, and it is hard to find answers to questions that require more than a keyword search. In general, one can say that the web is organizing itself. Information is often published in relatively ad hoc fashion. Typically, concern about the presentation of content has been limited to purely layout issues. This, combined with the fact that the representation language used on the World Wide Web (HTML) is mainly format-oriented, makes publishing on the WWW easy, giving it an enormous expressiveness. People add private, educational or organizational content to the web that is of an immensely diverse nature. Content on the web is growing closer to a real universal knowledge base, with one problem relatively undefined; the problem of the interpretation of its contents. Although widely acknowledged for its general and universal advantages, the increasing popularity of the web also shows us some major drawbacks. The developments of the information content on the web during the last year alone, clearly indicates the need for some changes. Perhaps one of the most significant problems with the web as a distributed information system is the difficulty of finding and comparing information.
    Thus, there is a clear need for the web to become more semantic. The aim of introducing semantics into the web is to enhance the precision of search, but also enable the use of logical reasoning on web contents in order to answer queries. The CORPORUM OntoBuilder toolset is developed specifically for this task. It consists of a set of applications that can fulfil a variety of tasks, either as stand-alone tools, or augmenting each other. Important tasks that are dealt with by CORPORUM are related to document and information retrieval (find relevant documents, or support the user finding them), as well as information extraction (building a knowledge base from web documents to answer queries), information dissemination (summarizing strategies and information visualization), and automated document classification strategies. First versions of the toolset are encouraging in that they show large potential as a supportive technology for building up the Semantic Web. In this chapter, methods for transforming the current web into a semantic web are discussed, as well as a technical solution that can perform this task: the CORPORUM tool set. First, the toolset is introduced; followed by some pragmatic issues relating to the approach; then there will be a short overview of the theory in relation to CognIT's vision; and finally, a discussion on some of the applications that arose from the project.
    Source
    Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
    Theme
    Semantic Web
  3. Saruladha, K.; Aghila, G.; Penchala, S.K.: Design of new indexing techniques based on ontology for information retrieval systems (2010) 0.03 
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    Abstract
    Information Retrieval [IR] is the science of searching for documents, for information within documents, and for metadata about documents, as well as that of searching relational databases and the World Wide Web. This paper describes a document representation method instead of keywords ontological descriptors. The purpose of this paper is to propose a system for content-based querying of texts based on the availability of ontology for the concepts in the text domain and to develop new Indexing methods to improve RSV (Retrieval status value). There is a need for querying ontologies at various granularities to retrieve information from various sources to suit the requirements of Semantic web, to eradicate the mismatch between user request and response from the Information Retrieval system. Most of the search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. The indexes do not contain synonyms, cannot differentiate between homonyms and users receive different search results when they use different conjugation forms of the same word.
    Series
    Communications in computer and information science; vol.101
    Source
    Information and communication technologies: international conference; proceedings / ICT 2010, Kochi, Kerala, India, September 7 - 9, 2010. Ed.: V.V. Das
  4. Kiryakov, A.; Popov, B.; Terziev, I.; Manov, D.; Ognyanoff, D.: Semantic annotation, indexing, and retrieval (2004) 0.03 
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    Abstract
    The Semantic Web realization depends on the availability of a critical mass of metadata for the web content, associated with the respective formal knowledge about the world. We claim that the Semantic Web, at its current stage of development, is in a state of a critical need of metadata generation and usage schemata that are specific, well-defined and easy to understand. This paper introduces our vision for a holistic architecture for semantic annotation, indexing, and retrieval of documents with regard to extensive semantic repositories. A system (called KIM), implementing this concept, is presented in brief and it is used for the purposes of evaluation and demonstration. A particular schema for semantic annotation with respect to real-world entities is proposed. The underlying philosophy is that a practical semantic annotation is impossible without some particular knowledge modelling commitments. Our understanding is that a system for such semantic annotation should be based upon a simple model of real-world entity classes, complemented with extensive instance knowledge. To ensure the efficiency, ease of sharing, and reusability of the metadata, we introduce an upper-level ontology (of about 250 classes and 100 properties), which starts with some basic philosophical distinctions and then goes down to the most common entity types (people, companies, cities, etc.). Thus it encodes many of the domain-independent commonsense concepts and allows straightforward domain-specific extensions. On the basis of the ontology, a large-scale knowledge base of entity descriptions is bootstrapped, and further extended and maintained. Currently, the knowledge bases usually scales between 105 and 106 descriptions. Finally, this paper presents a semantically enhanced information extraction system, which provides automatic semantic annotation with references to classes in the ontology and to instances. The system has been running over a continuously growing document collection (currently about 0.5 million news articles), so it has been under constant testing and evaluation for some time now. On the basis of these semantic annotations, we perform semantic based indexing and retrieval where users can mix traditional information retrieval (IR) queries and ontology-based ones. We argue that such large-scale, fully automatic methods are essential for the transformation of the current largely textual web into a Semantic Web.
    Source
    Web semantics: science, services and agents on the World Wide Web. 2(2004) no.1, S.49-79
    Theme
    Semantic Web
  5. Scheir, P.; Pammer, V.; Lindstaedt, S.N.: Information retrieval on the Semantic Web : does it exist? (2007) 0.03 
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    Abstract
    Plenty of contemporary attempts to search exist that are associated with the area of Semantic Web. But which of them qualify as information retrieval for the Semantic Web? Do such approaches exist? To answer these questions we take a look at the nature of the Semantic Web and Semantic Desktop and at definitions for information and data retrieval. We survey current approaches referred to by their authors as information retrieval for the Semantic Web or that use Semantic Web technology for search.
    Source
    Lernen - Wissen - Adaption : workshop proceedings / LWA 2007, Halle, September 2007. Martin Luther University Halle-Wittenberg, Institute for Informatics, Databases and Information Systems. Hrsg.: Alexander Hinneburg
    Theme
    Semantic Web
  6. Fernández, M.; Cantador, I.; López, V.; Vallet, D.; Castells, P.; Motta, E.: Semantically enhanced Information Retrieval : an ontology-based approach (2011) 0.03 
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    Abstract
    Currently, techniques for content description and query processing in Information Retrieval (IR) are based on keywords, and therefore provide limited capabilities to capture the conceptualizations associated with user needs and contents. Aiming to solve the limitations of keyword-based models, the idea of conceptual search, understood as searching by meanings rather than literal strings, has been the focus of a wide body of research in the IR field. More recently, it has been used as a prototypical scenario (or even envisioned as a potential "killer app") in the Semantic Web (SW) vision, since its emergence in the late nineties. However, current approaches to semantic search developed in the SW area have not yet taken full advantage of the acquired knowledge, accumulated experience, and technological sophistication achieved through several decades of work in the IR field. Starting from this position, this work investigates the definition of an ontology-based IR model, oriented to the exploitation of domain Knowledge Bases to support semantic search capabilities in large document repositories, stressing on the one hand the use of fully fledged ontologies in the semantic-based perspective, and on the other hand the consideration of unstructured content as the target search space. The major contribution of this work is an innovative, comprehensive semantic search model, which extends the classic IR model, addresses the challenges of the massive and heterogeneous Web environment, and integrates the benefits of both keyword and semantic-based search. Additional contributions include: an innovative rank fusion technique that minimizes the undesired effects of knowledge sparseness on the yet juvenile SW, and the creation of a large-scale evaluation benchmark, based on TREC IR evaluation standards, which allows a rigorous comparison between IR and SW approaches. Conducted experiments show that our semantic search model obtained comparable and better performance results (in terms of MAP and P@10 values) than the best TREC automatic system.
    Source
    Web semantics: science, services and agents on the World Wide Web. 9(2011) no.4, S.434-452
    Theme
    Semantic Web
  7. Zhitomirsky-Geffet, M.; Bar-Ilan, J.: Towards maximal unification of semantically diverse ontologies for controversial domains (2014) 0.02 
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    Abstract
    Purpose - Ontologies are prone to wide semantic variability due to subjective points of view of their composers. The purpose of this paper is to propose a new approach for maximal unification of diverse ontologies for controversial domains by their relations. Design/methodology/approach - Effective matching or unification of multiple ontologies for a specific domain is crucial for the success of many semantic web applications, such as semantic information retrieval and organization, document tagging, summarization and search. To this end, numerous automatic and semi-automatic techniques were proposed in the past decade that attempt to identify similar entities, mostly classes, in diverse ontologies for similar domains. Apparently, matching individual entities cannot result in full integration of ontologies' semantics without matching their inter-relations with all other-related classes (and instances). However, semantic matching of ontological relations still constitutes a major research challenge. Therefore, in this paper the authors propose a new paradigm for assessment of maximal possible matching and unification of ontological relations. To this end, several unification rules for ontological relations were devised based on ontological reference rules, and lexical and textual entailment. These rules were semi-automatically implemented to extend a given ontology with semantically matching relations from another ontology for a similar domain. Then, the ontologies were unified through these similar pairs of relations. The authors observe that these rules can be also facilitated to reveal the contradictory relations in different ontologies. Findings - To assess the feasibility of the approach two experiments were conducted with different sets of multiple personal ontologies on controversial domains constructed by trained subjects. The results for about 50 distinct ontology pairs demonstrate a good potential of the methodology for increasing inter-ontology agreement. Furthermore, the authors show that the presented methodology can lead to a complete unification of multiple semantically heterogeneous ontologies. Research limitations/implications - This is a conceptual study that presents a new approach for semantic unification of ontologies by a devised set of rules along with the initial experimental evidence of its feasibility and effectiveness. However, this methodology has to be fully automatically implemented and tested on a larger dataset in future research. Practical implications - This result has implication for semantic search, since a richer ontology, comprised of multiple aspects and viewpoints of the domain of knowledge, enhances discoverability and improves search results. Originality/value - To the best of the knowledge, this is the first study to examine and assess the maximal level of semantic relation-based ontology unification.
    Date
    20. 1.2015 18:30:22
    Source
    Aslib journal of information management. 66(2014) no.5, S.494-518
    Theme
    Semantic Web
  8. Wang, H.; Liu, Q.; Penin, T.; Fu, L.; Zhang, L.; Tran, T.; Yu, Y.; Pan, Y.: Semplore: a scalable IR approach to search the Web of Data (2009) 0.02 
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    Abstract
    The Web of Data keeps growing rapidly. However, the full exploitation of this large amount of structured data faces numerous challenges like usability, scalability, imprecise information needs and data change. We present Semplore, an IR-based system that aims at addressing these issues. Semplore supports intuitive faceted search and complex queries both on text and structured data. It combines imprecise keyword search and precise structured query in a unified ranking scheme. Scalable query processing is supported by leveraging inverted indexes traditionally used in IR systems. This is combined with a novel block-based index structure to support efficient index update when data changes. The experimental results show that Semplore is an efficient and effective system for searching the Web of Data and can be used as a basic infrastructure for Web-scale Semantic Web search engines.
    Source
    Web semantics: science, services and agents on the World Wide Web. 7(2009) no.3, S.177-188
    Theme
    Semantic Web
  9. Teskey, F.N.: Enriched knowledge representation for information retrieval (1987) 0.02 
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    Abstract
    In this paper we identify the need for a new theory of information. An information model is developed which distinguishes between data, as directly observable facts, information, as structured collections of data, and knowledge as methods of using information. The model is intended to support a wide range of information systems. In the paper we develop the use of the model for a semantic information retrieval system using the concept of semantic categories. The likely benefits of this area discussed, though as yet no detailed evaluation has been conducted.
    Source
    SIGIR'87: Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
  10. Das, S.; Roy, S.: Faceted ontological model for brain tumour study (2016) 0.02 
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    Abstract
    The purpose of this work is to develop an ontology-based framework for developing an information retrieval system to cater to specific queries of users. For creating such an ontology, information was obtained from a wide range of information sources involved with brain tumour study and research. The information thus obtained was compiled and analysed to provide a standard, reliable and relevant information base to aid our proposed system. Facet-based methodology has been used for ontology formalization for quite some time. Ontology formalization involves different steps such as identification of the terminology, analysis, synthesis, standardization and ordering. A vast majority of the ontologies being developed nowadays lack flexibility. This becomes a formidable constraint when it comes to interoperability. We found that a facet-based method provides a distinct guideline for the development of a robust and flexible model concerning the domain of brain tumours. Our attempt has been to bridge library and information science and computer science, which itself involved an experimental approach. It was discovered that a faceted approach is really enduring, as it helps in the achievement of properties like navigation, exploration and faceted browsing. Computer-based brain tumour ontology supports the work of researchers towards gathering information on brain tumour research and allows users across the world to intelligently access new scientific information quickly and efficiently.
    Date
    12. 3.2016 13:21:22
  11. Zhang, L.; Liu, Q.L.; Zhang, J.; Wang, H.F.; Pan, Y.; Yu, Y.: Semplore: an IR approach to scalable hybrid query of Semantic Web data (2007) 0.02 
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    Abstract
    As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we briefy describe how Semplore is used for searching Wikipedia and an IBM customer's product information.
    Source
    Proceeding ISWC'07/ASWC'07 : Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference. Ed.: K. Aberer et al
    Theme
    Semantic Web
  12. Broekstra, J.; Kampman, A.; Harmelen, F. van: Sesame: a generic architecture for storing and querying RDF and RDF schema (2004) 0.02 
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    Abstract
    The resource description framework (RDF) is a W3C recommendation for the formulation of meta-data on the World Wide Web. RDF Schema (RDFS) extends this standard with the means to specify domain vocabulary and object structures. These techniques will enable the enrichment of the Web with machine-processable semantics, thus giving rise to what has been dubbed the Semantic Web. We have developed Sesame, an architecture for storage and querying of RDF and RDFS information. Sesame allows persistent storage of RDF data and schema information, and provides access methods to that information through export and querying modules. It features ways of caching information and offers support for concurrency control. This chapter is organized as follows: In Section 5.2 we discuss why a query language specifically tailored to RDF and RDFS is needed, over and above existing query languages such as XQuery. In Section 5.3 we look at Sesame's modular architecture in some detail. In Section 5.4 we give an overview of the SAIL API and a brief comparison to other RDF API approaches. Section 5.5 discusses our experiences with Sesame to date, and Section 5.6 looks into possible future developments. Finally, we provide our conclusions in Section 5.7.
    Source
    Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
    Theme
    Semantic Web
  13. Menzel, C.: Knowledge representation, the World Wide Web, and the evolution of logic (2011) 0.02 
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    Abstract
    In this paper, I have traced a series of evolutionary adaptations of FOL motivated entirely by its use by knowledge engineers to represent and share information on the Web culminating in the development of Common Logic. While the primary goal in this paper has been to document this evolution, it is arguable, I think that CL's syntactic and semantic egalitarianism better realizes the goal "topic neutrality" that a logic should ideally exemplify - understood, at least in part, as the idea that logic should as far as possible not itself embody any metaphysical presuppositions. Instead of retaining the traditional metaphysical divisions of FOL that reflect its Fregean origins, CL begins as it were with a single, metaphysically homogeneous domain in which, potentially, anything can play the traditional roles of object, property, relation, and function. Note that the effect of this is not to destroy traditional metaphysical divisions. Rather, it simply to refrain from building those divisions explicitly into one's logic; instead, such divisions are left to the user to introduce and enforce axiomatically in an explicit metaphysical theory.
    Theme
    Semantic Web
  14. De Maio, C.; Fenza, G.; Loia, V.; Senatore, S.: Hierarchical web resources retrieval by exploiting Fuzzy Formal Concept Analysis (2012) 0.02 
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    Abstract
    In recent years, knowledge structuring is assuming important roles in several real world applications such as decision support, cooperative problem solving, e-commerce, Semantic Web and, even in planning systems. Ontologies play an important role in supporting automated processes to access information and are at the core of new strategies for the development of knowledge-based systems. Yet, developing an ontology is a time-consuming task which often needs an accurate domain expertise to tackle structural and logical difficulties in the definition of concepts as well as conceivable relationships. This work presents an ontology-based retrieval approach, that supports data organization and visualization and provides a friendly navigation model. It exploits the fuzzy extension of the Formal Concept Analysis theory to elicit conceptualizations from datasets and generate a hierarchy-based representation of extracted knowledge. An intuitive graphical interface provides a multi-facets view of the built ontology. Through a transparent query-based retrieval, final users navigate across concepts, relations and population.
    Content
    Beitrag in einem Themenheft "Soft Approaches to IA on the Web". Vgl.: doi:10.1016/j.ipm.2011.04.003.
    Source
    Information processing and management. 48(2012) no.3, S.399-418
  15. Atanassova, I.; Bertin, M.: Semantic facets for scientific information retrieval (2014) 0.02 
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    Abstract
    We present an Information Retrieval System for scientific publications that provides the possibility to filter results according to semantic facets. We use sentence-level semantic annotations that identify specific semantic relations in texts, such as methods, definitions, hypotheses, that correspond to common information needs related to scientific literature. The semantic annotations are obtained using a rule-based method that identifies linguistic clues organized into a linguistic ontology. The system is implemented using Solr Search Server and offers efficient search and navigation in scientific papers.
    Series
    Communications in computer and information science; vol.475
    Source
    Semantic Web Evaluation Challenge. SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers. Eds.: V. Presutti et al
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  16. Yi, M.: Information organization and retrieval using a topic maps-based ontology : results of a task-based evaluation (2008) 0.02 
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    Abstract
    As information becomes richer and more complex, alternative information-organization methods are needed to more effectively and efficiently retrieve information from various systems, including the Web. The objective of this study is to explore how a Topic Maps-based ontology approach affects users' searching performance. Forty participants participated in a task-based evaluation where two dependent variables, recall and search time, were measured. The results of this study indicate that a Topic Maps-based ontology information retrieval (TOIR) system has a significant and positive effect on both recall and search time, compared to a thesaurus-based information retrieval (TIR) system. These results suggest that the inclusion of a Topic Maps-based ontology is a beneficial approach to take when designing information retrieval systems.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.12, S.1898-1911
  17. Davies, J.; Weeks, R.; Krohn, U.: QuizRDF: search technology for the Semantic Web (2004) 0.02 
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    Abstract
    Important information is often scattered across Web and/or intranet resources. Traditional search engines return ranked retrieval lists that offer little or no information on the semantic relationships among documents. Knowledge workers spend a substantial amount of their time browsing and reading to find out how documents are related to one another and where each falls into the overall structure of the problem domain. Yet only when knowledge workers begin to locate the similarities and differences among pieces of information do they move into an essential part of their work: building relationships to create new knowledge. Information retrieval traditionally focuses on the relationship between a given query (or user profile) and the information store. On the other hand, exploitation of interrelationships between selected pieces of information (which can be facilitated by the use of ontologies) can put otherwise isolated information into a meaningful context. The implicit structures so revealed help users use and manage information more efficiently. Knowledge management tools are needed that integrate the resources dispersed across Web resources into a coherent corpus of interrelated information. Previous research in information integration has largely focused on integrating heterogeneous databases and knowledge bases, which represent information in a highly structured way, often by means of formal languages. In contrast, the Web consists to a large extent of unstructured or semi-structured natural language texts. As we have seen, ontologies offer an alternative way to cope with heterogeneous representations of Web resources. The domain model implicit in an ontology can be taken as a unifying structure for giving information a common representation and semantics. Once such a unifying structure exists, it can be exploited to improve browsing and retrieval performance in information access tools. QuizRDF is an example of such a tool.
    Source
    Towards the semantic Web: ontology-driven knowledge management. Eds.: J. Davies, u.a
    Theme
    Semantic Web
  18. Vallet, D.; Fernández, M.; Castells, P.: ¬An ontology-based information retrieval model (2005) 0.02 
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    Abstract
    Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontologybased KBs to improve search over large document repositories. Our approach includes an ontology-based scheme for the semi-automatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal is illustrated with sample experiments showing improvements with respect to keyword-based search, and providing ground for further research and discussion.
    Source
    The Semantic Web: research and applications ; second European Semantic WebConference, ESWC 2005, Heraklion, Crete, Greece, May 29 - June 1, 2005 ; proceedings. Eds.: A. Gómez-Pérez u. J. Euzenat
    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  19. Giri, K.; Gokhale, P.: Developing a banking service ontology using Protégé, an open source software (2015) 0.02 
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    Abstract
    Computers have transformed from single isolated devices to entry points into a worldwide network of information exchange. Consequently, support in the exchange of data, information, and knowledge is becoming the key issue in computer technology today. The increasing volume of data available on the Web makes information retrieval a tedious and difficult task. Researchers are now exploring the possibility of creating a semantic web, in which meaning is made explicit, allowing machines to process and integrate web resources intelligently. The vision of the semantic web introduces the next generation of the Web by establishing a layer of machine-understandable data. The success of the semantic web depends on the easy creation, integration and use of semantic data, which will depend on web ontology. The faceted approach towards analyzing and representing knowledge given by S R Ranganathan would be useful in this regard. Ontology development in different fields is one such area where this approach given by Ranganathan could be applied. This paper presents a case of developing ontology for the field of banking.
    Source
    Annals of library and information studies. 62(2015) no.4, S.281-285
  20. Padmavathi, T.; Krishnamurthy, M.: Ontological representation of knowledge for developing information services in food science and technology (2012) 0.02 
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    Abstract
    Knowledge explosion in various fields during recent years has resulted in the creation of vast amounts of on-line scientific literature. Food Science &Technology (FST) is also an important subject domain where rapid developments are taking place due to diverse research and development activities. As a result, information storage and retrieval has become very complex and current information retrieval systems (IRs) are being challenged in terms of both adequate precision and response time. To overcome these limitations as well as to provide naturallanguage based effective retrieval, a suitable knowledge engineering framework needs to be applied to represent, share and discover information. Semantic web technologies provide mechanisms for creating knowledge bases, ontologies and rules for handling data that promise to improve the quality of information retrieval. Ontologies are the backbone of such knowledge systems. This paper presents a framework for semantic representation of a large repository of content in the domain of FST.

Authors

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