Search (77 results, page 1 of 4)

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Content

Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.

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Content

Vgl.: http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F1627&ei=tAtYUYrBNoHKtQb3l4GYBw&usg=AFQjCNHeaxKkKU3-u54LWxMNYGXaaDLCGw&sig2=8WykXWQoDKjDSdGtAakH2Q&bvm=bv.44442042,d.Yms.

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Content

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Language and Information Technologies. Vgl.: https%3A%2F%2Fwww.cs.cmu.edu%2F~cx%2Fpapers%2Fknowledge_based_text_representation.pdf&usg=AOvVaw0SaTSvhWLTh__Uz_HtOtl3.

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Abstract

Knowledge organization systems have been studied in several fields and for different and complementary aspects. Among the aspects that concentrate common interests, in this article we highlight those related to the terminological and conceptual relationships among the components of any knowledge organization system. This research aims to contribute to the critical analysis of knowledge organization systems, especially ontologies, thesauri, and classification systems, by the comprehension of its similarities and differences when dealing with concepts and their ways of relating to each other as well as to the conceptual design that is adopted.

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Abstract

In this chapter I propose that frames provide the fundamental representation of knowledge in human cognition. In the first section, I raise problems with the feature list representations often found in theories of knowledge, and I sketch the solutions that frames provide to them. In the second section, I examine the three fundamental concepts of frames: attribute-value sets, structural invariants, and constraints. Because frames also represents the attributes, values, structural invariants, and constraints within a frame, the mechanism that constructs frames builds them recursively. The frame theory I propose borrows heavily from previous frame theories, although its collection of representational components is somewhat unique. Furthermore, frame theorists generally assume that frames are rigid configurations of independent attributes, whereas I propose that frames are dynamic relational structures whose form is flexible and context dependent. In the third section, I illustrate how frames support a wide variety of representational tasks central to conceptual processing in natural and artificial intelligence. Frames can represent exemplars and propositions, prototypes and membership, subordinates and taxonomies. Frames can also represent conceptual combinations, event sequences, rules, and plans. In the fourth section, I show how frames define the extent of conceptual fields and how they provide a powerful productive mechanism for generating specific concepts within a field.

Source

Frames, fields and contrasts: new essays in semantic and lexical organization. Eds.: A. Lehrer u. E.F. Kittay

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Abstract

Looking for ontology in a search engine, one can find so many different approaches that it can be difficult to understand which field of research the subject belongs to and how it can be useful. The term ontology is employed within philosophy, computer science, and information science with different meanings. To take advantage of what ontology theories have to offer, one should understand what they address and where they come from. In information science, except for a few papers, there is no initiative toward clarifying what ontology really is and the connections that it fosters among different research fields. This article provides such a clarification. We begin by revisiting the meaning of the term in its original field, philosophy, to reach its current use in other research fields. We advocate that ontology is a genuine and relevant subject of research in information science. Finally, we conclude by offering our view of the opportunities for interdisciplinary research.

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Abstract

Research in ontology has, in recent years, become widespread in the field of information systems, in various areas of sciences, in business, in economy, and in industry. The importance of ontologies is increasingly recognized in fields diverse as in e-commerce, semantic web, enterprise, information integration, information science, qualitative modeling of physical systems, natural language processing, knowledge engineering, and databases. Ontologies provide formal specifications and harmonized definitions of concepts used to represent knowledge of specific domains. An ontology supplies a unifying framework for communication, it establishes a basis for knowledge organization and knowledge representation and contributes to theory formation and modeling of a specific domain. In the current paper, we present and discuss principles of organization and representation of knowledge that grew out of the use of formal ontology. The core of the discussed ontological framework is a top-level ontology, called GFO (General Formal Ontology), which is being developed at the University of Leipzig. These principles make use of the onto-axiomatic method, of graduated conceptualizations, of levels of reality, and of top-level-supported methods for ontology-development. We explore the interrelations between formal ontology and knowledge organization, and argue for a close interaction between both fields

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Abstract

LCSH is known as such since 1975. It always has created headings to serve the LC collections instead of a theoretical basis. It started to replace cross reference codes by thesaural codes in 1986, in a mechanical fashion. It was in no way transformed into a thesaurus. Its encyclopedic coverage, its pre-coordinate concepts make it substantially distinct, considering that thesauri usually map a restricted field of knowledge and use uniterms. The questions raised are whether the new symbols comply with thesaurus standards and if they are true to one or to several models. Explanations and definitions from other lists of subject headings and thesauri, literature in the field of classification and subject indexing will provide some answers. For instance, see refers from a subject heading not used to another or others used. Exceptionally it will lead from a specific term to a more general one. Some equate a see reference with the equivalence relationship. Such relationships are pointed by USE in LCSH. See also references are made from the broader subject to narrower parts of it and also between associated subjects. They suggest lateral or vertical connexions as well as reciprocal relationships. They serve a coordination purpose for some, lay down a methodical search itinerary for others. Since their inception in the 1950's thesauri have been devised for indexing and retrieving information in the fields of science and technology. Eventually they attended to a number of social sciences and humanities. Research derived from thesauri was voluminous. Numerous guidelines are designed. They did not discriminate between the "hard" sciences and the social sciences. RT relationships are widely but diversely used in numerous controlled vocabularies. LCSH's aim is to achieve a list almost free of RT and SA references. It thus restricts relationships to BT/NT, USE and UF. This raises the question as to whether all fields of knowledge can "fit" in the Procrustean bed of RT/NT, i.e., genus/species relationships. Standard codes were devised. It was soon realized that BT/NT, well suited to the genus/species couple could not signal a whole-part relationship. In LCSH, BT and NT function as reciprocals, the whole-part relationship is taken into account by ISO. It is amply elaborated upon by authors. The part-whole connexion is sometimes studied apart. The decision to replace cross reference codes was an improvement. Relations can now be distinguished through the distinct needs of numerous fields of knowledge are not attended to. Topic inclusion, and topic-subtopic, could provide the missing link where genus/species or whole/part are inadequate. Distinct codes, BT/NT and whole/part, should be provided. Sorting relationships with mechanical means can only lead to confusion.

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Abstract

After presenting some of the basic features of upper ontologies, the thesis is defended that all the relations needed by any concrete application can be generated by a small set of general relations, by adding proper ontological constraints to the general relations' arguments. This procedure provides an explicit and verifiable grounding to all forms of knowledge managements, including acquisition, interchange, integration, reuse, merging, aligning and updating knowledge. Upper ontologies therefore provide cues for developing both unification and decomposition methods. Finally, upper ontologies pave the ground for enhancing automatic reasoning and other machine-oriented procedures. I conclude by mentioning a difficulty in the theory of semantic fields.

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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.

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Abstract

Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.

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Abstract

Research in ontology has in recent years become widespread in the field of information systems, in distinct areas of sciences, in business, in economy, and in industry. The importance of ontologies is increasingly recognized in fields diverse as in e-commerce, semantic web, enterprise, information integration, qualitative modelling of physical systems, natural language processing, knowledge engineering, and databases. Ontologies provide formal specifications and harmonized definitions of concepts used to represent knowledge of specific domains. An ontology supplies a unifying framework for communication and establishes the basis of the knowledge about a specific domain. The term ontology has two meanings, it denotes, on the one hand, a research area, on the other hand, a system of organized knowledge. A system of knowledge may exhibit various degrees of formality; in the strongest sense it is an axiomatized and formally represented theory. which is denoted throughout this paper by the term axiomatized ontology. We use the term formal ontology to name an area of research which is becoming a science similar as formal or mathematical logic. Formal ontology is an evolving science which is concerned with the systematic development of axiomatic theories describing forms, modes, and views of being of the world at different levels of abstraction and granularity. Formal ontology combines the methods of mathematical logic with principles of philosophy, but also with the methods of artificial intelligence and linguistics. At themost general level of abstraction, formal ontology is concerned with those categories that apply to every area of the world. The application of formal ontology to domains at different levels of generality yields knowledge systems which are called, according to the level of abstraction, Top Level Ontologies or Foundational Ontologies, Core Domain or Domain Ontologies. Top level or foundational ontologies apply to every area of the world, in contrast to the various Generic, Domain Core or Domain Ontologies, which are associated to more restricted fields of interest. A foundational ontology can serve as a unifying framework for representation and integration of knowledge and may support the communication and harmonisation of conceptual systems. The current paper presents an overview about the current stage of the foundational ontology GFO.

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Pages

S.11-22

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Date

13.12.2017 14:17:22

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Date

26.12.2011 13:22:07

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Date

30. 5.2010 16:22:35

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Source

Spektrum der Wissenschaft. 2018, H.1, S.22-27

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Abstract

The originality of this book, which deals with such a new subject matter, lies in the application of methods and concepts never used before - such as Ontologies and Taxonomies, as well as Thesauri - to the ordering of knowledge based on primary information. Chapters in the book also examine the study of Ontologies, Taxonomies and Thesauri from the perspective of Systematics and General Systems Theory. "Ontologies, Taxonomy and Thesauri in Information Organisation and Retrieval" will be extremely useful to those operating within the network of related fields, which includes Documentation and Information Science.

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Abstract

This publication shows how the gap between the HTML based internet and the RDF based vision of the semantic web might be bridged, by linking words in texts to concepts of ontologies. Most current search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. However, the indexes do not contain synonyms, cannot differentiate between homonyms ('mouse' as a pointing vs. 'mouse' as an animal) and users receive different search results when they use different conjugation forms of the same word. In this publication, we present a system that uses ontologies and Natural Language Processing techniques to index texts, and thus supports word sense disambiguation and the retrieval of texts that contain equivalent words, by indexing them to concepts of ontologies. For this purpose, we developed fully automated methods for mapping equivalent concepts of imported RDF ontologies (for this prototype WordNet, SUMO and OpenCyc). These methods will thus allow the seamless integration of domain specific ontologies for concept based information retrieval in different domains. To demonstrate the practical workability of this approach, a set of web pages that contain synonyms and homonyms were indexed and can be queried via a search engine like query frontend. However, the ontology based indexing approach can also be used for other data mining applications such text clustering, relation mining and for searching free text fields in biological databases. The ontology alignment methods and some of the text mining principles described in this publication are now incorporated into the ONDEX system http://ondex.sourceforge.net/.

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Abstract

Astronomy, like many domains, already has several sets of terminology in general use, referred to as controlled vocabularies. For example, the keywords for tagging journal articles, or the taxonomy of terms used to label image files. These existing vocabularies can be encoded into skos, a W3C proposed recommendation for representing vocabularies on the Semantic Web, so that computer systems can help users to search for and discover resources tagged with vocabulary concepts. However, this requires a search mechanism to go from a user-supplied string to a vocabulary concept. In this paper, we present our experiences in implementing the Vocabulary Explorer, a vocabulary search service based on the Terrier Information Retrieval Platform. We investigate the capabilities of existing document weighting models for identifying the correct vocabulary concept for a query. Due to the highly structured nature of a skos encoded vocabulary, we investigate the effects of term weighting (boosting the score of concepts that match on particular fields of a vocabulary concept), and query expansion. We found that the existing document weighting models provided very high quality results, but these could be improved further with the use of term weighting that makes use of the semantic evidence.