Search (110 results, page 6 of 6)

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Abstract

The focus of research on representing and reasoning with knowledge traditionally has been on single specifications and appropriate inference paradigms to draw conclusions from such data. Accordingly, this is also an essential aspect of ontology research which has received much attention in recent years. But ontologies introduce another new challenge based on the distributed nature of most of their applications, which requires to relate heterogeneous ontological specifications and to integrate information from multiple sources. These problems have of course been recognized, but many current approaches still lack the deep formal backgrounds on which todays reasoning paradigms are already founded. Here we propose category theory as a well-explored and very extensive mathematical foundation for modelling distributed knowledge. A particular prospect is to derive conclusions from the structure of those distributed knowledge bases, as it is for example needed when merging ontologies

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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.

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Source

Information - Wissenschaft und Praxis. 57(2006) H.6/7, S.301-308

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Series

Foyer: Information digital

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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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Source

Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.150-168

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Content

Aus den so festgelegten Beziehungen zwischen den verlinkten Begriffen sollen Computer automatisch sinnvolle Antworten auf komplexere Anfragen generieren können; z.B. eine Liste erzeugen, in der alle Länder von Afrika aufgeführt sind. Die Ländernamen führen als Link zurück zu dem Eintrag, in dem sie stehen - dem Artikel zum Land, für das man sich interessiert. Aus informationswissenschaftlicher Sicht ist das Informationsergebnis, das die neue Technologie produziert, relativ simpel. Aus sozialwissenschaftlicher Sicht steckt darin aber ein riesiges Potential zur Verbesserung der Bereitstellung von enzyklopädischer Information und Wissen für Menschen auf der ganzen Welt. Spannend ist auch die durch Semantic MediaWiki gegebene Möglichkeit der automatischen Zusammenführung von Informationen, die in den verschiedenen Wiki-Einträgen verteilt sind, bei einer hohen Konsistenz der Ergebnisse. Durch die feststehenden Beziehungen zwischen den Links enthält die automatisch erzeugte Liste nach Angaben der Karlsruher Forscher immer die gleichen Daten, egal, von welcher Seite aus man sie abruft. Die Suchmaschine holt sich die Bevölkerungszahl von Ägypten immer vom festgelegten Ägypten-Eintrag, so dass keine unterschiedlichen Zahlen in der Wiki-Landschaft kursieren können. Ein mit Semantic MediaWiki erstellter Testeintrag zu Deutschland kann unter http://ontoworld.org/index.php/Germany eingesehen werden. Die Faktenbox im unteren Teil des Eintrags zeigt an, was der "Eintrag" der Suchmaschine an Wissen über Deutschland anbieten kann. Diese Ergebnisse werden auch in dem Datenbeschreibungsstandard RDF angeboten. Mehr als das, was in der Faktenbox steht, kann der Eintrag nicht an die Suchmaschine abgeben."

Source

Information - Wissenschaft und Praxis. 57(2006) H.6/7, S.372

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Source

Information und Sprache: Beiträge zu Informationswissenschaft, Computerlinguistik, Bibliothekswesen und verwandten Fächern. Festschrift für Harald H. Zimmermann. Herausgegeben von Ilse Harms, Heinz-Dirk Luckhardt und Hans W. Giessen

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Abstract

This chapter describes two case studies conducted by the Swiss Life insurance group with the objective of proving the practical applicability and superiority of ontology-based knowledge management over classical approaches based on text retrieval technologies. The first case study in the domain of skills management uses manually constructed ontologies about skills, job functions and education. The purpose of the system is to give support for finding employees with certain skills. The ontologies are used to ensure that the user description of skills and the machine-held index of skills and people use the same vocabulary. The use of a shared vocabulary increases the performance of such a system significantly. The second case study aims at improving content-oriented access to passages of a 1000 page document about the International Accounting Standard on the corporate intranet. To this end, an ontology was automatically extracted from the document. It can be used to reformulate queries that turned out not to deliver the intended results. Since the ontology was automatically built, it is of a rather simple structure, consisting of weighted semantic associations between the relevant concepts in the document. We therefore call it a 'lightweight ontology'. The two case studies cover quite different aspects of using ontologies in knowledge management applications. Whereas in the second case study an ontology was automatically derived from a search space to improve information retrieval, in the first skills management case study the ontology itself introduces a structured search space. In one case study we gathered experience in building an ontology manually, while the challenge of the other case study was automatic ontology creation. A number of the novel Semantic Web-based tools described elsewhere in this book were used to build the two systems and both case studies described have led to projects to deploy live systems within Swiss Life.

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Source

Information - Wissenschaft und Praxis. 55(2004) H.3, S.139-142