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Content

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

Source

Graph-Based Methods for Natural Language Processing - proceedings of the Thirteenth Workshop (TextGraphs-13): November 4, 2019, Hong Kong : EMNLP-IJCNLP 2019. Ed.: Dmitry Ustalov

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Abstract

This document contains and presents test cases for the Web Ontology Language (OWL) approved by the Web Ontology Working Group. Many of the test cases illustrate the correct usage of the Web Ontology Language (OWL), and the formal meaning of its constructs. Other test cases illustrate the resolution of issues considered by the Working Group. Conformance for OWL documents and OWL document checkers is specified.

Date

14. 8.2011 13:33:22

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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.

Imprint

Pittsburgh, PA : Carnegie Mellon University, School of Computer Science, Language Technologies Institute

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Pages

S.11-22

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Abstract

Das CIDOC Conceptual Reference Model (CRM) ist eine Ontologie für den Bereich des Kulturellen Erbes, die als ISO 21127 standardisiert ist. Inzwischen liegen auch OWL-DL-Implementationen des CRM vor, die ihren Einsatz auch im Semantic Web ermöglicht. OWL-DL ist eine entscheidbare Untermenge der Web Ontology Language, die vom W3C spezifiziert wurde. Lokale Anwendungsontologien, die ebenfalls in OWL-DL modelliert werden, können über Subklassenbeziehungen mit dem CRM als Referenzontologie verbunden werden. Dadurch wird es automatischen Prozessen ermöglicht, autonom heterogene Daten semantisch zu validieren, zueinander in Bezug zu setzen und Anfragen über verschiedene Datenbestände innerhalb der Wissensdomäne zu verarbeiten und zu beantworten.

Source

Wissensspeicher in digitalen Räumen: Nachhaltigkeit - Verfügbarkeit - semantische Interoperabilität. Proceedings der 11. Tagung der Deutschen Sektion der Internationalen Gesellschaft für Wissensorganisation, Konstanz, 20. bis 22. Februar 2008. Hrsg.: J. Sieglerschmidt u. H.P.Ohly

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Abstract

We present a deductive data model for concept-based query expansion. It is based on three abstraction levels: the conceptual, linguistic and occurrence levels. Concepts and relationships among them are represented at the conceptual level. The expression level represents natural language expressions for concepts. Each expression has one or more matching models at the occurrence level. Each model specifies the matching of the expression in database indices built in varying ways. The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for environments providing heterogeneous IR systems. Expansion is controlled by adjustable matching reliability.

Source

Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR '96), Zürich, Switzerland, August 18-22, 1996. Eds.: H.P. Frei et al

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Abstract

Major efforts have been conducted on ontology learning, that is, semiautomatic processes for the construction of domain ontologies from diverse sources of information. In the past few years, a research trend has focused on the construction of educational ontologies, that is, ontologies to be used for educational purposes. The identification of the terminology is crucial to build ontologies. Term extraction techniques allow the identification of the domain-related terms from electronic resources. This paper presents LiTeWi, a novel method that combines current unsupervised term extraction approaches for creating educational ontologies for technology supported learning systems from electronic textbooks. LiTeWi uses Wikipedia as an additional information source. Wikipedia contains more than 30 million articles covering the terminology of nearly every domain in 288 languages, which makes it an appropriate generic corpus for term extraction. Furthermore, given that its content is available in several languages, it promotes both domain and language independence. LiTeWi is aimed at being used by teachers, who usually develop their didactic material from textbooks. To evaluate its performance, LiTeWi was tuned up using a textbook on object oriented programming and then tested with two textbooks of different domains-astronomy and molecular biology.

Date

22. 1.2016 12:38:14

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Abstract

The Web Ontology Language OWL is a semantic markup language for publishing and sharing ontologies on the World Wide Web. OWL is developed as a vocabulary extension of RDF (the Resource Description Framework) and is derived from the DAML+OIL Web Ontology Language. This document contains a structured informal description of the full set of OWL language constructs and is meant to serve as a reference for OWL users who want to construct OWL ontologies.

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Abstract

Natural Language is not only the most important means of communication between human beings, it is also used over historical periods for the preservation of cultural achievements and their transmission from one generation to the other. During the last few decades, the flod of digitalized information has been growing tremendously. This tendency will continue with the globalisation of information societies and with the growing importance of national and international computer networks. This is one reason why the theoretical understanding and the automated treatment of communication processes based on natural language have such a decisive social and economic impact. In this context, the semantic representation of knowledge originally formulated in natural language plays a central part, because it connects all components of natural language processing systems, be they the automatic understanding of natural language (analysis), the rational reasoning over knowledge bases, or the generation of natural language expressions from formal representations. This book presents a method for the semantic representation of natural language expressions (texts, sentences, phrases, etc.) which can be used as a universal knowledge representation paradigm in the human sciences, like linguistics, cognitive psychology, or philosophy of language, as well as in computational linguistics and in artificial intelligence. It is also an attempt to close the gap between these disciplines, which to a large extent are still working separately.

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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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Abstract

The aim of this book is to highlight the relationship between knowledge representation and language in artificial intelligence, and in particular on the way in which the choice of representation influences the language used to discuss a problem - and vice versa. Opening with a discussion of knowledge representation methods, and following this with a look at reasoning methods, the author begins to make his case for the intimate relationship between language and representation. He shows how each representation method fits particularly well with some reasoning methods and less so with others, using specific languages as examples. The question of representation change, an important and complex issue about which very little is known, is addressed. Dr Hodgson gathers together recent work on problem solving, showing how, in some cases, it has been possible to use representation changes to recast problems into a language that makes them easier to solve. The author maintains throughout that the relationships that this book explores lie at the heart of the construction of large systems, examining a number of the current large AI systems from the viewpoint of representation and language to prove his point.

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Abstract

The World Wide Web as it is currently constituted resembles a poorly mapped geography. Our insight into the documents and capabilities available are based on keyword searches, abetted by clever use of document connectivity and usage patterns. The sheer mass of this data is unmanageable without powerful tool support. In order to map this terrain more precisely, computational agents require machine-readable descriptions of the content and capabilities of Web accessible resources. These descriptions must be in addition to the human-readable versions of that information. The OWL Web Ontology Language is intended to provide a language that can be used to describe the classes and relations between them that are inherent in Web documents and applications. This document demonstrates the use of the OWL language to - formalize a domain by defining classes and properties of those classes, - define individuals and assert properties about them, and - reason about these classes and individuals to the degree permitted by the formal semantics of the OWL language. The sections are organized to present an incremental definition of a set of classes, properties and individuals, beginning with the fundamentals and proceeding to more complex language components.

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Abstract

The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. This document serves as an introduction to OWL 2 and the various other OWL 2 documents. It describes the syntaxes for OWL 2, the different kinds of semantics, the available profiles (sub-languages), and the relationship between OWL 1 and OWL 2.

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Abstract

This entry introduces ontologies as a potential "silver bullet" for knowledge management, enterprise application integration, and e-commerce. Ontologies enable knowledge sharing and knowledge reuse. The degree to which an ontology is machine-understandable, its formality, is determined by the language used for the specification of the ontology. There exists a trade-off between the expressiveness of an ontology language and the modeling support it provides for the ontology developer. This entry also describes how different knowledge representation formalisms, together with the Web languages XML and RDF, have influenced the development of the Web ontology language OWL.

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Abstract

Last word ontological thought and practice is exemplified on an axiomatic framework [a model for an Integrative Cross-Language Ontology (ICLO), cf. Poli, R., Schmitz-Esser, W., forthcoming 2007] that is highly general, based on natural language, multilingual, can be implemented as topic maps and may be openly enhanced by software available for particular languages. Basics of ontological modelling, conditions for construction and maintenance, and the most salient points in application are addressed, such as cross-language text mining and knowledge generation. The rationale is to open the eyes for the tremendous potential of terminology-based ontologies for principled Knowledge Organization and the interchange and reuse of formalized knowledge.

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Abstract

The Resource Description Framework (RDF) is a general-purpose language for representing information in the Web. This specification describes how to use RDF to describe RDF vocabularies. This specification defines a vocabulary for this purpose and defines other built-in RDF vocabulary initially specified in the RDF Model and Syntax Specification.

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Abstract

The Resource Description Framework (RDF) is a language for representing information about resources in the World Wide Web. This Primer is designed to provide the reader with the basic knowledge required to effectively use RDF. It introduces the basic concepts of RDF and describes its XML syntax. It describes how to define RDF vocabularies using the RDF Vocabulary Description Language, and gives an overview of some deployed RDF applications. It also describes the content and purpose of other RDF specification documents.

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Abstract

Based on the research on three general classification schemes, this paper discusses issues encountered when expressing classification schemes in SKOS and explores opportunities of resolving major issues using OWL 2 Web Ontology Language.

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Abstract

This document specifies usage scenarios, goals and requirements for a web ontology language. An ontology formally defines a common set of terms that are used to describe and represent a domain. Ontologies can be used by automated tools to power advanced services such as more accurate web search, intelligent software agents and knowledge management.

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Content

Tutorial topics and outline 1. Tutorial Background Overview The Semantic Web, Linked Data, and the Resource Description Framework 2. Ontology Basics and RDFS Tutorial Semantic modeling, domain ontologies, and RDF Vocabulary Description Language (RDFS) concepts and terminology Examples: domain ontologies, models, and schemas Exercises 3. OWL Overview Tutorial Web Ontology Language (OWL): selected concepts and terminology Exercises