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Series

Communications in computer and information science; 478

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Abstract

Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.

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Abstract

The purpose of this paper is to determine semantic types for tags in social tagging systems. In social tagging systems, the determination of the semantic type of tags plays an important role in tag classification, increasing the semantic information of tags and establishing mapping relations between tagged resources and a normed ontology. The research reported in this paper constructs the semantic type library that is needed based on the Unified Medical Language System (UMLS) and FrameNet and determines the semantic type of selected tags that have been pretreated via direct matching using the Semantic Navigator tool, the Semantic Type Word Sense Disambiguation (STWSD) tools in UMLS, and artificial matching. And finally, we verify the feasibility of the determination of semantic type for tags by empirical analysis.

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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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Source

Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1706-1724

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.10, S.2087-2099

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Abstract

Ontologies have been a hot research topic for the recent decade and have been used for many applications such as information integration, semantic search, knowledge management, etc. Manual engineering of ontologies is a costly process and automatic ontology engineering lacks in precision. Folksonomies have recently emerged as another hot research topic and several research efforts have been made to extract lightweight ontologies automatically from folksonomy data. Due to the high cost of manual ontology engineering and the lack of precision in automatic ontology engineering it is important that we are able to evaluate the structure of the ontology. Detection of problems with the suggested ontology at an early stage can, especially for manually engineered ontologies, be cost saving. In this paper we present an approach to evaluate the quality of hierarchical relations in ontologies and folksonomy based structures. The approach is based on constructing shallow semantic representations of the ontology concepts and folksonomy tags. We specify four hypotheses regarding the semantic representations and different quality aspects of the hierarchical relations and perform an evaluation on two different data sets. The results of the evaluation confirm our hypotheses.

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Footnote

Part of a section "Papers from the 13th Meeting of the German ISKO "Theory, Information, and Organization of Knowledge," Potsdam, 19-20 March 2013"

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Abstract

Division is obviously important to Knowledge Organization. Typically, an organizational infrastructure might acknowledge three types of connecting relationships: class hierarchies, where some classes are subclasses of others, partitive hierarchies, where some items are parts of others, and instantiation, where some items are members of some classes (see Z39.19 ANSI/NISO 2005 as an example). The first two of these involve division (the third, instantiation, does not involve division). Logical division would usually be a part of hierarchical classification systems, which, in turn, are central to shelving in libraries, to subject classification schemes, to controlled vocabularies, and to thesauri. Partitive hierarchies, and partitive division, are often essential to controlled vocabularies, thesauri, and subject tagging systems. Partitive hierarchies also relate to the bearers of information; for example, a journal would typically have its component articles as parts and, in turn, they might have sections as their parts, and, of course, components might be arrived at by partitive division (see Tillett 2009 as an illustration). Finally, verbal division, disambiguating homographs, is basic to controlled vocabularies. Thus Division is a broad and relevant topic. This article, though, is going to focus on Logical Division.

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Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Source

Philosophy, computing and information science. Eds.: R. Hagengruber u. U.V. Riss

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Source

Journal of the Association for Information Science and Technology. 68(2017) no.3, S.724-738