Search (133 results, page 7 of 7)

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Source

Advanced Information Systems Engineering: Proceedings of the 14th Internarional Conference on Advanced Information Systems Engineering (CAiSE 2002), Toronto, Canada, May 27-31. Eds.: Anne Banks Pidduck, John Mylopoulos, Carson C. Woo, M. Tamer Özsu

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Abstract

A database supports legal research by matching a user's request for information with documents of the database that contain it. Indexes are among the oldest tools to achieve that aim. Many legal publishers continue to provide manual subject indexing of legal documents, in addition to automatic full-text indexing, which improves the performance of a full-text search.

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Abstract

Terminology has evolved from static and prescriptive theories to dynamic and cognitive approaches. Thanks to these approaches, there have been significant advances in the design and elaboration of terminological resources. This has resulted in the creation of tools such as terminological knowledge bases, which are able to show how concepts are interrelated through different semantic or conceptual relations. Of these relations, hyponymy is the most relevant to terminology work because it deals with concept categorization and term hierarchies. This doctoral thesis presents an enhancement of the semantic structure of EcoLexicon, a terminological knowledge base on environmental science. The aim of this research was to improve the description, categorization, and representation of hyponymy in environmental terminology. Therefore, we created HypoLexicon, a new stand-alone module for EcoLexicon in the form of a hyponymy-based terminological resource. This resource contains twelve terminological entries from four specialized domains (Biology, Chemistry, Civil Engineering, and Geology), which consist of 309 concepts and 465 terms associated with those concepts. This research was mainly based on the theoretical premises of Frame-based Terminology. This theory was combined with Cognitive Linguistics, for conceptual description and representation; Corpus Linguistics, for the extraction and processing of linguistic and terminological information; and Ontology, related to hyponymy and relevant for concept categorization. HypoLexicon was constructed from the following materials: (i) the EcoLexicon English Corpus; (ii) other specialized terminological resources, including EcoLexicon; (iii) Sketch Engine; and (iv) Lexonomy. This thesis explains the methodologies applied for corpus extraction and compilation, corpus analysis, the creation of conceptual hierarchies, and the design of the terminological template. The results of the creation of HypoLexicon are discussed by highlighting the information in the hyponymy-based terminological entries: (i) parent concept (hypernym); (ii) child concepts (hyponyms, with various hyponymy levels); (iii) terminological definitions; (iv) conceptual categories; (v) hyponymy subtypes; and (vi) hyponymic contexts. Furthermore, the features and the navigation within HypoLexicon are described from the user interface and the admin interface. In conclusion, this doctoral thesis lays the groundwork for developing a terminological resource that includes definitional, relational, ontological and contextual information about specialized hypernyms and hyponyms. All of this information on specialized knowledge is simple to follow thanks to the hierarchical structure of the terminological template used in HypoLexicon. Therefore, not only does it enhance knowledge representation, but it also facilitates its acquisition.

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Abstract

This paper is concerned with the issue of scope in topic maps. Topic maps are a form of knowledge representation suitable for solving a number of complex problems in the area of information management, ranging from findability (navigation and querying) to knowledge management and enterprise application integration (EAI). The topic map paradigm has its roots in efforts to understand the essential semantics of back-of-book indexes in order that they might be captured in a form suitable for computer processing. Once understood, the model of a back-of-book index was generalised in order to cover the needs of digital information, and extended to encompass glossaries and thesauri, as well as indexes. The resulting core model, of typed topics, associations, and occurrences, has many similarities with the semantic networks developed by the artificial intelligence community for representing knowledge structures. One key requirement of topic maps from the earliest days was to be able to merge indexes from disparate origins. This requirement accounts for two further concepts that greatly enhance the power of topic maps: subject identity and scope. This paper concentrates on scope, but also includes a brief discussion of the feature known as the topic naming constraint, with which it is closely related. It is based on the authors' experience in creating topic maps (in particular, the Italian Opera Topic Map, and in implementing processing systems for topic maps (in particular, the Ontopia Topic Map Engine and Navigator.

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Abstract

Internet of Things (IoT) brings the third development wave of the global information industry which makes users, network and perception devices cooperate more closely. However, if IoT has security problems, it may cause a variety of damage and even threaten human lives and properties. To improve the abilities of monitoring, providing emergency response and predicting the development trend of IoT security, a new paradigm called network security situation awareness (NSSA) is proposed. However, it is limited by its ability to mine and evaluate security situation elements from multi-source heterogeneous network security information. To solve this problem, this paper proposes an IoT network security situation awareness model using situation reasoning method based on semantic ontology and user-defined rules. Ontology technology can provide a unified and formalized description to solve the problem of semantic heterogeneity in the IoT security domain. In this paper, four key sub-domains are proposed to reflect an IoT security situation: context, attack, vulnerability and network flow. Further, user-defined rules can compensate for the limited description ability of ontology, and hence can enhance the reasoning ability of our proposed ontology model. The examples in real IoT scenarios show that the ability of the network security situation awareness that adopts our situation reasoning method is more comprehensive and more powerful reasoning abilities than the traditional NSSA methods. [http://ieeexplore.ieee.org/abstract/document/7999187/]

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Source

Journal of Intelligent Information Systems

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Footnote

Preprint. To be published as Vol 122 in the Conferences in Research and Practice in Information Technology Series by the Australian Computer Society Inc. http://crpit.com/.

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Abstract

This paper presents an overview of the work in progress at the W3C to produce a standard conversion of WordNet to the RDF/OWL representation language in use in the SemanticWeb community. Such a standard representation is useful to provide application developers a high-quality resource and to promote interoperability. Important requirements in this conversion process are that it should be complete and should stay close to WordNet's conceptual model. The paper explains the steps taken to produce the conversion and details design decisions such as the composition of the class hierarchy and properties, the addition of suitable OWL semantics and the chosen format of the URIs. Additional topics include a strategy to incorporate OWL and RDFS semantics in one schema such that both RDF(S) infrastructure and OWL infrastructure can interpret the information correctly, problems encountered in understanding the Prolog source files and the description of the two versions that are provided (Basic and Full) to accommodate different usages of WordNet.

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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

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

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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Content

Eine neue visuelle Ordnung Martin Hirsch ist der Enkel des Nobelpreisträgers Werner Heisenberg. Außerdem ist er Hirnforscher und beschäftigt sich seit Jahren mit der Frage: Was tut mein Kopf eigentlich, während ich hirnforsche? Ralf von Grafenstein ist Marketingexperte und spezialisiert auf Dienstleistungen im Internet. Zusammen haben sie also am 1. Dezember 2008 eine Firma in Berlin gegründet, deren Heiliger Gral besagte Scheibe ist, auf der - das ist die Idee - bald die ganze Welt, die Internetwelt zumindest, Platz finden soll. Die Scheibe heißt eyePlorer, was sich als Aufforderung an ihre Nutzer versteht. Die sollen auf einer neuartigen, eben scheibenförmigen Plattform die unermesslichen Datensätze des Internets in eine neue visuelle Ordnung bringen. Der Schlüssel dafür, da waren sich Hirsch und von Grafenstein sicher, liegt in der Hirnforschung, denn warum nicht die assoziativen Fähigkeiten des Menschen auf Suchmaschinen übertragen? Anbieter wie Google lassen von solchen Ansätzen bislang die Finger. Hier setzt man dafür auf Volltextprogramme, also sprachbegabte Systeme, die letztlich aber, genau wie die Schlagwortsuche, nur zu opak gerankten Linksammlungen führen. Weiter als auf Seite zwei des Suchergebnisses wagt sich der träge Nutzer meistens nicht vor. Weil sie niemals wahrgenommen wird, fällt eine Menge möglicherweise kostbare Information unter den Tisch.