8,062 research outputs found
Identification of Design Principles
This report identifies those design principles for a (possibly new) query and transformation
language for the Web supporting inference that are considered essential. Based upon these
design principles an initial strawman is selected. Scenarios for querying the Semantic Web
illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of
the query language to be designed and implemented by the REWERSE working group I4
Data Model and Query Constructs for Versatile Web Query Languages
As the Semantic Web is gaining momentum, the need for
truly versatile query languages becomes increasingly apparent. A Web
query language is called versatile if it can access in the same query program
data in different formats (e.g. XML and RDF). Most query languages
are not versatile: they have not been specifically designed to cope
with both worlds, providing a uniform language and common constructs
to query and transform data in various formats. Moreover, most of them
do not provide a flexible data model that is powerful enough to naturally
convey both Semantic Web data formats (especially RDF and
Topic Maps) and XML. This article highlights challenges related to the
data model and language constructs for querying both standard Web
and Semantic Web data with an emphasis on facilitating sophisticated
reasoning. It is shown that Xcerpt’s data model and querying constructs
are particularly well-suited for the Semantic Web, but that some adjustments
of the Xcerpt syntax allow for even more effective and natural
querying of RDF and Topic Maps
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Integration with Ontologies
One of today’s hottest IT topics is integration, as bringing together information from different sources and structures is not completely solved. The approach outlined here wants to illustrate how ontologies [Gr93] could help to support the integration process
Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web
Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions
Report on the first Twente Data Management Workshop on XML Databases and Information Retrieval
The Database Group of the University of Twente initiated a new series of workshops called Twente Data Management workshops (TDM), starting with one on XML Databases and Information Retrieval which took place on 21 June 2004 at the University of Twente. We have set ourselves two goals for the workshop series: i) To provide a forum to share original ideas as well as research results on data management problems; ii) To bring together researchers from the database community and researchers from related research fields
Multimedia search without visual analysis: the value of linguistic and contextual information
This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features
The aceToolbox: low-level audiovisual feature extraction for retrieval and classification
In this paper we present an overview of a software platform
that has been developed within the aceMedia project,
termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM),
with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images
Intelligent multimedia indexing and retrieval through multi-source information extraction and merging
This paper reports work on automated meta-data\ud
creation for multimedia content. The approach results\ud
in the generation of a conceptual index of\ud
the content which may then be searched via semantic\ud
categories instead of keywords. The novelty\ud
of the work is to exploit multiple sources of\ud
information relating to video content (in this case\ud
the rich range of sources covering important sports\ud
events). News, commentaries and web reports covering\ud
international football games in multiple languages\ud
and multiple modalities is analysed and the\ud
resultant data merged. This merging process leads\ud
to increased accuracy relative to individual sources
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