2,125 research outputs found

    An information retrieval approach to ontology mapping

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    In this paper, we present a heuristic mapping method and a prototype mapping system that support the process of semi-automatic ontology mapping for the purpose of improving semantic interoperability in heterogeneous systems. The approach is based on the idea of semantic enrichment, i.e., using instance information of the ontology to enrich the original ontology and calculate similarities between concepts in two ontologies. The functional settings for the mapping system are discussed and the evaluation of the prototype implementation of the approach is reported. \ud \u

    A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

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    Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

    Dealing with uncertain entities in ontology alignment using rough sets

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    This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Ontology alignment facilitates exchange of knowledge among heterogeneous data sources. Many approaches to ontology alignment use multiple similarity measures to map entities between ontologies. However, it remains a key challenge in dealing with uncertain entities for which the employed ontology alignment measures produce conflicting results on similarity of the mapped entities. This paper presents OARS, a rough-set based approach to ontology alignment which achieves a high degree of accuracy in situations where uncertainty arises because of the conflicting results generated by different similarity measures. OARS employs a combinational approach and considers both lexical and structural similarity measures. OARS is extensively evaluated with the benchmark ontologies of the ontology alignment evaluation initiative (OAEI) 2010, and performs best in the aspect of recall in comparison with a number of alignment systems while generating a comparable performance in precision

    Developing tools and models for evaluating geospatial data integration of official and VGI data sources

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    PhD ThesisIn recent years, systems have been developed which enable users to produce, share and update information on the web effectively and freely as User Generated Content (UGC) data (including Volunteered Geographic Information (VGI)). Data quality assessment is a major concern for supporting the accurate and efficient spatial data integration required if VGI is to be used alongside official, formal, usually governmental datasets. This thesis aims to develop tools and models for the purpose of assessing such integration possibilities. Initially, in order to undertake this task, geometrical similarity of formal and informal data was examined. Geometrical analyses were performed by developing specific programme interfaces to assess the positional, linear and polygon shape similarity among reference field survey data (FS); official datasets such as data from Ordnance Survey (OS), UK and General Directorate for Survey (GDS), Iraq agencies; and VGI information such as OpenStreetMap (OSM) datasets. A discussion of the design and implementation of these tools and interfaces is presented. A methodology has been developed to assess such positional and shape similarity by applying different metrics and standard indices such as the National Standard for Spatial Data Accuracy (NSSDA) for positional quality; techniques such as buffering overlays for linear similarity; and application of moments invariant for polygon shape similarity evaluations. The results suggested that difficulties exist for any geometrical integration of OSM data with both bench mark FS and formal datasets, but that formal data is very close to reference datasets. An investigation was carried out into contributing factors such as data sources, feature types and number of data collectors that may affect the geometrical quality of OSM data and consequently affect the integration process of OSM datasets with FS, OS and GDS. Factorial designs were undertaken in this study in order to develop and implement an experiment to discover the effect of these factors individually and the interaction between each of them. The analysis found that data source is the most significant factor that affects the geometrical quality of OSM datasets, and that there are interactions among all these factors at different levels of interaction. This work also investigated the possibility of integrating feature classification of official datasets such as data from OS and GDS geospatial data agencies, and informal datasets such as OSM information. In this context, two different models were developed. The first set of analysis included the evaluation of semantic integration of corresponding feature classifications of compared datasets. The second model was concerned with assessing the ability of XML schema matching of feature classifications of tested datasets. This initially involved a tokenization process in order to split up into single words classifications that were composed of multiple words. Subsequently, encoding feature classifications as XML schema trees was undertaken. The semantic similarity, data type similarity and structural similarity were measured between the nodes of compared schema trees. Once these three similarities had been computed, a weighted combination technique has been adopted in order to obtain the overall similarity. The findings of both sets of analysis were not encouraging as far as the possibility of effectively integrating feature classifications of VGI datasets, such as OSM information, and formal datasets, such as OS and GDS datasets, is concerned.Ministry of Higher Education and Scientific Research, Republic of Iraq

    Review implementation of linguistic approach in schema matching

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    Research related schema matching has been conducted since last decade. Few approach related schema matching has been conducted with various methods such as neuron network, feature selection, constrain based, instance based, linguistic, and so on. Some field used schema matching as basic model such as e-commerce, e-business and data warehousing. Implementation of linguistic approach itself has been used a long time with various problem such as to calculated entity similarity values in two or more schemas. The purpose of this paper was to provide an overview of previous studies related to the implementation of the linguistic approach in the schema matching and finding gap for the development of existing methods. Futhermore, this paper focused on measurement of similarity in linguistic approach in schema matching

    Natural Language Processing in-and-for Design Research

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    We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research
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