Optimizing Recursive Information Gathering Plans in EMERAC

Kambhampati, Subbarao; Lambrecht, Eric; Nambiar, Ullas; Nie, Zaiqing; Gnanaprakasam, Senthil

doi:10.1023/b:jiis.0000012467.66268.9e

Cited by 16 publications

(16 citation statements)

References 27 publications

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“…The lack of forward propagation rules involving base predicates (and not just sources) makes the discovery of potential interactions between base predicates (and thus the full use of domain knowledge) impossible. Other related mediator-based Web integration systems are EMERAC [13] and Ariadne [15]. This paper extends our research related to the SILK intelligent information environment [3] to deal with the specificities of the Semantic Web.…”

Section: Discussionmentioning

confidence: 57%

Semantic Web Reasoning for Ontology-Based Integration of Resources

Badea

Tilivea

Hotaran

2004

Principles and Practice of Semantic Web Reasoning

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Abstract. The Semantic Web should enhance the current World Wide Web with reasoning capabilities for enabling automated processing of possibly distributed information. In this paper we describe an architecture for Semantic Web reasoning and query answering in a very general setting involving several heterogeneous information sources, as well as domain ontologies needed for offering a uniform and source-independent view on the data. Since querying a Web source is very costly in terms of response time, we focus mainly on the query planner of such a system, as it may allow avoiding the access to queryirrelevant sources or combinations of sources based on knowledge about the domain and the sources.Taking advantage of the huge amount of knowledge implicit and distributed on the Web is a significant challenge. The main obstacle is due to the fact that most Web pages were designed for human-centred browsing rather than being machineprocessable. In addition to static HTML pages the Web currently offers online access to a large number information resources, such as databases with a Web interface. But real-life applications frequently require combining the information from several such resources, which may not have been developed with this interoperability requirement in mind. Thus, a large amount of knowledge is implicit, heterogeneously distributed among various resources and thus hard to process automatically.The recent developments towards a "Semantic Web" should help address these problems. Being able to explicitly represent domain-specific knowledge in the form of ontologies, should allow reasoning about such machine-processable Web pages.The emergence of standards for data markup and interchange such as XML and for representing information about resources and their semantics (such as RDF and RDF Schema) can be seen as a first step in the transition towards a Semantic Web. However, the vast majority of Web pages still conform to the HTML standard, which only controls their visual aspects rather than their informational content. Extracting the informational content from such pages which essentially contain free text is a difficult practical problem. The Resource Description Framework (RDF) has been designed to complement such human-oriented text with machine-processable annotations. A large number of prototype systems able to read and reason about such annotations have been developed (TRIPLE [7], Metalog [20], SiLRI [8], Ontobroker [9]). However, currently only a very small minority of Web pages have RDF annotations. Moreover, existing annotations tend to refer to basic features such as document author, creation date, etc., but do not duplicate the information content of the page.

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Section: Discussionmentioning

confidence: 57%

Semantic Web Reasoning for Ontology-Based Integration of Resources

Badea

Tilivea

Hotaran

2004

Principles and Practice of Semantic Web Reasoning

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“…Kambhampati et al [13] describe an optimization technique that checks the constraints in the source descriptions and the constraint in the user query to remove all sources that have constraints that conflict with the constraints in the user query. For example consider a data source that provides road vector data for the bounding box ' [[25,-74],[27,-76]]'.…”

Section: Previous Work: View Integrationmentioning

confidence: 99%

“…Utilize the optimization techniques described in [13] to remove rules containing unnecessary source requests.…”

Section: Qgm's Extensions To Exploit Qualitymentioning

confidence: 99%

Quality-driven geospatial data integration

Thakkar

Knoblock

Ambite

2007

Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems

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Accurate and efficient integration of geospatial data is an important problem with applications in areas such as emergency response and urban planning. Some of the key challenges in supporting large-scale geospatial data integration are automatically computing the quality of the data provided by a large number of geospatial sources and dynamically providing high quality answers to the user queries based on a quality criteria supplied by the user. We describe a framework called the Quality-driven Geospatial Mediator (QGM) that supports efficient and accurate integration of geospatial data from a large number of sources. The key contributions of our framework are: (1) the ability to automatically estimate the quality of data provided by a source by using the information from another source of known quality, (2) representing the quality of data provided by the sources in a declarative data integration framework, and (3) a query answering technique that exploits the quality information to provide high quality geospatial data in response to user queries. Our experimental evaluation using over 1200 realworld sources shows that QGM can accurately estimate the quality of geospatial sources. Moreover, QGM provides better quality data in response to the user queries compared to the traditional data integration systems and does so with lower response time.

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“…Obviously this requires resolving the schematic and semantic heterogeneity among the sources. The task of providing a common interface over sources has been the focus of research done in the context of data mediation (integration) systems [9,3,15,11,12]. Data integration systems combine multiple data sources such that they appear as a single (virtually) integrated source projecting a single global schema.…”

Section: Motivationmentioning

confidence: 99%

The GEON portal

Nambiar

Ludaescher

Lin

et al. 2006

Proceedings of the 8th Annual ACM International Workshop on Web Information and Data Management

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Geoscience studies produce data from various observations, experiments, and simulations at an enormous rate. With proliferation of applications and data formats, the geoscience research community faces many challenges in effectively managing and sharing resources and in efficiently integrating and analyzing the data. In this paper, we discuss how this challenge is being addressed by the GEON Portal, a Web based distributed resource management system that provides integrated access to data and tools needed for knowledge discovery in the geosciences. Unlike previous data management efforts that were either data-driven or applicationdriven, the GEON Portal provides facilities for efficient sharing, discovery and integration of both data and services that use geoscience data. We identify the challenges involved in managing geoscientific resources and provide solutions that exploit the syntactic, semantic, temporal and spatial metadata associated with the resources. One of our goals is to provide some insight into the challenges involved in providing a comprehensive scientific data management solution based on our experiences with geoscientific data.

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Optimizing Recursive Information Gathering Plans in EMERAC

Cited by 16 publications

References 27 publications

Semantic Web Reasoning for Ontology-Based Integration of Resources

Semantic Web Reasoning for Ontology-Based Integration of Resources

Quality-driven geospatial data integration

The GEON portal

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