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- paper-26 type InProceedings.
- paper-26 label "An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices".
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- paper-26 abstract "The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception - explanation and discrimination - and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale.".
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- paper-26 title "An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices".
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- item-1 hasContent cory-henson.
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- item-2 hasContent krishnaprasad-thirunarayan.
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- item-3 hasContent amit-sheth.
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- paper-27 type InProceedings.
- paper-27 label "SRBench: A Streaming RDF/SPARQL Benchmark".
- paper-27 sameAs paper-27.
- paper-27 abstract "We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet comprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art.".
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- paper-27 title "SRBench: A Streaming RDF/SPARQL Benchmark".
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- item-1 hasContent ying-zhang.
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- item-3 hasContent oscar-corcho.
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- item-4 hasContent jean-paul-calbimonte.
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- paper-28 type InProceedings.
- paper-28 label "Discovering Concept Coverings in Ontologies of Linked Data Sources".
- paper-28 sameAs paper-28.
- paper-28 abstract "Despite the increase in the number of linked instances in the Linked Data Cloud in recent times, the absence of links at the concept level has resulted in heterogenous schemas, challenging the interoperability goal of the Semantic Web. In this paper, we address this problem by finding alignments between concepts from multiple Linked Data sources. Instead of only considering the existing concepts present in each ontology, we hypothesize new composite concepts defined as disjunctions of conjunctions of (RDF) types and value restrictions, which we call restriction classes, and generate alignments between these composite concepts. This extended concept language enables us to find more complete definitions and to even align sources that have rudimentary ontologies, such as those that are simple renderings of relational databases. Our concept alignment approach is based on analyzing the extensions of these concepts and their linked instances. Having explored the alignment of conjunctive concepts in our previous work, in this paper, we focus on concept coverings (disjunctions of restriction classes). We present an evaluation of this new algorithm to Geospatial, Biological Classification, and Genetics domains. The resulting alignments are useful for refining existing ontologies and determining the alignments between concepts in the ontologies, thus increasing the interoperability in the Linked Open Data Cloud.".
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- paper-28 title "Discovering Concept Coverings in Ontologies of Linked Data Sources".
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- item-1 hasContent rahul-parundekar.
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- item-2 hasContent craig-knoblock.
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- item-3 hasContent jose-luis-ambite.
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- paper-29 type InProceedings.
- paper-29 label "A Formal Semantics for Weighted Ontology Mappings".
- paper-29 sameAs paper-29.
- paper-29 abstract "Ontology mappings are often assigned a weight or confidence factor by matchers. Nonetheless, few semantic accounts have been given so far for such weights. This paper presents a formal semantics for weighted mappings between different ontologies. It is based on a classificational interpretation of mappings: if O1 and O2 are two ontologies used to classify a common set X , then mappings between O1 and O2 are interpreted to encode how elements of X classified in the concepts of O1 are re-classified in the concepts of O2, and weights are interpreted to measure how precise and complete re-classifications are. This semantics is justifiable by extensional practice of ontology matching. It is a conservative extension of a semantics of crisp mappings. The paper also includes properties that relate mapping entailment with description logic constructors.".
- paper-29 hasAuthorList authorList.
- paper-29 isPartOf proceedings-2.
- paper-29 title "A Formal Semantics for Weighted Ontology Mappings".
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- item-1 hasContent manuel-atencia.
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- item-2 hasContent alexander-borgida.
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