SwetoDblp Ontology of Computer Science Publications

Aleman-Meza, Boanerges; Hakimpour, Farshad; Arpinar, Budak; Sheth, Amit P.

doi:10.2139/ssrn.3199363

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…SwetoDBLP [1] is a large-scale ontology that contains bibliographic information of computer science publications. We analyze SwetoDBLP data set based on its term distribution and partition sizes.…”

Section: Experimental Testsmentioning

confidence: 99%

Overcoming limitations of term-based partitioning for distributed RDFS reasoning

Kulahcioglu

Bulut

2013

Proceedings of the Fifth Workshop on Semantic Web Information Management

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RDFS reasoning is carried out via joint terms of triples; accordingly, a distributed reasoning approach should bring together triples that have terms in common. To achieve this, term-based partitioning distributes triples to partitions based on the terms they include. However, skewed distribution of Semantic Web data results in unbalanced load distribution. A single peer should be able to handle even the largest partition, and this requirement limits scalability. This approach also suffers from data replication since a triple is sent to multiple partitions. In this paper, we propose a two-step method to overcome above limitations. Our RDFS specific term-based partitioning algorithm applies a selective distribution policy and distributes triples with minimum replication. Our schema-sensitive processing approach eliminates non-productive partitions, and enables processing of a partition regardless of its size. Resulting partitions reach full closure without repeating the global schema or without fix-point iteration as suggested by previous studies.

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Section: Experimental Testsmentioning

confidence: 99%

Overcoming limitations of term-based partitioning for distributed RDFS reasoning

Kulahcioglu

Bulut

2013

Proceedings of the Fifth Workshop on Semantic Web Information Management

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“…According to the basic principles of ontology and the ACM Topic Hierarchy [6], we create ontology to describe the terms about computer science, called "CmpOnto". Then, the ontology "SwetoDblp_2" is created through the extension of SwetoDblp [7] on the aspect of research field and keywords. The segment of ontology CmpOnto is as follows:…”

Section: The Structure In the Ontologymentioning

confidence: 99%

A Semantic Vector Retrieval Model for Desktop Documents

Li¹

2009

JSEA

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The paper provides a semantic vector retrieval model for desktop documents based on the ontology. Comparing with traditional vector space model, the semantic model using semantic and ontology technology to solve several problems that traditional model could not overcome such as the shortcomings of weight computing based on statistical method, the expression of semantic relations between different keywords, the description of document semantic vectors and the similarity calculating, etc. Finally, the experimental results show that the retrieval ability of our new model has significant improvement both on recall and precision

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“…In order to evaluate the performance of System Π for a real problem, we employ a real data set, SwetoDblp [14] . SwetoDblp is a large-size ontology dump from DBLP .…”

Section: Performance Evaluation On Swetodblpmentioning

confidence: 99%

“…The results of the performance evaluation on LUBM benchmark are given in Section 4. In Section 5, we show the performance of System Π on a real data set, i.e., SwetoDblp [14] . Finally, we present the conclusions and the future work in Section 6.…”

Section: Introductionmentioning

confidence: 99%

System Π: A Native RDF Repository Based on the Hypergraph Representation for RDF Data Model

et al. 2009

J. Comput. Sci. Technol.

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RDF is the data interchange layer for the Semantic Web. In order to manage the increasing amount of RDF data, an RDF repository should provide not only the necessary scalability and efficiency, but also sufficient inference capabilities. Though existing RDF repositories have made progress towards these goals, there is still ample space for improving the overall performance. In this paper, we propose a native RDF repository, System Π, to pursue a better tradeoff among system scalability, query efficiency, and inference capabilities. System Π takes a hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. Based on this native storage scheme, a set of efficient semantic query processing techniques are designed. First, several indices are built to accelerate RDF data access including a value index, a labeling scheme for transitive closure computation, and three triple indices. Second, we propose a hybrid inference strategy under the pD * semantics to support inference for OWL-Lite with a relatively low computational complexity. Finally, we extend the SPARQL algebra to explicitly express inference semantics in logical query plan by defining some new algebra operators. In addition, MD5 hash value of URI and schema level cache are introduced as practical implementation techniques. The results of performance evaluation on the LUBM benchmark and a real data set show that System Π has a better combined metric value than other comparable systems.

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SwetoDblp Ontology of Computer Science Publications

Cited by 5 publications

References 12 publications

Overcoming limitations of term-based partitioning for distributed RDFS reasoning

Overcoming limitations of term-based partitioning for distributed RDFS reasoning

A Semantic Vector Retrieval Model for Desktop Documents

System Π: A Native RDF Repository Based on the Hypergraph Representation for RDF Data Model

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