An Evaluation of Knowledge Base Systems for Large OWL Datasets

Guo, Yuanbo; Pan, Zhengxiang; Heflin, Jeff

doi:10.1007/978-3-540-30475-3_20

Cited by 104 publications

(77 citation statements)

References 12 publications

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“…BigOWLIM 8 is a scalable repository supporting structured queries but uses its own proprietary storage and index format. LUBM [18] benchmark are developed alongside those work to evaluate semantic web knowledge base systems [21].…”

Section: Related Workmentioning

confidence: 99%

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

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Section: Related Workmentioning

confidence: 99%

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

et al. 2007

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“…The first weakness is addressed by using efficient and large-scale ontology repositories [17] in combination with Lucene 3 . Lucene indexes the semantic entities in the online and distributed back-end repositories into one or more indexes, and is used as our fast search engine 4 , which supports fuzzy searches based on the Lavenshtein Distance, or Edit Distance algorithm.…”

Section: Phase I: Syntactic Mappingmentioning

confidence: 99%

PowerMap: Mapping the Real Semantic Web on the Fly

López

Sabou

Motta

2006

Lecture Notes in Computer Science

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Abstract. Ontology mapping plays an important role in bridging the semantic gap between distributed and heterogeneous data sources. As the Semantic Web slowly becomes real and the amount of online semantic data increases, a new generation of tools is developed that automatically find and integrate this data. Unlike in the case of earlier tools where mapping has been performed at the design time of the tool, these new tools require mapping techniques that can be performed at run time. The contribution of this paper is twofold. First, we investigate the general requirements for run time mapping techniques. Second, we describe our PowerMap mapping algorithm that was designed to be used at run-time by an ontology based question answering tool.

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“…− a big set of 255Mb, containing 3,196,692 statements, 813,479 unique resources and the average node degree of 3.90 in the biggest connected component. − a small synthetic dataset, generated to include three ontologies (business, sports, and entertainment); 14Mb in size, containing 104,891 statements, 29,825 unique resources and the average node degree of 3.86 in the biggest component, and − a big synthetic set, generated as Univ(50, 0) using the Lehigh University Benchmark [10], 556Mb in size, containing 6,888,642 statements, 1,082,818 unique resources and the average node degree of 6.09.…”

Section: Data Setsmentioning

confidence: 99%

BRAHMS: A WorkBench RDF Store and High Performance Memory System for Semantic Association Discovery

Janik

Kochut

2005

The Semantic Web – ISWC 2005

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Abstract. Discovery of semantic associations in Semantic Web ontologies is an important task in various analytical activities. Several query languages and storage systems have been designed and implemented for storage and retrieval of information in RDF ontologies. However, they are inadequate for semantic association discovery. In this paper we present the design and implementation of BRAHMS, an efficient RDF storage system, specifically designed to support fast semantic association discovery in large RDF bases. We present memory usage and timing results of several tests performed with BRAHMS and compare them to similar tests performed using Jena, Sesame, and Redland, three of the well-known RDF storage systems. Our results show that BRAHMS handles basic association discovery well, while the RDF query languages and even the low-level APIs in the other three tested systems are not suitable for the implementation of semantic association discovery algorithms.

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An Evaluation of Knowledge Base Systems for Large OWL Datasets

Cited by 104 publications

References 12 publications

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

PowerMap: Mapping the Real Semantic Web on the Fly

BRAHMS: A WorkBench RDF Store and High Performance Memory System for Semantic Association Discovery

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