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54
Hexastore: Sextuple Indexing for Semantic Web Data Management
, 2008
"... Despite the intense interest towards realizing the Semantic Web vision, most existing RDF data management schemes are constrained in terms of efficiency and scalability. Still, the growing popularity of the RDF format arguably calls for an effort to offset these drawbacks. Viewed from a relationalda ..."
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Cited by 188 (11 self)
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Despite the intense interest towards realizing the Semantic Web vision, most existing RDF data management schemes are constrained in terms of efficiency and scalability. Still, the growing popularity of the RDF format arguably calls for an effort to offset these drawbacks. Viewed from a relationaldatabase perspective, these constraints are derived from the very nature of the RDF data model, which is based on a triple format. Recent research has attempted to address these constraints using a vertical-partitioning approach, in which separate two-column tables are constructed for each property. However, as we show, this approach suffers from similar scalability drawbacks on queries that are not bound by RDF property value. In this paper, we propose an RDF storage scheme that uses the triple nature of RDF as an asset. This scheme enhances the vertical partitioning idea and takes it to its logical conclusion. RDF data is indexed in six possible ways, one for each possible ordering of the three RDF elements. Each instance of an RDF element is associated with two vectors; each such vector gathers elements of one of the other types, along with lists of the third-type resources attached to each vector element. Hence, a sextupleindexing scheme emerges. This format allows for quick and scalable general-purpose query processing; it confers significant advantages (up to five orders of magnitude) compared to previous approaches for RDF data management, at the price of a worst-case five-fold increase in index space. We experimentally document the advantages of our approach on real-world and synthetic data sets with practical queries.
Survey of graph database models
, 2001
"... Graph database models can be characterized as those where data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models flourished in the eighties and early nineties i ..."
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Cited by 112 (8 self)
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Graph database models can be characterized as those where data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models flourished in the eighties and early nineties in parallel to object oriented models and their influence gradually faded with the emergence of other database models, particularly the geographical, spatial, semistructured and XML. Recently, the need to manage information with inherent graph-like nature has brought back the relevance of the area. In fact, a whole new wave of applications for graph databases emerged with the development of huge networks (e.g. Web, geographical systems, transportation, telephones), and families of networks generated due to the automation of the process of data gathering (e.g. social and biological networks). The main objective of this survey is to present in a single place the work that has been done in the area of graph database modeling, concentrating in data structures, query languages and integrity constraints.
YARS2: A federated repository for querying graph structured data from the Web
- In ISWC
, 2007
"... Abstract. We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answer-ing over structured and interlinked data collected from many disparate sources on the Web. In particular, we study distributed indexing meth-ods for graph-str ..."
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Cited by 112 (11 self)
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Abstract. We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answer-ing over structured and interlinked data collected from many disparate sources on the Web. In particular, we study distributed indexing meth-ods for graph-structured data and parallel query evaluation methods on a cluster of computers. We evaluate the system on a dataset with 430 million statements collected from the Web, and provide scale-up experi-ments on 7 billion synthetically generated statements. 1
Extending faceted navigation to RDF data
- PROC. 5TH SEMANTIC WEB CONF.’, LNCS 4273
, 2006
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Ontology Ranking based on the Analysis of Concept Structures
- IN PROCS OF THE 3RD INTERNATIONAL CONFERENCE ON KNOWLEDGE CAPTURE
, 2005
"... In view of the need to provide tools to facilitate the reuse of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontol ..."
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Cited by 66 (9 self)
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In view of the need to provide tools to facilitate the reuse of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies. We apply a number of classical metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study. Our results show that AKTiveRank will have great utility although there is potential for improvement.
BRAHMS: A WorkBench RDF Store and High Performance Memory System for Semantic Association Discovery
- In ISWC
"... 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 s ..."
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Cited by 49 (6 self)
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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. 1
Ranking Ontologies with AKTiveRank
- In Proc. of the International Semantic Web Conference, ISWC
, 2006
"... Abstract. Ontology search and reuse is becoming increasingly important as the quest for methods to reduce the cost of constructing such knowledge structures continues. A number of ontology libraries and search engines are coming to existence to facilitate locating and retrieving potentially relevant ..."
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Cited by 45 (1 self)
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Abstract. Ontology search and reuse is becoming increasingly important as the quest for methods to reduce the cost of constructing such knowledge structures continues. A number of ontology libraries and search engines are coming to existence to facilitate locating and retrieving potentially relevant ontologies. The number of ontologies available for reuse is steadily growing, and so is the need for methods to evaluate and rank existing ontologies in terms of their relevance to the needs of the knowledge engineer. This paper presents AKTiveRank, a prototype system for ranking ontologies based on a number of structural metrics. 1
SPARQ2L: Towards Supporting Subgraph Extraction Queries
- in RDF Databases. Proceedings of the WWW Conference 2007, May 7-12, 2005
, 2007
"... Many applications in analytical domains often have the need to “connect the dots ” i.e., query about the structure of data. In bioinformatics for example, it is typical to want to query about interactions between proteins. The aim of such queries is to “extract ” relationships between entities i.e. ..."
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Cited by 44 (3 self)
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Many applications in analytical domains often have the need to “connect the dots ” i.e., query about the structure of data. In bioinformatics for example, it is typical to want to query about interactions between proteins. The aim of such queries is to “extract ” relationships between entities i.e. paths from a data graph. Often, such queries will specify certain constraints that qualifying results must satisfy e.g. paths involving a set of mandatory nodes. Unfortunately, most present day Semantic Web query languages including the current draft of the anticipated recommendation SPARQL, lack the ability to express queries about arbitrary path structures in data. In addition, many systems that support some limited form of path queries rely on main memory graph algorithms limiting their applicability to very large scale graphs. In this paper, we present an approach for supporting Path Extraction queries. Our proposal comprises (i) a query language SPARQ2L which extends SPARQL with path variables and path variable constraint expressions, and (ii) a novel query evaluation framework based on efficient algebraic techniques for solving path problems which allows for path queries to be efficiently evaluated on disk resident RDF graphs. The effectiveness of our proposal is demonstrated by a performance evaluation of our approach on both real world and synthetic datasets.
A Distributed Graph Engine for Web Scale RDF Data
"... Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as ..."
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Cited by 35 (1 self)
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Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as relational tables or as a bitmap matrix) to maximize one particular operation on RDF data: SPARQL query processing. In this paper, we introduce Trinity.RDF, a distributed, memory-based graph engine for web scale RDF data. Instead of managing the RDF data in triple stores or as bitmap matrices, we store RDF data in its native graph form. It achieves much better (sometimes orders of magnitude better) performance for SPARQL queries than the state-of-the-art approaches. Furthermore, since the data is stored in its native graph form, the system can support other operations (e.g., random walks, reachability) on RDF graphs as well. We conduct comprehensive experimental studies on real life, web scale RDF data to demonstrate the effectiveness of our approach. 1
