Querying Ontologies in Relational Database Systems

Trißl, Silke; Leser, Ulf

doi:10.1007/11530084_7

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…As soon as nodes have multiple incoming edges, they are visited multiple times during a traversal, and thus no unique pair of pre-and postorder values can be assigned. To extend this strategy to directed, acyclic graphs (DAGs) we used an 'unfolding' technique [24], where each added 'non-tree' edge in the DAG introduces a new entry in the index structure. The target node of the additional edge as well as all its successors get additional pre-and postorder values incurring an exponential explosion in the index size as DAGs become very 'tree-unlike'.…”

Section: Interval-based Approachesmentioning

confidence: 99%

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Fast and practical indexing and querying of very large graphs

Trißl

Leser

2007

Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data

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184

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Many applications work with graph-structured data. As graphs grow in size, indexing becomes essential to ensure sufficient query performance. We present the GRIPP index structure (GRaph Indexing based on Pre-and Postorder numbering) for answering reachability queries in graphs.GRIPP requires only linear time and space. Using GRIPP, we can answer reachability queries on graphs with 5 million nodes on average in less than 5 milliseconds, which is unrivaled by previous methods. We evaluate the performance and scalability of our approach on real and synthetic random and scale-free graphs and compare our approach to existing indexing schemes. GRIPP is implemented as stored procedure inside a relational database management system and can therefore very easily be integrated into existing graphoriented applications.

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Section: Interval-based Approachesmentioning

confidence: 99%

“…Its basic idea is an adaptation of the preand postorder numbering scheme -so far only applied to trees [10] and directed, acyclic graphs (DAGs) [1,24] -to (cyclic, possibly unrooted) graphs. The GRIPP index can be computed in O(n + m) time and requires only O(n + m) space.…”

Section: Introductionmentioning

confidence: 99%

Fast and practical indexing and querying of very large graphs

Trißl

Leser

2007

Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data

Self Cite

184

163

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“…Most ontology systems are based on the XML files [2,10,18,28,34] or relational DBMS [7,11] [ 13,14,15,17,24,26,27,30] In this paper, we describe a GIS framework for geo-semantic information retrieval in mobile computing environments. We built a geographic ontology that integrates mobile user contexts with POI (point of interest) information using an ORDBMS, so the space, time, and semantic functions can work together in a fully integrated database system.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Geo-Semantic App by Combining Mobile User Contexts with Geographic Ontologies

Lee¹,

Lee²

2013

Journal of Korea Spatial Information Society

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This paper describes a GIS framework for geo-semantic information retrieval in mobile computing environments. We built geographic ontologies of POI (point of interest) and weather information for use in the combination of semantic, spatial, and temporal functions in a fully integrated database. We also implemented a geo-semantic app for Android-based smartphones that can extract more appropriate POIs in terms of user contexts and geographic ontologies and can visualize the POIs using Google Maps API (application programming interface). The feasibility tests showed our geo-semantic app can provide pertinent POI information according to mobile user contexts such as location, time, schedule, and weather. We can discover a baking CVS (convenience store) in the test of bakery search and can find out a drive-in theater for a not rainy day, which are good examples of the geo-semantic query using semantic, spatial, and temporal functions. As future work, we should need ontology-based inference systems and the LOD (linked open data) of various ontologies for more advanced sharing of geographic knowledge.

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“…Despite this need, most of the systems available to life scientists are mostly operated with visual interfaces allow only simple operations like keyword based node search, descendant enumeration, shortest path finding and neighborhood operations on graphs. This paper is an early step toward searching repositories of large ontological structures using a DAG query language, and similar in its intent as [12]. Example 1.…”

Section: Introductionmentioning

confidence: 99%

On Querying OBO Ontologies Using a DAG Pattern Query Language

Gupta

Santini

2006

Lecture Notes in Computer Science

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The Open Biomedical Ontologies (OBO) is a consortium that serves as a repository of ontologies that are structured like directed acyclic graphs. In this paper we present a language DQL for querying a database of directed acyclic graphs. The query language has a comprehension style syntax and contains a pattern specification sub-language DPL. DPL can be viewed as an extension of tree-pattern query language like XPath. The language allows extraction of nodes, paths and subgraphs from DAGs, and permits construction of result structures by composing them. We show that using such a language on OBO ontologies (such as the gene ontology), we can express more complex and scientifically valuable queries.

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Querying Ontologies in Relational Database Systems

Cited by 9 publications

References 10 publications

Fast and practical indexing and querying of very large graphs

Fast and practical indexing and querying of very large graphs

Implementation of a Geo-Semantic App by Combining Mobile User Contexts with Geographic Ontologies

On Querying OBO Ontologies Using a DAG Pattern Query Language

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