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Distributed Rule-Based Ontology Reasoning

Mutharaju, Raghava
http://orcid.org/0000-0003-2421-3935
http://rave.ohiolink.edu/etdc/view?acc_num=wright1472534764

Abstract Details

2016, Doctor of Philosophy (PhD), Wright State University, Computer Science and Engineering PhD.
The vision of the Semantic Web is to provide structure and meaning to the data on the Web. Knowledge representation and reasoning play a crucial role in accomplishing this vision. OWL (Web Ontology Language), a W3C standard, is used for representing knowledge. Reasoning over the ontologies is used to derive logical consequences. A fixed set of rules are run on an ontology iteratively until no new logical consequences can be derived. All existing reasoners run on a single machine, possibly using multiple cores. Ontologies (sometimes loosely referred to as knowledge bases) that are automatically constructed can be very large. Single machine reasoners will not be able to handle these large ontologies. They are constrained by the memory and computing resources available on a single machine. In this dissertation, we use distributed computing to find scalable approaches to ontology reasoning. In particular, we explore four approaches that use a cluster of machines for ontology reasoning -- 1) A MapReduce approach named MR-EL where reasoning happens in the form of a series of map and reduce jobs. Termination is achieved by eliminating the duplicate consequences. The MapReduce approach is simple, fault tolerant and less error-prone due to the usage of a framework that handles aspects such as communication, synchronization etc. But it is very slow and does not scale well with large ontologies. 2) Our second approach named DQuEL is a distributed version of a sequential reasoning algorithm used in the CEL reasoner. Each node in the cluster applies all of the rules and generates partial results. The reasoning process terminates when each node in the cluster has no more work to do. DQuEL works well on small and medium sized ontologies but does not perform well on large ontologies. 3) The third approach, named DistEL, is a distributed fixpoint iteration approach where each node in the cluster applies only one rule to a subset of the ontology. This happens iteratively until all of the nodes cannot generate any more new logical consequences. This is the most scalable of all of the approaches. 4) Our fourth approach, named SparkEL, is based on the Apache Spark framework where each reasoning rule is translated into a form that is suitable for Apache Spark. Several algorithmic and framework related optimizations were considered. SparkEL works very well on small and medium sized ontologies, but it does not scale to large ontologies. All four distributed reasoning systems work on a subset of OWL 2 EL which is a tractable profile of OWL with a polynomial reasoning time. Along with the description of the algorithms, optimizations and evaluation results of the four distributed reasoners, we also provide recommendations for the best choice of reasoners for different scenarios.
Pascal Hitzler, Ph.D. (Advisor)
Prabhaker Mateti, Ph.D. (Committee Member)
Derek Doran, Ph.D. (Committee Member)
Freddy Lecue, Ph.D. (Committee Member)
Frederick Maier, Ph.D. (Committee Member)
173 p.
Computer Science
distributed reasoning; ontology reasoning; distributed OWL 2 EL reasoning; Semantic Web; scalable ontology reasoning; OWL 2 EL reasoning; MapReduce reasoning; Apache Spark reasoning

Recommended Citations

Citations

  • Mutharaju, R. (2016). Distributed Rule-Based Ontology Reasoning [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472534764

    APA Style (7th edition)

  • Mutharaju, Raghava. Distributed Rule-Based Ontology Reasoning. 2016. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1472534764.

    MLA Style (8th edition)

  • Mutharaju, Raghava. "Distributed Rule-Based Ontology Reasoning." Doctoral dissertation, Wright State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1472534764

    Chicago Manual of Style (17th edition)

wright1472534764
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© 2016, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.