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Outline

Title
Abstract
Figures
Introduction
Related Work 2.1 Backward Chaining
Optimization Details & Discussion
Experimental Design
Ontology Data for the Experiments
Experimental Environment and Metrics
Evaluation Procedure
Results and Discussion
Conclusions
References
All Topics
Computer Science
Data Science

Backward Chaining Ontology Reasoning Systems with Custom Rules

Profile image of Kurt MalyKurt Maly

2016, Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion

https://doi.org/10.1145/2872518.2890521
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Abstract

In the semantic web, content is tagged with "meaning" or "semantics" to facilitate machine processing and web searching. In general, question answering systems that are built on top of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as "groundbreaking", "top researcher", and "tenurable at university x". This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized backward chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed backward chaining ontology reasoning system has better scalability than in-memory reasoning systems.

Figures (4)
Table 4 Query sets expressed in SPARQL
Table 4 Query sets expressed in SPARQL
base. Although we have optimized backward chaining in terms of scalability and efficiency, the largest knowledge base we can handle is about 30 million. 2) Secondly, all our experiments were only performed “in memory” or using Jena TDB as external storage. The employment of other external storage, such as Oracle, Jena SDB, may affect the scalability and performance of the reasoner. 3) Only one types of ontology data including datasets generated from LUBM was included in the experiments, which limited the study to academic knowledge bases with specific custom rules. Table 6. Comparison of query processing time between in-memory store and external storage on LUBM (Unit:ms) 6. CONCLUSIONS When our optimized backward chaining reasoner runs in memory wit hout any support of external storage, it can only handle up to 7 data sets from LUBM. Thus we only compared performance on 7 data sets. We are aware that working in memory has limitations wit h respect to the size of the knowledge base and the retrieved data. As Table 6 shows, our reasoner running with support of Jena TD B has twice processing time as much as running entirely in memory, that is, running entirely in memory performs better than running with support of Jena TDB. For light applications like mo bile applications, in-memory version would be a better choice.
base. Although we have optimized backward chaining in terms of scalability and efficiency, the largest knowledge base we can handle is about 30 million. 2) Secondly, all our experiments were only performed “in memory” or using Jena TDB as external storage. The employment of other external storage, such as Oracle, Jena SDB, may affect the scalability and performance of the reasoner. 3) Only one types of ontology data including datasets generated from LUBM was included in the experiments, which limited the study to academic knowledge bases with specific custom rules. Table 6. Comparison of query processing time between in-memory store and external storage on LUBM (Unit:ms) 6. CONCLUSIONS When our optimized backward chaining reasoner runs in memory wit hout any support of external storage, it can only handle up to 7 data sets from LUBM. Thus we only compared performance on 7 data sets. We are aware that working in memory has limitations wit h respect to the size of the knowledge base and the retrieved data. As Table 6 shows, our reasoner running with support of Jena TD B has twice processing time as much as running entirely in memory, that is, running entirely in memory performs better than running with support of Jena TDB. For light applications like mo bile applications, in-memory version would be a better choice.

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References (29)

  1. REFERENCES
  2. Breslin, J.G., Harth, A., Bojars, U. and Decker, S. (2005). Towards semantically-interlinked online communities. The Semantic Web: Research and Applications. Springer. pp. 500-514.
  3. Schaffert, S. (2006). IkeWiki: A semantic wiki for collaborative knowledge management. Enabling Technologies: Infrastructure for Collaborative Enterprises, (2006). WETICE'06. 15th IEEE International Workshops on, IEEE.
  4. Sure, Y. et al. (2002). OntoEdit: Collaborative ontology development for the semantic web: Springer.
  5. Wennerberg, P.O. (2005). Ontology based knowledge discovery in Social Networks. Final Report, JRC Joint Research Center 1-34.
  6. Bizer, C. et al. (2009). DBpedia-A crystallization point for the Web of Data. Web Semantics: Science, Services and Agents on the World Wide Web 7(3) 154-165.
  7. Doms, A. and Schroeder, M. (2005). GoPubMed: exploring PubMed with the gene ontology. Nucleic acids research 33(suppl 2) W783-W786.
  8. Mars, N.J. (1995). Towards Very Large Knowledge Bases: Knowledge Building & Knowledge Sharing 1995: Ios Press.
  9. Gruber, T.R. (1993). A translation approach to portable ontology specifications. Knowledge acquisition 5(2) 199- 220.
  10. Horrocks, I. and Sattler, U. (2001). Ontology reasoning in the SHOQ (D) description logic. IJCAI.
  11. Shi, H., Maly, K., Zeil, S. and Zubair, M. (2011). Comparison of Ontology Reasoning Systems Using Custom Rules. International Conference on Web Intelligence, Mining and Semantics. Sogndal, Norway.
  12. The Apache Software Foundation. (2014)a. Apache Jena [online]. Available at: http://jena.apache.org/ [Accessed February,16 2016].
  13. Clark & Parsia. (2010). Pellet:The Open Source OWL2 Reasoner [online]. Available at: http://clarkparsia.com/pellet/ [Accessed October,13 2010].
  14. Sirin, E. et al. (2007). Pellet: A practical owl-dl reasoner. Web Semantics: Science, Services and Agents on the World Wide Web 5(2) 51-53.
  15. Information Process Engineering (IPE), Institute of Applied Informatics and Formal Description Methods (AIFB) and (IMG), I.M.G. (2014). KAON2-Ontology Management for the Semantic Web [online]. Available at: http://kaon2.semanticweb.org/ [Accessed February 16 2016].
  16. Motik, B. and Studer, R. (2005). KAON2-A Scalable Reasoning Tool for the Semantic Web. Proc. 2nd ESWC. Heraklion, Greece.
  17. Xavier Lopez and Das, S. (2010). Semantic Technologies in Oracle Database 11g Release 2: Capabilities, Interfaces, Performance.
  18. Kiryakov, A., Ognyanov, D. and Manov, D. (2005). OWLIM-a pragmatic semantic repository for OWL. Web Information Systems Engineering 3807/2005 182-192.
  19. Guo, Y., Pan, Z. and Heflin, J. (2005). LUBM: A benchmark for OWL knowledge base systems. Web Semantics: Science, Services and Agents on the World Wide Web 3(2-3) 158- 182.
  20. Shi, H., Maly, K., & Zeil, S. (2014). A scalable backward chaining-based reasoner for a semantic web. International Journal on Advances in Intelligent Systems, 7(1-2), 23-38.
  21. Urbani, J., Piro, R., van Harmelen, F. and Bal, H. (2013). Hybrid reasoning on OWL RL. Semantic Web.
  22. Harris, S., Lamb, N. and Shadbolt, N. (2009). 4store: The Design and Implementation of a Clustered RDF store. Proceedings of the 5th International Workshop on Scalable Semantic Web Knowledge Base Systems. 81-96.
  23. Erling, O. and Mikhailov, I. (2009). RDF Support in the Virtuoso DBMS. Networked Knowledge-Networked Media 7-24.
  24. Horrocks, I. et al. (2004). SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission 21.
  25. Tamaki, H. and Sato, T. (1986). OLD resolution with tabulation. Third international conference on logic programming. Springer. 84-98.
  26. Ontotext. (2013). owl-sameAs-optimization [online]. Available at: http://graphdb.ontotext.com/documentation/standard/sameas- optimisation.html [Accessed February 16 2016].
  27. Shi, H., Maly, K., & Zeil, S. (2014, June). Optimized backward chaining reasoning system for a semantic web. In Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) (p. 34). ACM.
  28. Kowalski, R. and Kuehner, D. (1972). Linear resolution with selection function. Artificial Intelligence 2(3) 227-260.
  29. The Apache Software Foundation. (2014)b. Apache Jena- TDB [online]. Available at: http://jena.apache.org/documentation/tdb/.[Accessed February 16 2016].

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