Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-28T14:56:41.074Z Has data issue: false hasContentIssue false
  • English
  • Français

A survey of large-scale reasoning on the Web of data

Published online by Cambridge University Press:  03 December 2018

Grigoris Antoniou ,
Sotiris Batsakis ,
Raghava Mutharaju ,
Jeff Z. Pan ,
Guilin Qi ,
Ilias Tachmazidis ,
Jacopo Urbani  and
Zhangquan Zhou
Grigoris Antoniou
Affiliation:
School of Computing and Engineering, University of Huddersfield, UK; e-mail: [email protected], [email protected], [email protected]
Sotiris Batsakis
Affiliation:
School of Computing and Engineering, University of Huddersfield, UK; e-mail: [email protected], [email protected], [email protected]
Raghava Mutharaju
Affiliation:
GE Global Research, USA; e-mail: [email protected]
Jeff Z. Pan
Affiliation:
Department of Computing Science, The University of Aberdeen, UK; e-mail: [email protected]
Guilin Qi
Affiliation:
School of Computer Science and Engineering, Southeast University, China; e-mail: [email protected], [email protected]
Ilias Tachmazidis
Affiliation:
School of Computing and Engineering, University of Huddersfield, UK; e-mail: [email protected], [email protected], [email protected]
Jacopo Urbani
Affiliation:
Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands; e-mail: [email protected]
Zhangquan Zhou
Affiliation:
School of Computer Science and Engineering, Southeast University, China; e-mail: [email protected], [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

As more and more data is being generated by sensor networks, social media and organizations, the Web interlinking this wealth of information becomes more complex. This is particularly true for the so-called Web of Data, in which data is semantically enriched and interlinked using ontologies. In this large and uncoordinated environment, reasoning can be used to check the consistency of the data and of associated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insights from the data. However, reasoning approaches need to be scalable in order to enable reasoning over the entire Web of Data. To address this problem, several high-performance reasoning systems, which mainly implement distributed or parallel algorithms, have been proposed in the last few years. These systems differ significantly; for instance in terms of reasoning expressivity, computational properties such as completeness, or reasoning objectives. In order to provide a first complete overview of the field, this paper reports a systematic review of such scalable reasoning approaches over various ontological languages, reporting details about the methods and over the conducted experiments. We highlight the shortcomings of these approaches and discuss some of the open problems related to performing scalable reasoning.

Type
Survey Article
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e21
Copyright
© Cambridge University Press, 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abiteboul, S., Hull, R. & Vianu, V. 1995. Foundations of Databases. Addison-Wesley.Google Scholar
Alaya, N., Yahia, S. B. & Lamolle, M. 2015. What makes ontology reasoning so arduous?: unveiling the key ontological features. In Proceedings of the 5th International Conference on Web Intelligence, Mining and Semantics, WIMS ‘15, 4:1–4:12. ACM.Google Scholar
Aluç, G., Hartig, O., Özsu, M. T. & Daudjee, K. 2014. Diversified stress testing of RDF data management systems. In The Semantic Web—ISWC 2014—13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, volume 8796 of Lecture Notes in Computer Science, Mika, P., Tudorache, T., Bernstein, A., Welty, C., Knoblock, C. A., Vrandecic, D., Groth, P. T., Noy, N. F., Janowicz, K. & Goble, C. A. (eds). Springer, 197–212.Google Scholar
Antoniou, G. & Williams, M.-A. 1997. Nonmonotonic reasoning. MIT Press.Google Scholar
Aslani, M. & Haarslev, V. 2012. Concurrent classification of OWL ontologies—an empirical evaluation. In Proceedings of the 2012 International Workshop on Description Logics, DL-2012, Rome, Italy, June 7–10, 2012, CEUR Workshop Proceedings 846. CEUR-WS.org.Google Scholar
Baader, F. & Suntisrivaraporn, B. 2008. Debugging SNOMED CT using axiom pinpointing in the description logic EL+. In Proceedings of the Third International Conference on Knowledge Representation in Medicine, Phoenix, Arizona, USA, May 31st–June 2nd, 2008.Google Scholar
Baader, F., Brandt, S. & Lutz, C. 2005. Pushing the EL envelope. In Proceedings of IJCAI, 364–369. Professional Book Center.Google Scholar
Baader, F., Brandt, S. & Lutz, C. 2008. Pushing the EL envelope further. In Proceedings of OWLED. CEUR-WS.org.Google Scholar
Baader, F., Lutz, C. & Suntisrivaraporn, B. 2005. Is tractable reasoning in extensions of the description logic EL useful in practice? In Proceedings of the 2005 International Workshop on Methods for Modalities (M4M-05).Google Scholar
Baader, F., Lutz, C. & Suntisrivaraporn, B. 2006. Efficient reasoning in EL + . In Proceedings of DL.Google Scholar
Barbieri, D. F., Braga, D., Ceri, S., Valle, E. D. & Grossniklaus, M. 2010. Incremental reasoning on streams and rich background knowledge. In The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30 - June 3, 2010, Proceedings, Part I, volume 6088 of Lecture Notes in Computer Science, Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L. & Tudorache, T. (eds). Springer, 1–15.Google Scholar
Bazoobandi, H. R., Urbani, J., van Harmelen, F. & Bal, H. E. 2017. An empirical study on how the distribution of ontologies affects reasoning on the web. In The Semantic Web—ISWC 2017—16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I, 69–86. Springer.Google Scholar
Benedikt, M., Konstantinidis, G., Mecca, G., Motik, B., Papotti, P., Santoro, D. & Tsamoura, E. 2017. Benchmarking the chase. In Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 37–52. ACM.Google Scholar
Billington, D., Antoniou, G., Governatori, G. & Maher, M. 2010. An inclusion theorem for defeasible logics. ACM Transactions on Computational Logic 12, 6:1–6:27.Google Scholar
Bizer, C. & Schultz, A. 2009. The Berlin SPARQL benchmark. International Journal on Semantic Web and Information Systems 5, 1–24.Google Scholar
Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R. & Hellmann, S. 2009. DBpedia—A crystallization point for the Web of data. Journal of Web Semantics 7, 154–165.Google Scholar
Bonatti, P. A., Hogan, A., Polleres, A. & Sauro, L. 2011. Robust and scalable linked data reasoning incorporating provenance and trust annotations. Journal of Web Semantics 9, 165–201.Google Scholar
Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M. & Rosati, R. 2007. Tractable reasoning and efficient query answering in description logics: The DL-Lite family. Journal of. Automated Reasoning 39, 385–429.Google Scholar
Dean, J. & Ghemawat, S. 2004. MapReduce: simplified data processing on large clusters. In OSDI'04: Proceedings of the 6th Symposium on Operating Systems Design and Implementation. USENIX Association.Google Scholar
Dong, X., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S. & Zhang, W. 2014. Knowledge vault: a Web-scale approach to probabilistic knowledge fusion. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ‘14, New York, NY, USA—August 24 - 27, 2014, Macskassy, S. A., Perlich, C., Leskovec, J., Wang, W. & Ghani, R. (eds). ACM, 601–610.Google Scholar
Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Valle, E. D., Fischer, F., Huang, Z., Kiryakov, A., Lee, T. K., Schooler, L., Tresp, V., Wesner, S., Witbrock, M. & Zhong, N. 2008. Towards larkc: a platform for web-scale reasoning. In Proceedings of the 2th IEEE International Conference on Semantic Computing (ICSC 2008), August 4–7, 2008, Santa Clara, California, USA, 524–529. IEEE Computer Society.Google Scholar
Flouris, G., Konstantinidis, G., Antoniou, G. & Christophides, V. 2013. Formal foundations for RDF/S KB evolution. Knowledge and Information Systems. 35, 153–191.Google Scholar
Gelder, A. V., Ross, K. A. & Schlipf, J. S. 1991. The well-founded semantics for general logic programs. Journal of the ACM 38, 620–650.Google Scholar
Gelfond, M. 2008. Chapter 7 answer sets. In Handbook of Knowledge Representation, volume 3 of Foundations of Artificial Intelligence , F. van Harmelen, V. L. & Porter, B. (eds). Elsevier, 285–316.Google Scholar
Glimm, B., Horrocks, I., Motik, B., Stoilos, G. & Wang, Z. 2014. HermiT: an OWL 2 reasoner. Journal of Automated Reasoning 53, 245–269.Google Scholar
Gonçalves, R. S., Parsia, B. & Sattler, U. 2012. Performance heterogeneity and approximate reasoning in description logic ontologies. In The Semantic Web—ISWC 2012—11th International Semantic Web Conference, Boston, MA, USA, November 11-15, 2012, Proceedings, Part I, volume 7649 of Lecture Notes in Computer Science, Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J. X., Hendler, J., Schreiber, G., Bernstein, A. & Blomqvist, E. (eds). Springer, 82–98.Google Scholar
Goodman, E. L., Jimenez, E., Mizell, D., al Saffar, S., Adolf, B. & Haglin, D. 2011. High-performance computing applied to semantic databases. In Proceedings of the 8th Extended Semantic Web Conference on The Semanic Web: Research and Applications Part II, ESWC'11, 31–45. Springer-Verlag.Google Scholar
Gottlob, G., Manna, M. & Pieris, A. 2014. Polynomial combined rewritings for existential rules. In Proceedings of the 14th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2014. AAAI Press.Google Scholar
Guo, Y., Pan, Z. & Heflin, J. 2005. LUBM: a benchmark for OWL knowledge base systems. Journal of Web Semantics 3, 158–182.Google Scholar
Gupta, A., Mumick, I. S. & Subrahmanian, V. S. 1993. Maintaining views incrementally. ACM SIGMOD Record 22, 157–166.Google Scholar
Hayes, P. 2004. Rdf semantics. In W3C Recommendation. https://www.w3.org/TR/rdf-mt/.Google Scholar
Heino, N. & Pan, J. Z. 2012. Rdfs reasoning on massively parallel hardware. In Proceedings of the 11th International Conference on The Semantic Web, Part I, ISWC'12, 133–148. Springer-Verlag.Google Scholar
Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P. F. & Rudolph, S. (eds). 2009. OWL 2 Web Ontology Language: Primer. W3C Recommendation. Available from http://www.w3.org/TR/owl2-primer/.Google Scholar
Hoeksema, J. & Kotoulas, S. 2011. High-performance distributed stream reasoning using S4. In Proccedings of the 1st International Workshop on Ordering and Reasoning.Google Scholar
Hogan, A., Pan, J. Z., Polleres, A. & Decker, S. 2010. SAOR: template rule optimisations for distributed reasoning over 1 billion linked data triples. In Proceedings of the 9th International Semantic Web Conference on The Semantic Web Part I, ISWC'10, 337–353. Springer-Verlag.Google Scholar
Horrocks, I. 2008. Ontologies and the semantic web. Communications of the ACM 51, 58–67.Google Scholar
Huang, S. S., Green, T. J. & Loo, B. T. 2011. Datalog and emerging applications: an interactive tutorial. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, June 12–16, 2011, 1213–1216. ACM.Google Scholar
Kang, Y., Pan, J. Z., Krishnaswamy, S., Sawangphol, W. & Li, Y. 2014. How long will it take? Accurate prediction of ontology reasoning performance. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27–31, 2014, Québec City, Québec, Canada., Brodley, C. E. & Stone, P. (eds). AAAI Press, 80–86.Google Scholar
Kaoudi, Z., Miliaraki, I. & Koubarakis, M. 2008. RDFS reasoning and query answering on top of DHTs. In Proceedings of the 7th International Semantic Web Conference, ISWC 2008, Karlsruhe, Germany, October 26–30, 2008, Lecture Notes in Computer Science 5318, Sheth, A. P., et al. (eds). Springer, 499–516.Google Scholar
Kazakov, Y. & Klinov, P. 2013. Incremental reasoning in EL+ without bookkeeping. In Informal Proceedings of the 26th International Workshop on Description Logics, 294–315. CEUR-WS.org.Google Scholar
Kazakov, Y., Krötzsch, M. & Simancik, F. 2011. Concurrent classification of EL ontologies. In 10th International Semantic Web Conference, Bonn, Germany, October 23–27, Lecture Notes in Computer Science 7031, 305–320. Springer.Google Scholar
Kim, J.-M. & Park, Y.-T. 2015. Scalable owl-horst ontology reasoning using spark. In 2015 International Conference on Big Data and Smart Computing (BIGCOMP), 79–86. IEEE.Google Scholar
Kolovski, V., Wu, Z. & Eadon, G. 2010. Optimizing enterprise-scale OWL 2 RL reasoning in a relational database system. In International Semantic Web Conference (1), Lecture Notes in Computer Science 6496, Patel-Schneider, P. F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J. Z., Horrocks, I. & Glimm, B. (eds). Springer, 436–452.Google Scholar
Kotoulas, S., Oren, E. & van Harmelen, F. 2010. Mind the data skew: distributed inferencing by speeddating in elastic regions. In Proceedings of the 19th International Conference on World Wide Web, WWW ‘10, 531–540. ACM.Google Scholar
Krötzsch, M. 2011. Efficient rule-based inferencing for OWL EL. In Proceedings of IJCAI, 2668–2673. IJCAI/AAAI.Google Scholar
Lécué, F., Tallevi-Diotallevi, S., Hayes, J., Tucker, R., Bicer, V., Sbodio, M. L. & Tommasi, P. 2014. Smart traffic analytics in the semantic web with STAR-CITY: scenarios, system and lessons learned in dublin city. Journal of Web Semantics 27, 26–33.Google Scholar
Lembo, D., Santarelli, V. & Savo, D. F. 2013. A graph-based approach for classifying OWL 2 QL ontologies. In Informal Proceedings of the 26th International Workshop on Description Logics, Ulm, Germany, July 23–26, 2013, 747–759.Google Scholar
Liebig, T. & Müller, F. 2007. Parallelizing tableaux-based description logic reasoning. In On the Move to Meaningful Internet Systems 2007: OTM 2007, volume 4806 of Lecture Notes in Computer Science , Meersman, R., Tari, Z. & Herrero, P. (eds). Springer, 1135–1144.Google Scholar
Liebig, T., Steigmiller, A. & Noppens, O. 2010. Scalability via parallelization of OWL reasoning. In Proceedings of the 4th International Workshop on New Forms of Reasoning for the Semantic Web: Scalable and Dynamic (NeFoRS 2010).Google Scholar
Liu, B., Huang, K., Li, J. & Zhou, M. 2015. An incremental and distributed inference method for large-scale ontologies based on mapreduce paradigm. IEEE Transcations on Cybernetics 45, 53–64.Google Scholar
Ma, L., Yang, Y., Qiu, Z., Xie, G. T., Pan, Y. & Liu, S. 2006. Towards a complete OWL ontology benchmark. In The Semantic Web: Research and Applications, 3rd European Semantic Web Conference, ESWC 2006, Budva, Montenegro, June 11-14, 2006, Proceedings, Lecture Notes in Computer Science 4011, Sure, Y. & Domingue, J. (eds). Springer, 125–139.Google Scholar
Mahdisoltani, F., Biega, J. & Suchanek, F. M. 2015. YAGO3: a knowledge base from multilingual wikipedias. In CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 4–7, 2015, Online Proceedings. www.cidrdb.org.Google Scholar
Maier, F., Mutharaju, R. & Hitzler, P. 2010. Distributed reasoning with EL++ using MapReduce. Technical report, Department of Computer Science, Wright State University, USA. http://knoesis.wright.edu/pascal/resources/publications/elpp-mapreduce2010.pdf.Google Scholar
Margara, A., Urbani, J., van Harmelen, F. & Bal, H. 2014. Streaming the web: reasoning over dynamic data. Web Semantics: Science, Services and Agents on the World Wide Web 25, 24–44.Google Scholar
Martínez-Angeles, C. A., Dutra, I., Costa, V. S. & Buenabad-Chavez, J. 2013. A datalog engine for GPUs. In Proceedings of the 22nd International Workshop on Functional and (Constraint) Logic Programming (WFLP 2013), Hanus, M. (ed). 239–253.Google Scholar
Matentzoglu, N., Bail, S. & Parsia, B. 2013. A snapshot of the OWL Web. In The Semantic Web—ISWC 2013—12th International Semantic Web Conference, Sydney, NSW, Australia, October 21–25, 2013, Proceedings, Part I, volume 8218 of Lecture Notes in Computer Science, Alani, H., Kagal, L., Fokoue, A., Groth, P. T., Biemann, C., Parreira, J. X., Aroyo, L., Noy, N. F., Welty, C. & Janowicz, K. (eds). Springer, 331–346.Google Scholar
Meditskos, G. & Bassiliades, N. 2010. Dlejena: A practical forward-chaining OWL 2 RL reasoner combining jena and pellet. Journal of Web Semantics 8, 89–94.Google Scholar
Mitchell, T. M., CohenW. W., Jr., E. R. H. W. W., Jr., E. R. H., Talukdar, P. P., Betteridge, J., Carlson, A., Mishra, B. D., Gardner, M., Kisiel, B., Krishnamurthy, J., Lao, N., Mazaitis, K., Mohamed, T., Nakashole, N., Platanios, E. A., Ritter, A., Samadi, M., Settles, B., Wang, R. C., Wijaya, D. T., Gupta, A., Chen, X., Saparov, A., Greaves, M. & Welling, J. 2015. Never-ending learning. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA., Bonet, B. & Koenig, S. (eds). AAAI Press, 2302–2310.Google Scholar
Morsey, M., Lehmann, J., Auer, S. & Ngomo, A. N. 2011. DBpedia SPARQL benchmark—performance assessment with real queries on real data. In The Semantic Web—ISWC 2011—10th International Semantic Web Conference, Bonn, Germany, October 23–27, 2011, Proceedings, Part I,Lecture Notes in Computer Science 7031, Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N. F. & Blomqvist, E. (eds). Springer, 454–469.Google Scholar
Motik, B., Nenov, Y., Piro, R. & Horrocks, I. 2014. Parallel materialisation of datalog programs in main-memory RDF databases. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27–31, 2014, Qébec City, Qébec, Canada. AAAI Press.Google Scholar
Motik, B., Nenov, Y., Piro, R. & Horrocks, I. 2015. Incremental Update of Datalog Materialisation: the Backward/Forward Algorithm. AAAI Press.Google Scholar
Muñoz, S., Pérez, J. & Gutierrez, C. 2009. Simple and efficient minimal rdfs. Web Semantics: Science, Services and Agents on the World Wide Web 7, 220–234.Google Scholar
Mutharaju, R., Hitzler, P., Mateti, P. & Lécué, F. 2015. Distributed and scalable OWL EL reasoning. In The Semantic Web. Latest Advances and New Domains—12th Extended Semantic Web Conference, ESWC 2015, Portoroz, Slovenia, May 31–June 4, 2015. Proceedings, Lecture Notes in Computer Science 9088, Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré -Mauroux, P. & Zimmermann, A. (eds). Springer, 88–103.Google Scholar
Mutharaju, R., Hitzler, P. & Mateti, P. 2013. DistEL: a distributed EL+ ontology classifier. In Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, Sydney, Australia, CEUR Workshop Proceedings 1046, Liebig, T. & Fokoue, A. (eds). CEUR-WS.org, 17–32.Google Scholar
Mutharaju, R., Hitzler, P. & Mateti, P. 2014. Distributed OWL EL reasoning: the story so far. In Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy, volume 1261 of CEUR Workshop Proceedings, Liebig, T. & Fokoue, A. (eds). CEUR-WS.org, 61–76.Google Scholar
Mutharaju, R., Maier, F. & Hitzler, P. 2010. A MapReduce algorithm for EL+. In Proceedings of the 23rd International Workshop on Description Logics (DL 2010), Waterloo, Ontario, Canada, May 4–7, 2010, CEUR Workshop Proceedings 573. CEUR-WS.org.Google Scholar
Mutharaju, R., Mateti, P. & Hitzler, P. 2015. Towards a rule based distributed OWL reasoning framework. In Ontology Engineering—12th International Experiences and Directions Workshop on OWL, OWLED 2015, co-located with ISWC 2015, Bethlehem, PA, USA, October 9–10, 2015, Revised Selected Papers, Lecture Notes in Computer Science 9557, Tamma, V. A. M., Dragoni, M., Gonçalves, R. & Lawrynowicz, A. (eds). Springer, 87–92.Google Scholar
Mutharaju, R. 2016. Distributed Rule-Based Ontology Reasoning. PhD Dissertation, Wright State University.Google Scholar
Niu, F., Zhang, C., Ré, C. & Shavlik, J. W. 2012. Elementary: large-scale knowledge-base construction via machine learning and statistical inference. International Journal on Semantic Web and Information Systems 8, 42–73.Google Scholar
Oren, E., Kotoulas, S., Anadiotis, G., Siebes, R., ten Teije, A. & van Harmelen, F. 2009. Marvin: distributed reasoning over large-scale semantic Web data. Web Semantics: Science, Services and Agents on the World Wide Web 7, 305–316.Google Scholar
Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E. & Phillips, J. C. 2008. GPU Computing. Proceedings of the IEEE 96, 879–899.Google Scholar
Patel-Schneider, P. 2012a. Comments on WebPIE. Web Semantics: Science, Services and Agents on the World Wide Web 15, 69–70.Google Scholar
Patel-Schneider, P. F. 2012b. Reasoning in RDFS is inherently serial, at least in the worst case. In Proceedings of the ISWC 2012 Posters & Demonstrations Track, Boston, USA, November 11–15, 2012, CEUR Workshop Proceedings 914, Glimm, B. & Huynh, D. (eds). CEUR-WS.org.Google Scholar
Ren, Y. & Pan, J. Z. 2011. Optimising ontology stream reasoning with truth maintenance system. In Proceedings of the ACM Conference on Information and Knowledge Management (CIKM 2011), 831–836. ACM.Google Scholar
Ren, Y., Pan, J. Z. & Lee, K. 2012. Optimising parallel ABox reasoning of EL ontologies. In Proceedings of the 2012 International Workshop on Description Logics, DL-2012, Rome, Italy, June 7–10, 2012, CEUR Workshop Proceedings 846. CEUR-WS.org.Google Scholar
Ren, Y., Pan, J. Z. & Zhao, Y. 2010. Soundness preserving approximation for TBox reasoning. In the Proceedings of the 25th AAAI Conference Conference (AAAI2010). AAAI Press.Google Scholar
Salvadores, M., Correndo, G., Harris, S., Gibbins, N. & Shadbolt, N. 2011. The design and implementation of RDFS backward reasoning in 4Store. In Proceedings of the 8th Extended Semantic Web Conference on The Semanic Web: Research and Applications—Part II, ESWC'11, 139–153. Springer-Verlag.Google Scholar
Schlicht, A. & Stuckenschmidt, H. 2008. Distributed resolution for ALC. In Proceedings of the 21st International Workshop on Description Logics (DL2008), Dresden, Germany, May 13–16, CEUR Workshop Proceedings 353. CEUR-WS.org.Google Scholar
Schlicht, A. & Stuckenschmidt, H. 2009. Distributed resolution for expressive ontology networks. In Proceedings of the Third International Conference on Web Reasoning and Rule Systems, RR 2009, Chantilly, VA, USA, October 25–26, 2009, Lecture Notes in Computer Science 5837, 87–101. Springer.Google Scholar
Schlicht, A. & Stuckenschmidt, H. 2011. MapResolve. In Web Reasoning and Rule Systems—5th International Conference, RR 2011, Galway, Ireland, August 29–30, 2011, Lecture Notes in Computer Science 6902, 294–299. Springer.Google Scholar
Schmidt, M., Hornung, T., Meier, M., Pinkel, C. & Lausen, G. 2009. Sp2bench: a SPARQL performance benchmark. In Semantic Web Information Management, — A Model-Based Perspective, Virgilio, R. D., Giunchiglia, F. & Tanca, L. eds). Springer, 371–393.Google Scholar
Schollmeier, R. 2001. A definition of peer-to-peer networking for the classification of peer-to-peer architectures and applications. In First International Conference on Peer-to-Peer Computing, 101–102. IEEE Computer Society.Google Scholar
Sirin, E., Parsia, B., Grau, B. C., Kalyanpur, A. & Katz, Y. 2007. Pellet: a practical OWL-DL reasoner. Journal of Web Semantics 5, 51–53.Google Scholar
Soma, R. & Prasanna, V. K. 2008. Parallel inferencing for OWL Knowledge Bases. In Proceedings of the 2008 37th International Conference on Parallel Processing, ICPP ‘08, 75–82. IEEE Computer Society.Google Scholar
Steigmiller, A., Liebig, T. & Glimm, B. 2014. Konclude: system description. Journal of Web Semantics (JWS) 27, 78–85.Google Scholar
Stoilos, G., Stamou, G. B. & Pan, J. Z. 2008. Classifying fuzzy subsumption in fuzzy-EL + . In Description Logics. CEUR-WS.org.Google Scholar
Suntisrivaraporn, B., Qi, G., Ji, Q. & Haase, P. 2008. A modularization-based approach to finding all justifications for OWL DL entailments. In The Semantic Web, 3rd Asian Semantic Web Conference, ASWC 2008, Bangkok, Thailand, December 8–11, 2008. Proceedings, 1–15. Springer.Google Scholar
Tachmazidis, I. & Antoniou, G. 2013. Computing the stratified semantics of logic programs over big data through mass parallelization. In Theory, Practice, and Applications of Rules on the Web—7th International Symposium, RuleML 2013, Seattle, WA, USA, July 11–13, 2013. Proceedings, Lecture Notes in Computer Science 8035, Morgenstern, L., Stefaneas, P. S., Lévy, F., Wyner, A. & Paschke, A. (eds). Springer, 188–202.Google Scholar
Tachmazidis, I., Antoniou, G. & Faber, W. 2014. Efficient computation of the well-founded semantics over big data. TPLP 14, 445–459.Google Scholar
Tachmazidis, I., Antoniou, G., Flouris, G., Kotoulas, S. & McCluskey, L. 2012a. Large-scale parallel stratified defeasible reasoning. In ECAI 2012—20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track, Montpellier, France, August 27-31 , 2012, volume 242 of Frontiers in Artificial Intelligence and Applications, Raedt, L. D., Bessière, C., Dubois, D., Doherty, P., Frasconi, P., Heintz, F. & Lucas, P. J. F. (eds). 738–743. IOS Press.Google Scholar
Tachmazidis, I., Antoniou, G., Flouris, G. & Kotoulas, S. 2012b. Towards parallel nonmonotonic reasoning with billions of facts. In Principles of Knowledge Representation and Reasoning: Proceedings of the Thirteenth International Conference, KR 2012, Rome, Italy, June 10–14, 2012, Brewka, G., Eiter, T. & McIlraith, S. A. (eds). AAAI Press.Google Scholar
ter Horst, H. J. 2005. Combining RDF and part of OWL with rules: semantics, decidability, complexity. In The Semantic Web—ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10, 2005, Proceedings, 668–684.Google Scholar
Thomas, E., Pan, J. Z. & Ren, Y. 2010. TrOWL: tractable OWL 2 reasoning infrastructure. In the Proceedings of the Extended Semantic Web Conference (ESWC2010). Springer.Google Scholar
Ullman, J. D. 1989. Principles of Database and Knowledge-Base Systems, II. Computer Science Press.Google Scholar
Urbani, J. & Jacobs, C. 2015. RDF-SQ: Mixing Parallel and Sequential Computation for Top-Down OWL RL Inference. Springer International Publishing, 125–138.Google Scholar
Urbani, J., Kotoulas, S., Oren, E. & van Harmelen, F. 2009. Scalable distributed reasoning using mapreduce. In The Semantic Web—ISWC 2009, 8th International Semantic Web Conference, ISWC 2009, Chantilly, VA, USA, October 25–29, 2009. Proceedings, 634–649.Google Scholar
Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F. & Bal, H. E. 2010. OWL reasoning with WebPIE: calculating the closure of 100 billion triples. In Proceedings of the 8th Extended Semantic Web Conference (ESWC2010), Heraklion, Greece, May 30–June 3, 2010. Springer.Google Scholar
Urbani, J., van Harmelen, F., Schlobach, S. & Bal, H. E. 2011. QueryPIE: backward reasoning for OWL Horst over very large knowledge bases. In 10th International Semantic Web Conference, Bonn, Germany, October 23–27, 2011, Lecture Notes in Computer Science 7031, 730–745. Springer.Google Scholar
Urbani, J., Kotoulas, S., Maassen, J., van Harmelen, F. & Bal, H. 2012a. Response to comments on WebPIE. Web Semantics: Science, Services and Agents on the World Wide Web 15, 71–72.Google Scholar
Urbani, J., Kotoulas, S., Maassen, J., Van Harmelen, F. & Bal, H. 2012b. WebPIE: a Web-scale parallel inference engine using MapReduce. Journal of Web Semantics 10, 59–75.Google Scholar
Urbani, J., Margara, A., Jacobs, C. J. H., van Harmelen, F. & Bal, H. E. 2013. DynamiTE: parallel materialization of dynamic RDF data. In International Semantic Web Conference (1), Lecture Notes in Computer Science 8218, Alani, H., Kagal, L., Fokoue, A., Groth, P. T., Biemann, C., Parreira, J. X., Aroyo, L., Noy, N. F., Welty, C. & Janowicz, K. (eds). Springer, 657–672.Google Scholar
Urbani, J., Margara, A., Jacobs, C., Voulgaris, S. & Bal, H. 2014. AJIRA: a lightweight distributed middleware for MapReduce and stream processing. In Distributed Computing Systems (ICDCS), 2014 IEEE 34th International Conference on, 545–554. IEEE.Google Scholar
Urbani, J., Piro, R., van Harmelen, F. & Bal, H. 2014. Hybrid reasoning on OWL RL. Semantic Web 5, 423–447.Google Scholar
Urbani, J., Jacobs, C. & Kr¶tzsch, M. 2016. Column-oriented datalog materialization for large knowledge graphs. In Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press.Google Scholar
Volz, R., Staab, S. & Motik, B. 2005. Incrementally maintaining materializations of ontologies stored in logic databases. Journal of Data Semantics 2, 1–34.Google Scholar
Vrandečić, D. & Krötzsch, M. 2014. Wikidata: a free collaborative knowledge base. Communications ACM 57, 78–85.Google Scholar
Weaver, J. & Hendler, J. A. 2009. Parallel materialization of the finite RDFS closure for hundreds of millions of triples. In 8th International Semantic Web Conference, ISWC 2009, Chantilly, VA, USA, October 25–29, 2009, Lecture Notes in Computer Science 5823, 682–697. Springer.Google Scholar
Wu, K. & Haarslev, V. 2012. A parallel reasoner for the description logic ALC. In Proceedings of the 2012 International Workshop on Description Logics, DL-2012, Rome, Italy, June 7–10, 2012, CEUR Workshop Proceedings 846. CEUR-WS.org.Google Scholar
Wu, G., Qi, G. & Du, J. 2011. Finding all justifications of OWL entailments using TMS and mapreduce. In Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24–28, 2011, 1425–1434. ACM.Google Scholar
Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S. & Stoica, I. 2010. Spark: cluster computing with working sets. In Proceedings of the 2nd USENIX conference on Hot topics in cloud computing, 10–10. USENIX Association.Google Scholar
Zhang, Y., Pham, M., Corcho, O. & Calbimonte, J. 2012. SRBench: a streaming RDF/SPARQL Benchmark. In The Semantic Web—ISWC 2012—11th International Semantic Web Conference, Boston, MA, USA, November 11–15, 2012, Proceedings, Part I Lecture Notes in Computer Science 7649, Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J. X., Hendler, J., Schreiber, G., Bernstein, A. & Blomqvist, E. (eds). Springer, 641–657.Google Scholar
Zhou, Z., Qi, G., Liu, C., Hitzler, P. & Mutharaju, R. 2012. Reasoning with fuzzy-EL+ ontologies using MapReduce. In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI 2012), Frontiers in Artificial Intelligence and Applications 242, 933–934. IOS Press.Google Scholar
Zhou, Z., Qi, G., Liu, C., Hitzler, P. & Mutharaju, R. 2013. Scale reasoning with fuzzy-EL+ ontologies based on MapReduce. In Proceedings of the IJCAI-2013 Workshop on Weighted Logics for Artificial Intelligence, WL4AI-2013, Beijing, China, August 2013, 87–93.Google Scholar
Zhou, Z., Qi, G., Liu, C., Mutharaju, R. & Hitzler, P. 2016. Reasoning with large scale OWL 2 EL ontologies based on MapReduce. In Proceedings of the 18th Asia Pacific Web Conference, Suzhou, China. Springer.Google Scholar
Zhou, Z., Qi, G. & Suntisrivaraporn, B. 2013. A new method of finding all justifications in OWL 2 EL. In 2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013, Atlanta, GA, USA, November 17–20, 2013, 213–220. IEEE Computer Society.Google Scholar