Abstract
Modern knowledge bases have matured to the extent of being capable of complex reasoning at scale. Unfortunately, wide deployment of this technology is still hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and skilled knowledge engineers are in short supply. A way around this problem could be to acquire knowledge from text. However, the current knowledge acquisition technologies for information extraction are not up to the task because logic reasoning systems are extremely sensitive to errors in the acquired knowledge, and existing techniques lack the required accuracy by too large of a margin. Because of the enormous complexity of the problem, controlled natural languages (CNLs) were proposed in the past, but even they lack high enough accuracy. Instead of tackling the general problem of text understanding, our interest is in a related, but different, area of knowledge authoring—a technology designed to enable domain experts to manually create formalized knowledge using CNL. Our approach adopts and formalizes the FrameNet methodology for representing the meaning, enables incrementally-learnable and explainable semantic parsing, and harnesses rich knowledge graphs like BabelNet in the quest to obtain unique, disambiguated meaning of CNL sentences. Our experiments show that this approach is 95.6% accurate in standardizing the semantic relations extracted from CNL sentences—far superior to alternative systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
The parameters were chosen experimentally. As part of future work, we will explore using a neural net to fine-tune this formula.
- 6.
References
Allan, K.: Natural Language Semantics. Wiley, Hoboken (2001)
Angeli, G., Premkumar, M.J.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, pp. 344–354 (2015)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: DBpedia: a nucleus for a web of open data. In: 6th International Semantic Web Conference, pp. 722–735 (2007)
Das, D., Chen, D., Martins, A.F.T., Schneider, N., Smith, N.A.: Frame-semantic parsing. Comput. Linguist. 40(1), 9–56 (2014)
Erdem, E., Erdogan, H., Öztok, U.: BIOQUERY-ASP: querying biomedical ontologies using answer set programming. In: 5th International RuleML2011@BRF Challenge, pp. 1–8 (2011)
Fuchs, N.E., Kaljurand, K., Kuhn, T.: Attempto controlled english for knowledge representation. In: Baroglio, C., Bonatti, P.A., Małuszyński, J., Marchiori, M., Polleres, A., Schaffert, S. (eds.) Reasoning Web. LNCS, vol. 5224, pp. 104–124. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85658-0_3
Fuchs, N.E., Kaljurand, K., Kuhn, T.: Discourse representation structures for ACE 6.6. Technical report 2010.0010, Department of Informatics, University of Zurich, Switzerland (2010)
Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: the potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)
Gomez, F.: The acquisition of common sense knowledge by being told: an application of NLP to itself. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 40–51. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69858-6_6
Johnson, C.R., et al.: FrameNet: Theory and Practice (2002)
Kifer, M.: Knowledge representation & reasoning with Flora-2 (2018). http://flora.sourceforge.net
Kuhn, T.: A survey and classification of controlled natural languages. Comp. Linguist. 40(1), 121–170 (2014)
Lehmann, J., Monahan, S., Nezda, L., Jung, A., Shi, Y.: LCC approaches to knowledge base population. In: 3D Text Analysis Conference, TAC, pp. 1–11. NIST, Gaithersburg (2010)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: 52nd Annual Meeting of the Association for Computational Linguistics, ACL, System Demonstrations, Baltimore, MD, USA, pp. 55–60 (2014)
Schmitz, M., Soderland, S., Bart, R., Etzioni, O.: Open language learning for information extraction. In: The Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL, Jeju Island, Korea, pp. 523–534 (2012)
McNamee, P., Dang, H.T., Simpson, H., Schone, P., Strassel, S.M.: An evaluation of technologies for knowledge base population. In: 7th International Conference on Language Resources and Evaluation (LREC 2010), Valletta, Malta, p. 4, May 2010
Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Navigli, R.: Word sense disambiguation: a survey. ACM Comput. Surv. 41(2), 10 (2009)
Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)
Palmer, M., Kingsbury, P., Gildea, D.: The PropBank: an annotated corpus of semantic roles. Comput. Linguist. 31(1), 71–106 (2005)
Ringgaard, M., Gupta, R., Pereira, F.C.N.: SLING: a framework for frame semantic parsing. CoRR 1710.07032, pp. 1–9 (2017). http://arxiv.org/abs/1710.07032
Sagonas, K., Swift, T., Warren, D.S.: XSB as an efficient deductive database engine. In: ACM Conference on the Management of Data, New York, USA, pp. 442–453 (1994)
Schuler, K.K.: VerbNet: a Broad-coverage, comprehensive Verb Lexicon. Ph.D. thesis, University of Pennsylvania (2005). aAI3179808
Schwitter, R.: English as a formal specification language. In: 13th International Workshop on Database and Expert Systems Applicationa (DEXA 2002), Aix-en-Provence, France, pp. 228–232 (2002)
Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: 31st AAAI Conference on AI, CA, USA, San Francisco, pp. 4444–4451 (2017)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Yang, G., Kifer, M., Zhao, C.: \(\cal{F}\)lora-2: a rule-based knowledge representation and inference infrastructure for the semantic web. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds.) OTM 2003. LNCS, vol. 2888, pp. 671–688. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39964-3_43
Acknowledgements
We thank Niranjan Balasubramanian and H. Andrew Schwartz for the helpful discussions. This work was partially supported by NSF grant 1814457.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Gao, T., Fodor, P., Kifer, M. (2018). Knowledge Authoring for Rule-Based Reasoning. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11230. Springer, Cham. https://doi.org/10.1007/978-3-030-02671-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-02671-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02670-7
Online ISBN: 978-3-030-02671-4
eBook Packages: Computer ScienceComputer Science (R0)