SK-languages as a Powerful and Flexible Semantic Formalism for the Systems of Cross-Lingual Intelligent Information Access

Vladimir A. Fomichov

Abstract


The first starting point of this paper is the broadly accepted idea of employing, as a promising methodology, an artificial semantic language-intermediary for the realization of automatic cross-lingual intelligent information access to natural language (NL) texts on the Web. The second one is the emergence in computational semantics during 2013-2016 of great interest in the semantic formalism (more exactly, notation) called Abstract Meaning Representation (AMR). This formalism was introduced in 2013 in an ACL publication by a group consisting of ten researchers from UK and USA. This paper shows that much broader prospects for creating semantic languages-intermediaries in comparison with AMR are opened by the theory of K-representations (TKR), developed by V. A. Fomichov. The basic mathematical model of TKR describes the regularities of NL structured meanings. The mathematical essence is that this model introduces a system consisting of ten partial operations on conceptual structures. Initial version of this model was published in 1996 in Informatica (Slovenia). The second version of the model (stated in a monograph released by Springer in 2010) defines a class of formal languages called SK-languages (standard knowledge languages). It is demonstrated that SK-languages allow us to simulate all expressive mechanisms of AMR. The advantages in comparison with AMR are, in particular, the possibilities to construct semantic representations of compound infinitive constructions (expressing goals, commitments, etc), of compound descriptions of notions and sets, and of complex discourses and knowledge pieces.


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References

Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., Schneider, N. (2013). Abstract Meaning Representation for Sembanking. In: Proceedings of the 7th ACL Linguistic Annotation Workshop and Interoperability with Discourse, Sofia, Bulgaria, August 8-9, 2013 (2013)(www.aclweb.org/anthology/W13-2322; retrieved 2016-03-12)

Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Koehn, P., Palmer, M., Schneider, N. (2015). Abstract Meaning Representation (AMR) 1.2.2 Specification; github.com/amrisi/amr-guidelines/blob/master/amr.md.

Blanco E., Moldovan D. (2014). Leveraging verb-argument structures to infer semantic relations. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, April 26-30, 2014. ACL, pp. 145-154.

Bordes, A., Glorot, X., Westion, J., Bengio, Y. (2012). Joint learning of words and meaning representations for open-text semantic parsing. Proc. of the 15th Intern. Conf. on Artificial Intelligence and Statistics (AISTATS) 2012, LasPalmas, Canary Islands. Vol. 22, pp. 127-135.

Buitelaar P., Choi K.-S. Cimiano P., Hovy E. H. (Eds.)(2012). Report from Dagstuhl Seminar 12362 “The Multilingual Semantic Web” (2 – 9 September, 2012). Schloss Dagstuhl: Leibniz-Zentrum fuer Informatik.

Cambridge Semantics Inc., The Smart Data Company, Web-page (retrieved 14.10.2016).; http://www.cambridgesemantics.com/semantic-university/nlp-and-semantic-web#.

Cimiano, P., Haase, P. et al. (2008). Towards Portable Natural Language Interfaces to Knowledge Bases – the Case of the ORAKEL System. Data and nowledge Engineering, Vol. 65, No. 2., pp. 325-354.

Clark P., Harrison P. (2008). Boeing’s NLP System and the Challenges of Semantic Representation. In: Proc. SIGSEM Symposium on Text Processing (STEP’08), Venice, Italy, ACL, pp. 263-276.

Das, D., Chen, D., Martins, A. F. T., Schneider, N., Smith, N. A. (2014). Frame-Semantic Parsing. Computational Linguistics, Vol. 40, No. 1, pp. 9-56.

Fillmore C., Johnson C. R., Petruck M. R. L. (2003). Background to FrameNet. International Journal of Lexicography, Vol. 16, No. 3, pp. 235-250.

Fomitchov, V. A. (1984). Formal systems for natural language man-machine interaction modelling. Artificial Intelligence. Proc. of the IFAC Symposium. Leningrad, USSR, 4-6 Oct. 1983, Ponomaryov, V.M. (ed.), Oxford, UK, Pergamon Press Ltd., New York, Pergamon Press Inc., 1984, pp. 203-207 (IFAC Proc. Series, 1984, No. 9).

Fomichov, V. A. (1988) Representing Information by Means of K-calculuses. Textbook. Moscow, The Moscow Institute of Electronic Engineering, 1988.

Fomichov, V. A. (1992). Mathematical models of natural-language-processing systems as cybernetic models of a new kind. Cybernetica. Quarterly Review of the International Association for Cybernetics (Belgium, Namur).,Vol. 35, No. 1, pp 63–91.

Fomichov, V. A. (1993). Towards a mathematical theory of natural language communication. Informatica. An Intern. Journal of Computing and Informatics (Slovenia), ol. 17, No. 1, pp. 21–34.

Fomichov, V. A. (1993). K-calculuses and K-languages as powerful formal means to design intelligent systems processing medical texts. Cybernetica (Belgium), 993, Vol. XXXVI, No. 2, pp.161-182.

Fomichov, V. A. (1994). Integral Formal Semantics and the design of legal full-text databases. Cybernetica (Belgium), ol. XXXVII, No. 2, pp. 145-177.

Fomichov, V. A. (1996). A mathematical model for describing structured items of conceptual level. Informatica. An International Journal of Computing and Informatics (Slovenia), Vol. 20, No. 1. pp. 5–32.

Fomichov V. A. (1998). Theory of restricted K-calculuses as a comprehensive framework for constructing agent communication languages. Fomichov, V.A., Zeleznikar, A.P. (eds.), Special Issue on NLP and Multi-Agent Systems. Informatica. An Intern. Journal of Computing and Informatics (Slovenia), Vol. 22, No. 4, pp. 451-463.

Fomichov, V. A. (2000). An ontological mathematical framework for electronic commerce and semantically-structured Web. Zhang, Y., Fomichov, V.A., Zeleznikar, A.P. (Eds.) Special Issue on Database, Web, and Cooperative Systems. Informatica. An Intern. Journal of Computing and Informatics (Slovenia), Vol. 24, No. 1, pp. 39-49.

Fomichov, V. A. (2002). Theory of K-calculuses as a powerful and flexible mathematical framework for building ontologies and designing natural language-processing systems. Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (Eds.), Flexible Query Answering Systems, 5th Intern. Conference, FQAS 2002, Proceedings, Lecture Notes in Artificial Intelligence, Vol. 2522, Springer: Berlin, Heidelberg, New York, pp. 183-196.

Fomichov, V.A. (2005a). The Formalization of Designing Natural Language Processing Systems. Moscow: MAX Press, 368 p. (in Russian).

Fomichov, V.A. (2005b). A new method of transforming natural language texts into semantic representations. Informational Technologies, Moscow, No. 10, pp. 25-35 (in Russian).

Fomichov V. A. (2007). Mathematical foundations of representing the content of messages sent by computer intelligent agents. Moscow, State University – Higher School of Economics, Publishing Hiuse “TEIS”, 2007 – 176 p. (in Russian).

Fomichov, V.A. (2008). A cmprehensive mhematical framework for bridging a gap between two approaches to creating a Meaning-Understanding Web. Intern. Journal of Intelligent Computing and Cybernetics,. Vol. 1, No. 1, pp. 143-163.

Fomichov, V. A. (2010a) Semantics-Oriented Natural Language Processing: Mathematical Models and Algorithms, Springer, New York, Dordrecht, Heidelberg, London. - 352 p.

Fomichov, V. A. (2010b) Theory of K-representations as a comprehensive formal framework for developing a Multilingual Semantic Web. Informatica. An Intern. Journal of Computing and Informatics (Slovenia), Vol. 34, No. 3, pp.. 387-396.

Fomichov, V. A. (2011). The prospects revealed by the theory of K-representations for bioinformatics and Semantic Web. Actes de la 18e conference sur le Traitement Automatique des Langues Naturels. Actes de la 15e Rencontre des Etudiants Cercheurs en Informatique pour le Traitement Automatique des Langues. France, Montpellie, 27th June - 1st July 2011 Vol. 1: Actes: articles longs. Montpellier : AVL Diffusion, pp 5-20.

Fomichov, V.A. (2013). A broadly applicable and flexible conceptual metagrammar as a basic tool for developing a Multilingual Semantic Web. In: Metais E., Meziane F., Saraee M., Sugumaran V., Vadera S. (Eds.) Natural Language Processing and Information Systems. 18th Intern. Conference on Applications of Natural Language to Information Systems, NLDB 2013. Salford, UK, June 2013, Proceedings. Lecture Notes in Computer Science, Vol. 7934, Springer, Berlin, Heidelberg, pp. 249-259.

Fomichov, V. A. (2014) SK-languages as a comprehensive formal environment for developing a Multilingual Semantic Web. Decker H., Lhotská L., Link S., Spies M., Wagner R.R. (Eds.). Database and Expert Systems Applications, 25th Intern. Conference, DEXA 2014, Munich, Germany, September 1-4, 2014, Part I, Proceedings. Lecture Notes in Computer Science, Vol. 8644, Cham: Springer International Publishing Switzerland, pp. 394-401.

Fomichov, V.A., Kirillov, A.V. (2012). A formal model for constructing semantic expansions of the search requests about the achievements and failures. Artificial Intelligence: Methodology, Systems, and Applications, Ramsay A., Agre G. (Eds.), Lecture Notes in Computer Science, Vol. 7557, Springer, Berlin, Heidelberg, pp. 296–304.

Gildea, D., Jurafsky, D. (2002). Automatic Labeling of Semantic Roles. Computational Linguistics, Vol. 28, No. 3, pp. 245 - 288.

Google Hummingbird (2016); https://en.wikipedia.org/wiki/ Google_Hummingbird (retrieved 10.11.2016).

Kingsbury, P., Palmer, M. (2002). From TreeBank to PropBank . Proceedings. LREC, 2002.

Langkilde, I, Knight, K. (1998). Generation that exploits corpus-based statistical knowledge. Proc. of the 36th Annual Meeting of the ACL and 17th International Conference on Computational Linguistics, Montreal, pp. 704-710.

Li, B., Wen, Y., Bu, L., Qu, W., Xue, N. (2016). Annotating the Little Prince with Chinese AMRs. Proc. of LAW X – the 10th Linguistic Annotation Workshop. Berlin, Germany, August 11, 2016, ACL, pp. 7-15.

Liang, P. (2016). Learning executable semantic parsers for natural language understanding. Communications of the ACM, Vol. 59, No. 9, pp. 68-76.

Lu, C., Xu, Y., Geva, S. (2008). Web-based query translation for English-Chinese CLIR. Computational Linguistics and Chinese Language Processing (CLCLP), pp. 61-90.

Marcus M. P., Marcinkiewicz M. A., Santorini B. (1993). Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, Vol. 19, No. 2.

Marquez L., Carreras X., Litkowski K. C., Stevenson S. (2008) Semantic Role Labeling: an Introduction to the Special Issue. Computational Linguistics, Vol. 34, No. 2, pp. 145-159.

Ontos GmbH company Web-page (2016): www.ontos.com.

Pravikov, A.A., Fomichov, V.A. (2010). Development of a recommender system with a natural language interface on the basis of semantic objects’ mathematical models. Business Informatics. Interdisciplinary scientific-practical journal, Moscow, State University – Higher School of Economics, 2010, No. 4 (14), pp. 3-11.

Punyakanok V., Roth D., Yih W. T. (2008). The importance of syntactic parsing and inferencing in semantic role labeling. Computational Linguistics, Vol. 34, No. 2, pp.. 257-287.

Pust, M., Hermjakob, U., Knight, K. , Marcu, D., May, J. (2015). Parsing English into abstract meaning representation using syntax-based machine translation. In Proc. of the EMNLP 2015, Lisbon, pp. 1143-1154.

Razorenov, A. A., Fomichov, V. A. (2014). The Design of a Natural Language Interface for File System Operations on the Basis of a Structured Meanings Model. In Procedia Computer Science, Elsevier. V. 31. P. 1005-1011; open access, URL: http://authors.elsevier.com/sd/article/S1877050914005304.

Razorenov, A. A., Fomichov, V. A. (2016). A new formal approach to semantic parsing of instructions and to file manager design. In Database and Expert Systems Applications, 27th Intern. Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Part I, Proceedings. Lecture Notes in Computer Science. V. 9827. Cham: Springer International Publishing Switzerland, pp. 416-430.

Rindflesh, T.C., Kilicoglu, H., Fiszman, M., Roszemblat, G., Shin, D. (2011). Semantic MEDLINE: An Advanced Information Management Application for Biomedicine. Information Services and Use, IOS Press Vol. 1, pp. 15-21.

Sawai, Y., Shindo, H., Matsumoto, Y. (2015). Semantic structure analysis of noun phrases using abstract meaning representation. Proc. of the 53rd Annual Meeting of the ACL (Volume 2: Short papers), Beijing, pp. 851-856.

Schubert, L.K., Hwang, C.H. (2000). Episodic Logic meets little red riding hood: A comprehensive, natural representation for language understanding. In Iwanska, L., Shapiro, S.C. (eds.), Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language, MIT/AAAI Press, Menlo Park, CA, and Cambridge, MA, pp. 111-174.

Stellato, A. (2016). A language-aware Web will give us a bigger and better Semantic Web. MSW 2015. Multilingual Semantic Web. Proc. of the Fourth Workshop on the Multilingual Semantic Web (MSW4) co-located with 12th Extended Semantic Web Conference (ESWC 2015), Portoroz, Slovenia, June 1, 2015, pp. 1-14.

Sullivan, D. (2013). FAQ: All about the new Google “Hummingbird” algorithm. Search Engine Land, 26 September 2013, http://searchengineland.com/google-hummingbird-172816 (retrieved 10.11.2016).

Uchida, H., Zhu, M., Della Senta, T. (1999). A Gift for a Millennium.

Uren, V.S., Lei, Y., Motta, E.: (2008). SemSearch: Refining Semantic Search. In: Bechhofer, S., Hauswirth, M., Hoffman, J., Koubarakis, M. (Eds.), ESWS 2008, LNCS, vol. 5021. Springer, Heidelberg, pp. 874-878.

Vogt, M. (2016). How Natural Language Processing will change the Semantic Web. Semantics, April 13, 2016; https://2016.semantics.cc/how-natural-language-processing-will-change-semantic-web (retrieved 12.09.2016).

Wang, C., Xue, N., Pradhan, S. (2015). Boosting transition-based AMR parsing with refined actions and auxiliary analyzers. In Proc. of the 53rd Annual Meeting of the ACL (Volume 2: Short papers), Beijing, pp. 857-862.

Werling, K., Gabor, A., Manning, C.D. (2015). Robust subgraph generation improves abstract meaning representation parsing. In Proc. of the 53rd Annual Meeting of the ACL (Volume 1: Long Papers), Beijing, pp. 982-991.

Wilks, Y., Brewster, C. (2006). Natural Language Processing as a Foundation of the Semantic Web. Foundations and Trends in Web Science, Vol. 1, No. 3. Hanover, MA; Delft: now Publ. Inc.

Wintner S. (2009). What science underlies natural language engineering? Computational Linguistics, Vol. 35, No. 4, pp. 641-644.

Yi ,S, Loper., E., Palmer, M. (2007) .Can semantic roles generalize across genres? Proceedings of the Human Language Technologies Conference of the North American Chapter of the ACL. Rochester, NY, pp. 548-555.




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