TREE-BASED SEMANTIC ANALYSIS METHOD FOR NATURAL LANGUAGE PHRASE TO FORMAL QUERY CONVERSION
Keywords:natural language processing, graph data base, semantic analysis, formal query, decision tree, ontology.
Context. This work is devoted to the problem of natural language interface construction for ontological graph databases. The focus here is on the methods for the conversion of natural language phrases into formal queries in SPARQL and CYPHER query languages.
Objective. The goals of the work are the creation of a semantic analysis method for the input natural language phrases semantic type determination and obtaining meaningful entities from them for query template variables initialization, construction of flexible query templates for the types, development of program implementation of the proposed technique.
Method. A tree-based method was developed for semantic determination of a user’s phrase type and obtaining a set of terms from it to put them into certain places of the most suiting formal query template. The proposed technique solves the tasks of the phrase type determination (and this is the criterion of the formal query template selection) and obtaining meaningful terms, which are to initialize variables of the chosen template. In the current work only interrogative and incentive user’s phrases are considered i.e. ones that clearly propose the system to answer or to do something. It is assumed that the considered dialog or reference system uses a graph ontological database, which directly impacts the formal query patterns – the resulting queries are destined to be in SPARQL or Cypher query languages. The semantic analysis examples considered in this work are aimed primarily at inflective languages, especially, Ukrainian and Russian, but the basic principles could be suitable to most of the other languages.
Results. The developed method of natural language phrase to a formal query in SPARQL and CYPHER conversion has been implemented in software for Ukrainian and Norwegian languages using narrow subjected ontologies and tested against formal performance criteria.
Conclusions. The proposed method allows the dialog system fast and with minimum number of steps to select the most suitable query template and extract informative entities from a natural language phrase given the huge phrase variability in inflective languages. Carried out experiments have shown high precision and reliability of the constructed system and its potential for practical usage and further development.
Galitsky B. Developing Enterprise Chatbots. Learning Linguistic Structures. Berlin, Springer, 2019, 566 p.
Sun C. A Natural Language Interface for Querying Graph Databases: master’s thesis … master in computer science and engineering. USA, Massachusetts Institute of Technology, 2018, 69 p.
Palagіn O. V., Krivij S. L., Bіbіkov D. S., Velichko V. Ju. ta іn. Formal-logical approach to building analysis systems of knowledge in different domains, Problems in progtamming, 2010, No. 2–3, pp. 382–389.
Li F., Jagadish H. V. Understanding natural language queries over databases, SIGMOD Record, 2016, Vol. 45, pp. 6– 13. DOI: 10.1145/2949741.2949744
Zhong V., Xiong G., Socher R. Seq2sql: generating structured queries from natural language using reinforcement learning, 2017 [Electronic resource]. Access mode: https://arxiv.org/pdf/1709.00103.pdf. arXiv: 1709.00103
Shaik S., Kanakam P., Hussain S. M., Suryanarayana D. Transforming natural language query to SPARQL for semantic information retrieval, International Journal of Engineering Trends and Technology, 2016, No. 7, pp. 347–350. DOI: 10.14445/22315381/IJETT-V41P263
Ochieng P. PAROT: Translating natural language to SPARQL, Expert Systems with Applications, 2020, No. 5, pp. 1–16. DOI: 10.1016/j.eswa.2021.114712
Jung H., Kim W. Automated conversion from natural language query to SPARQL query, Journal of Intelligent Information Systems, 2020, Vol. 55, pp. 501–520. DOI: 10.1007/s10844-019-00589-2
Yin X., Gromann D., Rudolph S. Neural machine translation from natural language to SPARQL, Future Generation Computer Systems, 2021, Vol. 117, pp. 510–519. DOI: 10.1016/j.future.2020.12.013
Damljanovic D., Agatonovic M., Cunningham H. FREyA: an interactive way of querying linked data using natural language, The Semantic Web: ESWC 2011 Workshops, 2011, pp. 125–138. DOI: 10.1007/978-3-642-25953-1_11
GIT-hub: FREyA documentation [Electronic resource]. Access mode: https://github.com/nmvijay/freya
GIT-hub Convert English sentences to Cypher queries documentation [Electronic resource]. Access mode: https://github.com/gsssrao/english2cypher
Litvin A. A., Velychko V. Yu., Kaverynskyi V. V. Method of information obtaining from ontology on the basis of a natural language phrase analysis, Problems in progtamming, 2020, No 2–3, pp. 322–330. DOI: 10.15407/pp2020.0203.322
Kіral’ S. S. recenzenti, ta іn. pіd zagal’n. red. M. Stepanenka Listi do Olesja Gonchara. Kyiv, Sakcent Pljus, Vol. 1, 1946–1982, 2016, 736 p.
How to Cite
Copyright (c) 2021 А. А. Литвин, В. Ю. Величко, В. В. Каверінскій
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.