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research-article

Deriving ontological semantic relations between Arabic compound nouns concepts

Published: 01 April 2017 Publication History

Abstract

Legal ontologies have proved their increasingly substantial role in representing, processing and retrieving legal information. By using the knowledge modeled by such ontologies in form of concepts and relations, it is possible to reason over the semantic content of legal documents. Supporting (semi-) automatically the development of ontologies from text is commonly referred to as ontology learning from text. The learning process includes learning of the concepts that will form the ontology and learning of the semantic relations among them.In this paper, we present a new approach for expliciting the semantic relations between Arabic compound nouns concepts. The originality of this work is twofold. Firstly, the technique of inferring relations is based on exploiting the internal structure of the compounds using a defined set of domain-and language-independent rules according to their different structures, on the one hand, and on studying prepositions semantics specifying the inferred relations applying a gamification mechanism that collects human votes, on the other hand. Secondly, relying on the compounds set described by both binary (structural positions in which there are written) and relational attributes (the deduced relations), we used a Relational Concept Analysis (RCA) technique, as an adaptation of Formal Concept Analysis (FCA), for the construction of interconnected lattices that we transformed into ontological concepts and relations which can be either taxonomic or transversal.Experiments carried out on Arabic legal dataset showed that the proposed approach reached encouraging performance through achieving high precision and recall scores. This performance affects positively the retrieval results of legal documents based on a powerful ontology, which presents our main objective.

References

[1]
G. Aguado de Cea, A. Gmez-Prez, E. Montiel-Ponsoda, M.C. Surez-Figueroa, Using linguistic patterns to enhance ontology development, in: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, KEOD, 2009, pp. 206-213.
[2]
M. Al-Yahya, S. Al-Malak, LuluhAldhubayi, A Pattern-based Approach to Semantic Relation Extraction Using a Seed Ontology, in: ICSC, Newport Beach, California, USA, 2014, pp. 96-99.
[3]
M. Al-Yahya, S. Al-Malak, LuluhAldhubayi, Ontological lexicon enrichment: the badea system for semi-automated extraction of antonymy relations from Arabic language corpora, Malaysian J. Comput. Sci., 29 (2016) 56-73.
[4]
M.G.H. Al Zamil, Q. Al-Radaideh, Automatic extraction of ontological relations from Arabic text, J. King Saud Univ. Comput. Inf. Sci., 26 (2014) 462-472.
[5]
I. Augenstein, D. Maynard, F. Ciravegna, Relation extraction from the web using distant supervision, in: 19th International Conference on Knowledge Engineering and Knowledge Management EKAW, 2014, pp. 26-41.
[6]
R. Bendaoud, A.M.R. Hacene, A. Napoli, Y. Toussaint, B. Delecroix, Text-based ontology construction using relational concept analysis, in: International Workshop on Ontology Dynamics IWOD, 2007, pp. 154-163.
[7]
R. Bendaoud, A.M.R. Hacene, A. Napoli, Y. Toussaint, P. Valtchev, Ontology learning from text using relational concept analysis, in: International MCETECH Conference on e-Technologies MCETECH, 2008, pp. 154-163.
[8]
N. Bouhriz, F. Benabbou, H. Benlahmer, Text conceptsextraction based on Arabic wordnet and formal concept analysis, Int. J. Comput. App., 111 (2015) 30-34.
[9]
I. Boujelben, S. Jamoussi, A.B. Hamadou, A hybrid method for extracting relations between Arabic named entities, J. King Saud Univ. Comput. Inf. Sci., 26 (2014) 425-440.
[10]
I. Boujelben, S. Jamoussi, A.B. Hamadou, Relane: discovering relations between Arabic named entities, in: Text, Speech and Dialogue - 17th International Conference, Czech Republic, TSD. Brno, 2014, pp. 233-239.
[11]
de Bess, B., Nkwenti-Azeh, B., Sager, J.C., 1997. Glossary of Terms Used in Terminology. vol. 4.
[12]
S. Deterding, M. Sicart, L. Nacke, K. OHara, D. Dixon, Gamification. using game-design elements in non-gaming contexts, in: Extended Abstracts on Human Factors in Computing Systems CHI, 2011, pp. 2425-2428.
[13]
V. Devisree, P.C. Reghu Raj, A hybrid approach to relationship extraction from stories, Procedia Technol., 24 (2016) 1499-1506.
[14]
H.H. Do, S. Melnik, E. Rahm, Comparison of schema matching evaluations, in: NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems, London, UK, 2003, pp. 221-237.
[15]
X. Dolques, F. Le Ber, M. Huchard, C. Nebut, Analyse relationnelle de concepts pour lexploration de donnes relationnelles, in: Confrence Francophone sur lExtraction et la Gestion des Connaissances EGC, 2013, pp. 121-132.
[16]
M.A. Falih, N. Omar, A comparative study on Arabic grammatical relation extraction based on machine learning classification, Middle-East J. Sci. Res., 23 (2015) 1222-1227.
[17]
B. Ganter, G. Stumme, R. Wille, Formal Concept Analysis: Foundations and Applications (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence), Springer-Verlag New York Inc, Secaucus, NJ, USA, 2005.
[18]
B. Ganter, R. Wille, Formal Concept Analysis: Mathematical Foundations, Springer-Verlag New York Inc, Secaucus, NJ, USA, 1997.
[19]
T.R. Gruber, Toward principles for the design of ontologies used for knowledge sharing, Int. J. Hum. Comput. Stud., 43 (1995) 907-928.
[20]
M. Hearst, Automatic acquisition of hyponyms from large text corpora, in: 14th conference on Computational linguistics. Association for Computational Linguistics, 1992, pp. 539-545.
[21]
M. Huchard, A.M.R. Hacene, P. Valtchev, C. Roume, Relational concept discovery in structured datasets, Ann. Math. Artif. Intell., 49 (2007) 39-76.
[22]
M. Huchard, A. Napoli, A.M.R. Hacene, P. Valtchev, Mining description logics concepts with relational concept analysis, in: Selected Contributions in Data Analysis and Classification, Studies in Classification, Data Analysis, and Knowledge Organization. Berlin, 2003, pp. 259-270.
[23]
M. Huchard, A. Napoli, A.M.R. Hacene, P. Valtchev, A gentle introduction to relational concept analysis, tutorial icfca, in: 9th International Conference on Formal Concept Analysis ICFCA. Nicosia, Cyprus, 2011.
[24]
S. Joseph, Smitha M. Jasminea, Na, Sheenaa, Automatic extraction of hypernym & meronym relations in english sentences using dependency parser, in: 6th International Conference On Advances In Computing & Communications, ICACC. Cochin, India, 2016, pp. 539-546.
[25]
A. Kawtrakul, M. Suktarachan, A. Imsombut, Automatic thai ontology construction and maintenance system, in: OntoLex Workshop on LREC, 2004.
[26]
B. Kumova, Generating ontologies from relational data with fuzzy-syllogistic reasoning, in: Beyond Databases Architectures and Structures (BDAS). Communications in Computer and Information Science (CCIS), 2015, pp. 21-32.
[27]
A. Lakhfif, M.T. Laskri, A frame-based approach for capturing semantics from Arabic text for text-to-sign language mt, Int. J. Speech Technol., 19 (2016) 203-228.
[28]
K.C. Litkowski, Digraph analysis of dictionary preposition definitions, in: Workshop on Word Sense Disambiguation: Recent Successes and Future Directions WSD, 2002, pp. 9-16.
[29]
A. Maedche, S. Staab, Ontology Learning, HandBook on Ontologies, Springer, International Handbooks on Information Systems, 2004.
[30]
I.B. Mezghanni, F. Gargouri, Learning of legal ontology supporting the user queries satisfaction, in: 13th IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Warsaw, Poland, 2014, pp. 414-418.
[31]
I.B. Mezghanni, F. Gargouri, Towards an Arabic legal ontology based on documents properties extraction, in: 12th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA, Marrakech, Morocco, 2015, pp. 1-8.
[32]
I.B. Mezghanni, F. Gargouri, Detecting hidden structures from Arabic electronic documents: Application to the legal field, in: 14th IEEE International Conference on Software Engineering Research, Management and Applications, SERA, 2016, pp. 75-81.
[33]
Q. Miao, S. Zhang, B. Zhang, Y. Meng, H. Yu, Extracting and visualizing semantic relationships from chinese biomedical text, in: Pacific Asia Conference on Language, Information and Computation, 2012, pp. 99-107.
[34]
J. Pustejovsky, The generative lexicon, Comput. Linguistics, 17 (1991) 409-441.
[35]
J. Sadek, F. Meziane, Extracting Arabic causal relations using linguistic patterns, ACM Trans. Asian Low-Resour. Lang. Inf. Process., 15 (2016) 14:1-14:20.
[36]
I. Sag, T. Baldwin, F. Bond, A. Copestake, D. Flickinger, Multiword expressions: a pain in the neck for NLP, in: Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics CICLing, London, UK, 2002, pp. 1-15.
[37]
S.P. Sruti Rallapalli, A hybrid approach for the interpretation of nominal compounds using ontology, in: 26th Pacific Asia Conference on Language, Information and Computation PACLIC, 2012, pp. 554-563.
[38]
Ta, C.D., Thi, T.P., 2016. Automatic Extraction of Semantic Relations from Text Documents. Can Tho City, Vietnam, pp. 344351.
[39]
S. Takase, N. Okazaki, K. Inui, Fast and large-scale unsupervised relation extraction, in: Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, PACLIC. Shanghai, China, 2015, pp. 96-105.
[40]
M. Vela, T. Declerck, A methodology for ontology learning: deriving ontology schema components from unstructured text, in: Workshop on Semantic Authoring, Annotation and Knowledge Markup, 2009.
[41]
Y. Xiang, Q. Chen, X. Wang, Y. Qin, Distant supervision for relation extraction with ranking-based methods, Entropy, 18 (2016) 204.

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  • (2022)ArSphere: Arabic word vectors embedded in a polar sphereInternational Journal of Speech Technology10.1007/s10772-022-09966-926:1(95-111)Online publication date: 3-Mar-2022
  • (2018)A Semantic Information Content Based Method for Evaluating FCA Concept SimilarityInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.201804010612:2(77-93)Online publication date: 1-Apr-2018
  • (2017)Natural language semantic model for arithmetic sentencesProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3162976(175-179)Online publication date: 24-Nov-2017
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Published In

cover image Journal of King Saud University - Computer and Information Sciences
Journal of King Saud University - Computer and Information Sciences  Volume 29, Issue 2
April 2017
89 pages

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 April 2017

Author Tags

  1. Arabic compound nouns
  2. Compound structure
  3. FCA
  4. Gamification
  5. RCA
  6. Semantic relations derivation

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View all
  • (2022)ArSphere: Arabic word vectors embedded in a polar sphereInternational Journal of Speech Technology10.1007/s10772-022-09966-926:1(95-111)Online publication date: 3-Mar-2022
  • (2018)A Semantic Information Content Based Method for Evaluating FCA Concept SimilarityInternational Journal of Cognitive Informatics and Natural Intelligence10.4018/IJCINI.201804010612:2(77-93)Online publication date: 1-Apr-2018
  • (2017)Natural language semantic model for arithmetic sentencesProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3162976(175-179)Online publication date: 24-Nov-2017
  • (2017)CrimArProcedia Computer Science10.1016/j.procs.2017.08.113112:C(653-662)Online publication date: 1-Sep-2017

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