Proximity-based Logic Programming is a formal framework for representing general or non-specialized knowledge. Although it is a powerful tool, it is too complex because the values of the proximity equations (fuzzy binary relations that establish the relationships among the symbols of a first-order language) must be manually defined by the designer of the system. In this paper, we propose a new framework for Proximity-based Logic Programming enhanced with WordNet and Interval-Valued Fuzzy Sets. Its main contribution is to compile automatically the information provided by WordNet and generate an interval-valued proximity relation on the set of their words. This proposal is completely integrated inside the unification mechanism of Bousi~Prolog system. This allows us to introduce the lexical knowledge induced from a linguistic resource, such as WordNet, into an approximate reasoning system. To the best of our knowledge, this is the first time that WordNet is introduced into the core of a Prolog system by means of compilation techniques and lexical knowledge is combined with proximity-based unification frame.
BobilloF., Gómez-RomeroJ. and León-AraúzP., Fuzzy Ontologies for Specialized Knowledge Representation in WordNet, in: Proceedings of 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU2012), vol. I, 2012, pp. 430–439.
2.
León-AraúzP., Gómez-RomeroJ. and BobilloF., A Fuzzy Ontology Extension of WordNet and EuroWordnet for Specialized Knowledge, in: Proceedings of the Terminology and Knowledge Engineering Conference 2012 (TKE-2012), 2012, pp. 154– 139–154.
3.
DavisE. and MorgensternL., Introduction: Progress in formal commonsense reasoning, Artificial Intelligence153 (2004), 1–12.
4.
LevesqueH., PirriF. and ReiterR., Foundations for the situation calculus, Electronic Transactions on Artificial Intelligence2(34) (1998), 159–178.
5.
McCarthyJ.L., Applications of circumscription to formalizing common sense knowledge, Artificial Intelligence28 (1984), 89–116.
6.
Fernández-LanzaS., Prolog for automatic processing of synonymy, Logica Trianguli4 (2000), 25–34.
7.
MinskyM., The emotion machine: Commonsense thinking, artificial intelligence and the future of human mind, Simon and Schuster, 2006.
8.
Martínez-CruzC., et al., Flexible queries on relational databases using fuzzy logic and ontologies, Information Sciences366 (2016), 150–164.
9.
LenatD.B. and GuhaR.V., Building large knowledge-based systems; representation and inference in the Cyc project,Addison-Wesley Longman Publishing Co., Inc., 1989.
10.
MillerG.A., WordNet: A lexical database for english, Communications of the ACM38 (1995), 39–41.
11.
LiuH. and SinghP., Commonsense Reasoning in and over Natural Language, Proc of the 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, 2004, pp. 293–306.
12.
Rubio-ManzanoC. and Julian-IranzoP., Reasoning with words: A first approximation, Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2014), 2014, pp. 569–574.
13.
Rubio-ManzanoC. and Julian-IranzoP., A fuzzy linguistic prolog and its applications, Journal of Intelligent and Fuzzy Systems26(3) (2014), 1503–1516.
14.
NewellA., The knowledge level, Artificial Intelligence18(1) (2008), 87–27.
Julián-IranzoP. and Rubio-ManzanoC., A sound semantics for a similarity-based logic programming language, in: International Work-Conference on Artificial Neural Networks, Springer Berlin Heidelberg C.2011, pp. 421–428.
17.
FontanaF. and FormatoF., Likelog: A logic programming language for flexible data retrieval. Proc of the ACM SAC, 1999, pp. 260–267.
18.
Julián-IranzoP., et al., A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees, arXiv, preprint arXiv:1501.02034.
19.
LoiaV., SenatoreS. and SessaM.I., Similarity-based SLD resolution and its role for web knowledge discovery, Fuzzy Sets and Systems144(1) (2004), 151–171.
20.
Rodriguez-ArtalejoM. and Romero-DiazC., A declarative semantics for CLP with qualification and proximity, Theory and Practice of Logic Programming10(4-6) (2010), 627–642.
21.
SobrinoA., The Role of Synonymy and Antonymy in ’Natural’ Fuzzy Prolog, in Soft Computing in Humanities and Social Sciences, SeisingRudolf, SanzVeronica, eds., Springer, 2012, pp. 209–236.
22.
Hadj TaiebM.A.,
Ben AouichaM. and Ben HamadouA., Ontology-based approach for measuring semantic similarity, Engineering Applications of Artificial Intelligence36 (2014), 238–261.
23.
RadaR., MiliH., BicknellE. and BlettnerM., Development and application of a metric on semantic nets, IEEE Transactions on Systems, Man and Cybernetics19 (1989), 17–30.
24.
WuZ. and PalmerM., Verbs semantics and lexical selection, in: Proceedings of the 32Nd Annual Meeting on Association for Computational Linguistics (ACL ’94), Association for Computational Linguistics, Stroudsburg, USA, 1994, pp. 133–138.
25.
TverskyA., Features of similarity, Psychological Review84 (1977), 327–352.
26.
HirstG. and St-OngeD., Lexical chains as representations of context for the detection and correction of malapropisms, WordNet: An electronic lexical database, 1998, pp. 305–332.
27.
BustinceH., Interval-valued fuzzy sets in soft computing, International Journal of Computational Intelligence Systems3(2) (2010), 215–222.
28.
Julián-IranzoP. and Rubio-ManzanoC., A similarity-based WAM for Bousi∼Prolog, Lecture Notes in Computer Science, 5517, Springer, Heidelberg, 2009, pp. 245–252.
29.
WitzigS., Accessing wordnet from prolog. Artificial Intelligence Centre, University of Georgia, 2003, pp. 1–18.
30.
SchallehnE., et al., Efficient similarity-based operations for data integration, Data and Knowledge Engineering48(3) (2004), 361–387.
