This area of research concerns the study of methods to support different types of uncertainty in Semantic Web technologies. Particularly, we mainly focus on the combination of fuzzy logic with Description Logics or ontologies [1]. This makes it possible to represent imprecise knowledge and to perform approximate reasoning. For example, in a fuzzy ontology there can be a fuzzy concept assertion stating than an individual is partially a member of a class, and we can ask a fuzzy reasoner to retrieve the instances of a class together with their membership degrees. Some results of our research include:
- Reasoning algorithms for fuzzy Description Logics, such as crisp representation algorithms [2] or optimization-based representation algorithms [3].
- A language for fuzzy ontology representation, Fuzzy OWL 2, and a software editor (a Protégé plug-in) [4].
- Implementation of software fuzzy ontology reasoners: fuzzyDL [5] and DeLorean [6].
- Software tools for fuzzy ontology learning. In particular, Datil learns fuzzy datatypes from numerical data properties [7] and Fudge learns them from the definitions given by different experts [8].
- Software tools for fuzzy knowledge graphs. In particular, FUKG solves flexible queries, described using fuzzy datatypes, over knowledge graphs [9].
- Several applications using fuzzy ontologies, such as construction [10], recommendation [11] or gait recognition [12].
SELECTED PUBLICATIONS
- F. Bobillo, M. Cerami, F. Esteva, Á. García-Cerdaña, R. Peñaloza, U. Straccia. Fuzzy Description Logics. Chapter XVI of Handbook of Mathematical Fuzzy Logic volume 3. Volume 58 of Studies in Logic, Mathematical Logic and Foundations. College Publications, pp. 1105-1181, 2015.
- F. Bobillo. The Role of Crisp Elements in Fuzzy Ontologies: The Case of Fuzzy OWL 2 EL. IEEE Transactions on Fuzzy Systems 24(5):1193-1209, 2016.
- F. Bobillo, U. Straccia. Fuzzy Description Logics with General T-norms and Datatypes. Fuzzy Sets and Systems 160(23):3382-3402, 2009.
- F. Bobillo, U. Straccia. Fuzzy Ontology Representation using OWL 2. International Journal of Approximate Reasoning 52(7):1073-1094, 2011.
- F. Bobillo, U. Straccia. The Fuzzy Ontology Reasoner FuzzyDL. Knowledge-Based Systems 95:12–34, 2016.
- F. Bobillo, M. Delgado, J. Gómez-Romero. DeLorean: A Reasoner for Fuzzy OWL 2. Expert Systems with Applications 39(1):258-272, 2012.
- I. Huitzil, F. Bobillo. Fuzzy Ontology Datatype Learning using Datil. Expert Systems With Applications 228:120299, 2023.
- I. Huitzil, F. Bobillo, J. Gómez-Romero, U. Straccia. Fudge: Fuzzy Ontology Building with Consensuated Fuzzy Datatypes. Fuzzy Sets and Systems 401:91-112, 2020.
- J. F. Yagüe, I. Huitzil, C. Bobed, F. Bobillo. FUKG: Answering Flexible Queries over Knowledge Graphs. The Electronic Library, 2023.
- I. Huitzil, M. Molina-Solana, J. Gómez-Romero, F. Bobillo. Minimalistic Fuzzy Ontology Reasoning: An application to Building Information Modeling. Applied Soft Computing 103:107158, 2021.
- I. Huitzil, F. Alegre, F. Bobillo. GimmeHop: A Recommender System for Mobile Devices using Ontology Reasoners and Fuzzy Logic. Fuzzy Sets and Systems 401:55-77, 2020.
- I. Huitzil, L. Dranca, J. Bernad, F. Bobillo. Gait Recognition Using Fuzzy Ontologies and Kinect Sensor Data. International Journal of Approximate Reasoning 113:354-371, 2019.
