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Incremental Evaluation of Lattice-Based Aggregates in Logic Programming Using Modular TCLP

Practical Aspects of Declarative Languages – 21th International Symposium, PADL 2019, Lisbon, Portugal, January 14-15, 2019.doi 10.1007/978-3-030-05998-9_7

Tuning Fuzzy SPARQL Queries in a Fuzzy Logic Programming Environment

We have recently designed FSA-SPARQL, an extension of the SPARQL query language for querying fuzzy RDF datasets. Answers of FSA-SPARQL queries are usually annotated with truth degrees which are computed from fuzzy connectives and operators that act on truth degrees associated to RDF triples. While FSA-SPARQL offers a rich repertoire of fuzzy connectives and operators, it is not always easy to retrieve the user’s expected answers. This is very often due to wrong formulation of queries, caused by inadequate use/combination of fuzzy connectives, operators and thresholds. For instance, a high threshold for truth degrees in some RDF datasets can lead to an empty set of answers, some strong or weak restrictive combination of fuzzy conditions might produce few or too many answers, etc. On the other hand, our research group has also developed the fuzzy logic programming language FASILL, which has been equipped with tuning techniques for enabling the customization of queries from test cases. In this paper, our goals are: (1) to provide a FSA-SPARQL translation to FASILL and (2) apply the tuning techniques to FSA-SPARQL queries for getting more precise formulation of queries from test cases.Artículo pendiente de publicación en el 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019): http://sites.ieee.org/fuzzieee-2019/

QL: Object-oriented Queries on Relational Data (Trabajo ya publicado)

Paper already published at: European Conference on Object-Oriented Programming (ECOOP) 2016 This paper describes QL, a language for querying complex, potentially recursive data structures. QL compiles to Datalog and runs on a standard relational database, yet it provides familiar-looking object-oriented features such as classes and methods, reinterpreted in logical terms: classes are logical properties describing sets of values, subclassing is implication, and virtual calls are dispatched dynamically by considering the most specific classes containing the receiver. Furthermore, types in QL are prescriptive and actively influence program evaluation rather than just describing it. In combination, these features enable the development of concise queries based on reusable libraries, which are written in a purely declarative style, yet can be efficiently executed even on very large data sets. In particular, we have used QL to implement static analyses for various programming languages, which scale to millions of lines of code.

PTL: A Prolog-based Model Transformation Language

In this paper we present a model transformation language based on logic programming. The language, called PTL (Prolog-based Transformation Language), can be considered as an hybrid language in which ATL-style rules are combined with logic rules for defining transformations. ATL-style rules are used to define mappings from source models to target models while logic rules are used as helpers. The proposal has been implemented so that a Prolog program is automatically obtained from a PTL program. We have equipped our language with debugging and tracing capabilities which help developers to detect programming errors in PTL rules.