Tuning fuzzy SPARQL queries

IF 3.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Approximate Reasoning Pub Date : 2024-04-30 DOI:10.1016/j.ijar.2024.109209

Jesús M. Almendros-Jiménez , Antonio Becerra-Terón , Ginés Moreno , José A. Riaza

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引用次数: 0

Abstract

During the last years, the study of fuzzy database query languages has attracted the attention of many researchers. In this line of research, our group has proposed and developed FSA-SPARQL (Fuzzy Sets and Aggregators based SPARQL), which is a fuzzy extension of the Semantic Web query language SPARQL. FSA-SPARQL works with fuzzy RDF datasets and allows the definition of fuzzy queries involving fuzzy conditions through fuzzy connectives and aggregators. However, there are two main challenges to be solved for the practical applicability of FSA-SPARQL. The first problem is the lack of fuzzy RDF data sources. The second is how to customize fuzzy queries on fuzzy RDF data sources. Our research group has also recently proposed a fuzzy logic programming language called $F A S I L L$ that offers powerful tuning capabilities that can accept applications in many fields. The purpose of this paper is to show how the $F A S I L L$ tuning capabilities serve to accomplish in a unified framework both challenges in FSA-SPARQL: data fuzzification and query customization. More concretely, from a FSA-SPARQL to $F A S I L L$ transformation, data fuzzification and query customization in FSA-SPARQL become $F A S I L L$ tuning problems. We have validated the approach with queries against datasets from online communities.

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调整模糊 SPARQL 查询

近年来，模糊数据库查询语言的研究吸引了众多研究人员的关注。在这一研究领域，我们小组提出并开发了 FSA-SPARQL（基于模糊集和聚合器的 SPARQL），它是语义网查询语言 SPARQL 的模糊扩展。FSA-SPARQL 适用于模糊 RDF 数据集，允许通过模糊连接词和聚合器定义涉及模糊条件的模糊查询。然而，FSA-SPARQL 在实际应用中面临两大挑战。第一个问题是缺乏模糊 RDF 数据源。第二个问题是如何在模糊 RDF 数据源上定制模糊查询。我们的研究小组最近还提出了一种名为 FASILL 的模糊逻辑编程语言，它提供了强大的调整功能，可以接受许多领域的应用。本文的目的是展示 FASILL 的调整功能如何在一个统一的框架内完成 FSA-SPARQL 中的两个挑战：数据模糊化和查询定制。更具体地说，从 FSA-SPARQL 到 FASILL 转换，FSA-SPARQL 中的数据模糊化和查询定制成为了 FASILL 调整问题。我们利用在线社区数据集的查询验证了这种方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Approximate Reasoning 工程技术-计算机：人工智能

CiteScore

6.90

自引率

12.80%

发文量

170

审稿时长

67 days

期刊介绍： The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.