Querying knowledge graphs through positive and negative examples and feedback

IF 2.3 3区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal of Intelligent Information Systems Pub Date : 2024-02-15 DOI:10.1007/s10844-024-00846-z

Akritas Akritidis, Yannis Tzitzikas

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引用次数: 0

Abstract

The formulation of structured queries over Knowledge Graphs is not an easy task. To alleviate this problem, we propose a novel interactive method for SPARQL query formulation, for enabling users (plain and advanced) to formulate gradually queries by providing examples and various kinds of positive and negative feedback, in a manner that does not pre-suppose knowledge of the query language or the contents of the Knowledge Graph. In comparison to other example-based query approaches, distinctive features of our approach is the support of negative examples, and the positive/negative feedback on the generated constraints. We detail the algorithmic aspect and we present an interactive user interface that implements the approach. The application of the model on real datasets from DBpedia (Movies, Actors) and other datasets (scientific papers), showcases the feasibility and the effectiveness of the approach. A task-based evaluation that included users that are not familiar with SPARQL, provided positive evidence that the interaction is easy-to-grasp and enabled most users to formulate the desired queries.

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通过正反实例和反馈查询知识图谱

对知识图谱进行结构化查询并非易事。为了缓解这一问题，我们提出了一种新颖的 SPARQL 查询交互式方法，通过提供示例和各种积极和消极反馈，使用户（普通用户和高级用户）能够以一种不预先假定查询语言或知识图谱内容知识的方式逐步提出查询。与其他基于示例的查询方法相比，我们的方法的显著特点是支持负面示例，并对生成的约束条件提供正面/负面反馈。我们详细介绍了算法方面的内容，并展示了实现该方法的交互式用户界面。该模型在 DBpedia 的真实数据集（电影、演员）和其他数据集（科学论文）上的应用展示了该方法的可行性和有效性。对不熟悉 SPARQL 的用户进行的基于任务的评估提供了积极的证据，证明这种交互方式易于掌握，大多数用户都能提出所需的查询。

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来源期刊

Journal of Intelligent Information Systems 工程技术-计算机：人工智能

CiteScore

7.20

自引率

11.80%

发文量

审稿时长

6-12 weeks

期刊介绍： The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.