A Method for Generating Comparison Tables From the Semantic Web

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
A. Giacometti, Béatrice Bouchou-Markhoff, Arnaud Soulet
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引用次数: 0

Abstract

This paper presents Versus, which is the first automatic method for generating comparison tables from knowledge bases of the Semantic Web. For this purpose, it introduces the contextual reference level to evaluate whether a feature is relevant to compare a set of entities. This measure relies on contexts that are sets of entities similar to the compared entities. Its principle is to favor the features whose values for the compared entities are reference (or frequent) in these contexts. The proposal efficiently evaluates the contextual reference level from a public SPARQL endpoint limited by a fair-use policy. Using a new benchmark based on Wikidata, the experiments show the interest of the contextual reference level for identifying the features deemed relevant by users with high precision and recall. In addition, the proposed optimizations significantly reduce the number of required queries for properties as well as for inverse relations. Interestingly, this experimental study also show that the inverse relations bring out a large number of numerical comparison features.
一种从语义网生成比较表的方法
本文提出了第一个基于语义网知识库自动生成比较表的方法Versus。为此,它引入了上下文引用级别,以评估一个特性是否与比较一组实体相关。此度量依赖于上下文,这些上下文是与被比较实体相似的实体集。其原则是优先考虑比较实体的值在这些上下文中是引用(或频繁)的特征。该建议有效地从受合理使用策略限制的公共SPARQL端点评估上下文引用级别。使用基于Wikidata的新基准,实验表明上下文参考水平对识别用户认为相关的特征具有较高的准确性和召回率。此外,建议的优化显著减少了属性和逆关系所需查询的数量。有趣的是,本实验研究还表明,反比关系带来了大量的数值比较特征。
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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