基于图的机器学习完成本体:一个综合评价

IF 4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine learning and knowledge extraction Pub Date : 2022-12-01 DOI:10.3390/make4040056
Sebastian Mežnar, Matej Bevec, N. Lavrač, Blaž Škrlj
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引用次数: 0

摘要

越来越多的语义资源为人类提供了丰富的知识,但它们的增长也增加了错误知识库条目的概率。因此,开发识别给定知识库中潜在虚假部分的方法是高度相关的。我们提出了一种本体补全方法,该方法将本体转换为图,并使用仅结构链接分析方法推荐缺失的边。通过系统地评估8种不同语义资源(包括基因本体、食品本体、海洋本体和类似本体)上的13种方法(其中一些用于知识图),我们证明了仅结构链接分析可以为分析数据集子集提供可扩展且计算效率高的本体补全方法。据我们所知,这是目前对不同类型的链接分析方法在不同领域语义资源之间的适用性进行的最广泛的系统研究。它表明,通过考虑符号节点嵌入,可以获得预测(链接)的解释,这使得该方法的分支可能比黑盒方法更有价值。
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Ontology Completion with Graph-Based Machine Learning: A Comprehensive Evaluation
Increasing quantities of semantic resources offer a wealth of human knowledge, but their growth also increases the probability of wrong knowledge base entries. The development of approaches that identify potentially spurious parts of a given knowledge base is therefore highly relevant. We propose an approach for ontology completion that transforms an ontology into a graph and recommends missing edges using structure-only link analysis methods. By systematically evaluating thirteen methods (some for knowledge graphs) on eight different semantic resources, including Gene Ontology, Food Ontology, Marine Ontology, and similar ontologies, we demonstrate that a structure-only link analysis can offer a scalable and computationally efficient ontology completion approach for a subset of analyzed data sets. To the best of our knowledge, this is currently the most extensive systematic study of the applicability of different types of link analysis methods across semantic resources from different domains. It demonstrates that by considering symbolic node embeddings, explanations of the predictions (links) can be obtained, making this branch of methods potentially more valuable than black-box methods.
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来源期刊
CiteScore
6.30
自引率
0.00%
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0
审稿时长
7 weeks
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