Tracking Logical Difference in Large-Scale Ontologies: A Forgetting-Based Approach

被引：0

作者：

Zhao, Yizheng ^{[1
]}

Alghamdi, Ghadah ^{[1
]}

Schmidt, Renate A. ^{[1
]}

Feng, Hao ^{[2
]}

Stoilos, Giorgos ^{[3
]}

Juric, Damir ^{[3
]}

Khodadadi, Mohammad ^{[3
]}

机构：

[1] Univ Manchester, Manchester, Lancs, England

[2] North China Univ Sci & Technol, Qinhuangdao, Hebei, Peoples R China

[3] Babylon Hlth, London, England

来源：

THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2019年

基金：

英国工程与自然科学研究理事会;

关键词：

ALGORITHMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper explores how the logical difference between two ontologies can be tracked using a forgetting-based or uniform interpolation (UI)-based approach. The idea is that rather than computing all entailments of one ontology not entailed by the other ontology, which would be computationally infeasible, only the strongest entailments not entailed in the other ontology are computed. To overcome drawbacks of existing forgetting/uniform interpolation tools we introduce a new forgetting method designed for the task of computing the logical difference between different versions of large-scale ontologies. The method is sound and terminating, and can compute uniform interpolants for ALC-ontologies as large as SNOMED CT and NCIt. Our evaluation shows that the method can achieve considerably better success rates (>90%) and provides a feasible approach to computing the logical difference in large-scale ontologies, as a case study on different versions of SNOMED CT and NCIt ontologies shows.

引用

页码：3116 / 3124

页数：9

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