本体摘要技术综述

doi:10.11959/j.issn.2096-109x.2018081

摘要/Abstract

摘要：

本体是知识共享的重要工具，也是知识图谱的上层结构。随着数据规模和本体复杂性的增长，本体理解与应用日益困难。本体摘要作为缩小本体规模的一项技术，为加速本体理解与应用提供了技术支持。给出了本体摘要的不同定义；对本体摘要方法进行比较分析；介绍了本体摘要评估的指标体系；最后提出本体摘要的进一步研究方向

关键词: 知识图谱, 本体, 本体摘要, 本体评估

Abstract:

Ontology is an important tool for knowledge sharing,while it is also the upper structure of knowledge graph.With the explosion of data and ontology complexity,ontology understanding and application are becoming more and more difficult.As a technique to shrink ontology scale,ontology summarization accelerated ontology understanding and application technologically.Different definitions of ontology summarization were shown,ontology summarization methods were compared and analyzed,ontology summarization evaluation parameter system was introduced,and at last,possible future research area was given.

Key words: knowledge graph, ontology, ontology summarization, ontology evaluation

中图分类号:

TP393

丁悦航, 于洪涛, 黄瑞阳, 李英乐. 本体摘要技术综述[J]. 网络与信息安全学报, 2018, 4(10): 12-21.

Yuehang DING, Hongtao YU, Ruiyang HUANG, Yingle LI. Ontology summarization technology survey[J]. Chinese Journal of Network and Information Security, 2018, 4(10): 12-21.

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推理机	推理方法
Jess^[31]	基于规则
Jena^[32]	基于规则
Pellet^[33]	基于描述逻辑
Racer^[34]	基于描述逻辑
FaCT^++[35]	基于描述逻辑
FuzzyDL^[36]	基于描述逻辑

本体	与标准概念的匹配度^[18]	与标准概念的匹配度^[2]	与标准概念的匹配度^[22]
biosphere	80.0%	60.0%	90.0%
financial	66.7%	55.6%	66.7%
aktors	75.0%	n/a%	62.5%
portal	75.0%	n/a%	62.5%

本体	与标准概念的匹配度
本体	文献[18]	文献[22]	文献[24]	文献[23]
biosphere	64.9%	64.5%	63.1%	64.7%
financial	54.0%	61.9%	65.0%	64.8%
aktors portal	42.6%	49.4%	62.2%	55.0%

方法	目标	输出结果	利用语义信息	摘要对象	评估方式	备注
文献[2]	用户导向	RDF句子	否	模式层	专家评测后，用 vocabulary overlap评价机器给出的结果	借鉴文本摘要方法
文献[20]	用户导向	RDF句子	是	模式层	定性评估，根据2个例子进行分析	构造句子?术语二分图，并利用全球语义网中的信息进行重要性评估
文献[18]	用户导向	核心概念	是	模式层	专家评测与机器结果对比	考虑了概念的普及程度
文献[37]	用户导向	核心概念	否	模式层	n/a	选取网络中的重要节点
文献[21-22]	用户导向	子本体	否	模式层	专家评测与机器结果对比	先选取核心概念，再通过路径连接
文献[23-25]	用户导向	子本体	否	数据层/模式层	比较参考本体和生成本体的相似度	利用了数据层和模式层的信息进行摘要
文献[10,28]	任务导向	子本体	否	数据层	评估空间复杂性和时间复杂性，即摘要前后本体大小变化和一致性检测需要的时间	通过合并等价关系缩小本体，摘要前后本体蕴含的信息量不变
文献[12]	任务导向	子本体	否	模式层	将摘要前后的本体应用于问答系统，根据答案质量判断本体摘要水平	仅针对特定应用，无法保证摘要本体的语义完整性和一致性
文献[13]	任务导向	子本体	否	数据层	对摘要前后的本体进行推理，若结果一致，说明未破坏本体信息	原本体发生变化时，摘要本体也随之变化