计算机科学
领域知识
领域(数学分析)
知识图
服务(商务)
图形
本体论
领域工程
数据科学
万维网
软件工程
知识管理
情报检索
理论计算机科学
程序设计语言
经济
哲学
软件系统
经济
基于构件的软件工程
数学分析
认识论
软件
数学
作者
Shuang Yu,Tao Huang,Mingyi Liu,Zhongjie Wang
标识
DOI:10.1007/978-3-031-48421-6_23
摘要
Knowledge graph (KG), as a novel knowledge storage approach, has been widely used in various domains. In the service computing community, researchers tried to harness the enormous potential of KG to tackle domain-specific tasks. However, the lack of an openly available service domain KG limits the in-depth exploration of KGs in domain-specific applications. Building a service domain KG primarily faces two challenges: first, the diversity and complexity of service domain knowledge, and second, the dispersion of domain knowledge and the lack of annotated data. These challenges discouraged costly investment in large, high-quality domain-specific KGs by researchers. In this paper, we present the construction of a service domain KG called BEAR. We design a comprehensive service domain knowledge ontology to automatically generate the prompts for the Large Language Model (LLM) and employ LLM to implement a zero-shot method to extract high-quality knowledge. A series of experiments are conducted to demonstrate the feasibility of graph construction process and showcase the richness of content available from BEAR. Currently, BEAR includes 133, 906 nodes, 169, 159 relations, and about 424, 000 factual knowledge as attributes, which is available through github.com/HTXone/BEAR.
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