TY - GEN
T1 - BEAR
T2 - 21st International Conference on Service-Oriented Computing, ICSOC 2023
AU - Yu, Shuang
AU - Huang, Tao
AU - Liu, Mingyi
AU - Wang, Zhongjie
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Knowledge graph construction
KW - Large language model
KW - Service domain knowledge graph
KW - Service domain ontology
UR - https://www.scopus.com/pages/publications/85178217581
U2 - 10.1007/978-3-031-48421-6_23
DO - 10.1007/978-3-031-48421-6_23
M3 - 会议稿件
AN - SCOPUS:85178217581
SN - 9783031484209
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 339
EP - 346
BT - Service-Oriented Computing - 21st International Conference, ICSOC 2023, Proceedings
A2 - Monti, Flavia
A2 - Mecella, Massimo
A2 - Rinderle-Ma, Stefanie
A2 - Ruiz Cortés, Antonio
A2 - Zheng, Zibin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 28 November 2023 through 1 December 2023
ER -