TY - GEN
T1 - Subgraph Search over Neural-Symbolic Graphs
AU - Yuan, Ye
AU - Ma, Delong
AU - Wu, Anbiao
AU - Qin, Jianbin
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2023/7/19
Y1 - 2023/7/19
N2 - In this paper, we propose neural-symbolic graph databases (NSGDs) that extends traditional graph data with content and structural embeddings in every node. The content embeddings can represent unstructured data (e.g., images, videos, and texts), while structural embeddings can be used to deal with incomplete graphs. We can advocate machine learning models (e.g., deep learning) to transform unstructured data and graph nodes to these embeddings. NSGDs can support a wide range of applications (e.g., online recommendation and natural language question answering) in social-media networks, multi-modal knowledge graphs and etc. As a typical search over graphs, we study subgraph search over a large NSGD, called neural-symbolic subgraph matching (NSMatch) that includes a novel ranking search function. Specifically, we develop a general algorithmic framework to process NSMatch efficiently. Using real-life multi-modal graphs, we experimentally verify the effectiveness, scalability and efficiency of NSMatch.
AB - In this paper, we propose neural-symbolic graph databases (NSGDs) that extends traditional graph data with content and structural embeddings in every node. The content embeddings can represent unstructured data (e.g., images, videos, and texts), while structural embeddings can be used to deal with incomplete graphs. We can advocate machine learning models (e.g., deep learning) to transform unstructured data and graph nodes to these embeddings. NSGDs can support a wide range of applications (e.g., online recommendation and natural language question answering) in social-media networks, multi-modal knowledge graphs and etc. As a typical search over graphs, we study subgraph search over a large NSGD, called neural-symbolic subgraph matching (NSMatch) that includes a novel ranking search function. Specifically, we develop a general algorithmic framework to process NSMatch efficiently. Using real-life multi-modal graphs, we experimentally verify the effectiveness, scalability and efficiency of NSMatch.
KW - embedding
KW - neural-symbolic
KW - subgraph search
UR - http://www.scopus.com/inward/record.url?scp=85168685924&partnerID=8YFLogxK
U2 - 10.1145/3539618.3591773
DO - 10.1145/3539618.3591773
M3 - Conference contribution
AN - SCOPUS:85168685924
T3 - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 612
EP - 621
BT - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery, Inc
T2 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023
Y2 - 23 July 2023 through 27 July 2023
ER -