Brain-Inspired Search Engine Assistant Based on Knowledge Graph
Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) syste...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2023-08, Vol.34 (8), p.4386-4400 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Zhao, Xuejiao Chen, Huanhuan Xing, Zhenchang Miao, Chunyan |
| description | Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers' information needs. |
| doi_str_mv | 10.1109/TNNLS.2021.3113026 |
| format | Article |
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In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. 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| subjects | Brain Brain-inspired system Cognition & reasoning Cognitive ability cognitive process Cognitive processes Constraint modelling Decision making Feature extraction Graph theory Knowledge engineering knowledge graph Knowledge representation Multilayers Prototypes Queries question and answering (Q&A) system Reasoning Search engines Search process Semantics |
| title | Brain-Inspired Search Engine Assistant Based on Knowledge Graph |
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