Summary of Joint Multi-facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph, by Rikui Huang et al.
Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph
by Rikui Huang, Wei Wei, Xiaoye Qu, Wenfeng Xie, Xianling Mao, Dangyang Chen
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Joint Multi Facts Reasoning Network (JMFRN), a novel approach to tackle complex temporal questions in Temporal Knowledge Graphs (TKGs). Existing TKG question answering models struggle with questions containing multiple temporal facts, leading to poor performance. JMFRN addresses this limitation by jointly reasoning over multiple temporal facts for accurate answer retrieval. The method first retrieves relevant facts from the TKG and then employs entity-aware and time-aware attention modules to aggregate information. Additionally, an answer type discrimination task is introduced to filter out incorrect types of answers. Experimental results demonstrate significant performance gains on the TimeQuestions benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how we can answer complex questions about things that happened in the past or will happen in the future. Right now, computers have trouble answering these kinds of questions because they only look at one piece of information at a time. The researchers propose a new way to answer questions by looking at multiple pieces of information together. They create a special network that can handle this kind of question and test it on a benchmark dataset. The results show that their method performs much better than the current state-of-the-art. |
Keywords
» Artificial intelligence » Attention » Question answering
