Summary of Question Answering Over Spatio-temporal Knowledge Graph, by Xinbang Dai et al.
Question Answering Over Spatio-Temporal Knowledge Graph
by Xinbang Dai, Huiying Li, Guilin Qi
First submitted to arxiv on: 18 Feb 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 research paper presents a novel approach to answering questions that involve spatio-temporal information, such as “What was the population of New York City in 2010?” or “Where did Napoleon Bonaparte live during his exile?” The paper introduces Spatio-Temporal Knowledge Graphs (STKGs) which extend traditional knowledge graphs by incorporating time and location information. However, there is a lack of comprehensive datasets that can be used to train models for this task. To address this issue, the authors create STQAD, a dataset comprising 10,000 natural language questions that require spatio-temporal knowledge graph question answering (STKGQA). They also propose a new approach called STCQA, which uses a novel STKG embedding method named STComplEx to extract temporal and spatial information from a question and retrieve accurate answers from the STKG. The authors demonstrate the quality of their dataset and the effectiveness of their STKGQA method through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at answering questions that involve time and location. Right now, computers are great at answering simple questions like “What’s the capital of France?” but they struggle when the question involves both time and place, like “Where was Albert Einstein born in 1879?” The authors of this paper created a special kind of map called a spatio-temporal knowledge graph that helps computers understand these kinds of questions better. They also made a big dataset with 10,000 questions that require computers to use this new kind of map. By doing this, they want to make it easier for computers to answer complex questions that involve both time and location. |
Keywords
» Artificial intelligence » Embedding » Knowledge graph » Question answering
