Summary of The Integration Of Semantic and Structural Knowledge in Knowledge Graph Entity Typing, by Muzhi Li et al.
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing
by Muzhi Li, Minda Hu, Irwin King, Ho-fung Leung
First submitted to arxiv on: 12 Apr 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 The proposed Semantic and Structure-aware KG Entity Typing (SSET) framework aims to predict missing type annotations for entities in knowledge graphs by utilizing both semantic and structural knowledge. The framework consists of three modules: the Semantic Knowledge Encoding module, which encodes factual knowledge in the KG with a Masked Entity Typing task; the Structural Knowledge Aggregation module, which aggregates knowledge from the multi-hop neighborhood of entities to infer missing types; and the Unsupervised Type Re-ranking module, which utilizes inference results to generate type predictions robust to false-negative samples. Experimental results show that SSET significantly outperforms existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict missing information about things (like “person” or “location”) in large databases of knowledge. This problem is important because it helps computers understand what kind of thing something is, which can be useful for many applications like search engines and language translation. The approach uses two kinds of information: the structure of how things are related to each other, and the meaning of words and phrases used to describe those things. The paper shows that by combining these two types of information, it’s possible to make better predictions about what kind of thing something is. |
Keywords
» Artificial intelligence » Inference » Translation » Unsupervised
