Summary of Effective Instruction Parsing Plugin For Complex Logical Query Answering on Knowledge Graphs, by Xingrui Zhuo et al.
Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
by Xingrui Zhuo, Jiapu Wang, Gongqing Wu, Shirui Pan, Xindong Wu
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper proposes a new approach called Query Instruction Parsing Plugin (QIPP) for enhancing the generalization of Knowledge Graph Query Embedding (KGQE) models. The QIPP method leverages pre-trained language models to capture latent query patterns from code-like query instructions, which are expressed in an alternative format that utilizes textual variables and nested tuples to convey logical semantics within FOL queries. This approach is designed to address the pattern-entity alignment bias problem in current Query Pattern Learning (QPL) methods, which limits the performance of KGQE models. The paper also proposes a query-guided instruction decoder to adapt query patterns to KGQE models and a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component to enhance the effectiveness of QIPP across various KGQE models. Experimental results demonstrate that the proposed method improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help computers understand complex questions on knowledge graphs. The idea is to create special instructions that explain how to ask questions in a way that computers can understand. This helps computers answer these questions better by using information from the internet and other sources. The method is called Query Instruction Parsing Plugin (QIPP) and it uses artificial intelligence to learn how to create these instructions. It also has some special features like a “decoder” that helps adapt the instructions to different computer models, so they can all work together better. This approach is tested on several computer models and shows that it can improve their performance by a lot. |
Keywords
» Artificial intelligence » Alignment » Decoder » Embedding » Generalization » Knowledge graph » Optimization » Parsing » Semantics
