Summary of Knowledge Graph Completion Using Structural and Textual Embeddings, by Sakher Khalil Alqaaidi et al.
Knowledge Graph Completion using Structural and Textual Embeddings
by Sakher Khalil Alqaaidi, Krzysztof Kochut
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 relations prediction model for Knowledge Graphs (KGs) is a novel approach that combines textual and structural information within KGs to complete missing relations between existing nodes. This study integrates walks-based embeddings with language model embeddings, demonstrating competitive results in the relation prediction task on a widely used dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to complete incomplete Knowledge Graphs (KGs) by predicting relations between existing nodes. The approach combines different types of information from KGs and language models to better represent nodes. The result is a model that performs well in completing missing relations, which can be useful for applications like question-answering and recommendation systems. |
Keywords
* Artificial intelligence * Language model * Question answering
