Summary of Open Knowledge Base Canonicalization with Multi-task Learning, by Bingchen Liu et al.
Open Knowledge Base Canonicalization with Multi-task Learning
by Bingchen Liu, Huang Peng, Weixin Zeng, Xiang Zhao, Shijun Liu, Li Pan
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 MulCanon framework tackles open knowledge base (OKB) canonicalization by combining clustering and knowledge graph embedding (KGE) with a multi-task learning approach. By unifying the learning objectives of these subtasks, MulCanon achieves competitive results on popular OKB canonicalization benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MulCanon helps remove redundancy and ambiguity in OKBs, which are essential for search engines and other web applications. The framework combines clustering algorithms with KGE to create more accurate representations of noun phrases and relational phrases. This approach is promising for improving the quality of knowledge bases on the internet. |
Keywords
* Artificial intelligence * Clustering * Embedding * Knowledge base * Knowledge graph * Multi task
