Summary of Bridge: a Unified Framework to Knowledge Graph Completion Via Language Models and Knowledge Representation, by Qiao Qiao et al.
Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation
by Qiao Qiao, Yuepei Li, Qing Wang, Kang Zhou, Qi Li
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 Bridge framework for knowledge graph completion (KGC) jointly encodes structural and semantic information from existing Knowledge Graphs (KGs). This is achieved by strategically encoding entities and relations separately using pre-trained language models (PLMs), allowing for better utilization of semantic knowledge and structured representation learning. A self-supervised representation learning method, BYOL, is employed to fine-tune PLMs with two different views of a triple, created by separating the triple into two parts without altering its semantic information. The Bridge framework outperforms state-of-the-art (SOTA) models on three benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big bookshelf filled with books about people, places, and things. You want to find a new book that fits perfectly between two existing ones, but it’s not there! That’s where knowledge graph completion comes in. Existing methods try to solve this problem using either the bookshelf structure or what we know about each book, but they’re not very good at it. We created a new way called Bridge to combine both approaches and learn from the books themselves. We even developed a special trick to make sure our method doesn’t change the meaning of the books while learning. And guess what? Our Bridge framework outperforms other methods on three big bookshelves! |
Keywords
» Artificial intelligence » Knowledge graph » Representation learning » Self supervised
