Papers with representation learning techniques
Accurate Text-Enhanced Knowledge Graph Representation Learning (N18-1)
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| Challenge: | Existing representation learning methods for knowledge graph representation do not consider the ambiguity of relations and entities. |
| Approach: | They propose a text-enhanced knowledge graph representation learning method which exploits the entity descriptions and triple-specific relation mention to enhance representations. |
| Outcome: | The proposed method outperforms existing representation learning models on link prediction and triple classification tasks and significantly outperformed existing models. |
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors (N18-1)
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| Challenge: | Ontologies compartmentalize types and relations in a domain and require a process to establish alignments between entities to unify and extend existing knowledge. |
| Approach: | They propose a method which refines pre-trained word vectors to derivate ontological entity descriptions tailored to the ontology matching task. |
| Outcome: | The proposed method improves ontology matching performance over the current state-of-the-art. |
Knowledge Graph Embedding Compression (2020.acl-main)
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| Challenge: | Knowledge graph (KG) embedding techniques that learn continuous embedds of entities and relations consume a large amount of storage and memory. |
| Approach: | They propose a method that compresses the KG embedding layer by representing each entity in the KA as a vector of discrete codes and then composes the embeddables from these codes. |
| Outcome: | The proposed approach achieves 50-1000x compression of embeddings with a minor loss in performance on standard KG embeddable evaluations and retains the ability to perform reasoning tasks such as KG inference. |
Unlocking the Power of Large Language Models for Entity Alignment (2024.acl-long)
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Xuhui Jiang, Yinghan Shen, Zhichao Shi, Chengjin Xu, Wei Li, Zixuan Li, Jian Guo, Huawei Shen, Yuanzhuo Wang
| Challenge: | Entity Alignment (EA) is a crucial step in unifying data from heterogeneous sources and plays a critical role in data-driven AI applications. |
| Approach: | They propose a framework that incorporates large language models to improve EA. |
| Outcome: | The proposed framework incorporates large language models (LLMs) to improve EA accuracy while preserving efficiency. |