natural-language-processing-8
Entity Alignment
3260 papers • 126 benchmarks • 313 datasets
Entity Alignment is the task of finding entities in two knowledge bases that refer to the same real-world object. It plays a vital role in automatically integrating multiple knowledge bases. Note: results that have incorporated machine translated entity names (introduced in the RDGCN paper) or pre-alignment name embeddings are considered to have used extra training labels (both are marked with "Extra Training Data" in the leaderboard) and are not adhere to a comparable setting with others that have followed the original setting of the benchmark. Source: Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding The task of entity alignment is related to the task of entity resolution which focuses on matching structured entity descriptions in different contexts.
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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
Yingtao Tian, Muhao Chen, C. Zaniolo, Mohan Yang•Fri Nov 11 2016
MTransE, a translation-based model for multilingual knowledge graph embeddings, is proposed to provide a simple and automated solution to achieve cross-lingual knowledge alignment and explore how MTransE preserves the key properties of its monolingual counterpart.
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