Papers Entity Alignment

SecureBoost: A Lossless Federated Learning Framework

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to…

Recurrent Skipping Networks for Entity Alignment

We consider the problem of learning knowledge graph (KG) embeddings for entity alignment (EA). Current methods use the embedding models mainly focusing on triple-level learning, which lacks the ability of capturing long-…

Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks

Multilingual knowledge graphs (KGs) such as DBpedia and YAGO contain structured knowledge of entities in several distinct languages, and they are useful resources for cross-lingual AI and NLP applications. Cross-lingual …

AceKG: A Large-scale Knowledge Graph for Academic Data Mining

Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we pr…

Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. How…

DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors

Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the sa…

Creating a Translation Matrix of the Bible's Names Across 591 Languages

Supersense Tagging with a Combination of Character, Subword, and Word-level Representations

Recently, there has been increased interest in utilizing characters or subwords for natural language processing (NLP) tasks. However, the effect of utilizing character, subword, and word-level information simultaneously …

Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods…

Iterative Entity Alignment via Joint Knowledge Embeddings

Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly man…

Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lin…