Entity Alignment between Knowledge Graphs Using Attribute Embeddings

Abstract

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

“…MTransE [5] WK3l-15K, WK3l-120K, CN3l H@10(, MR) yes IPTransE [29] DFB-{1,2,3} H@{1,10}, MR yes JAPE [19] DBP15K(JAPE) H@{1,10,50}, MR yes KDCoE [4] WK3l-60K H@{1,10}, MR yes BootEA [20] DBP15K(JAPE), DWY100K H@{1,10}, MRR yes SEA [15] WK3l-15K, WK3l-120K H@{1,5,10}, MRR yes MultiKE [28] DWY100K H@{1,10}, MR, MRR yes AttrE [22] DBP-LGD,DBP-GEO,DBP-YAGO H@{1,10}, MR yes RSN [8] custom DBP15K, DWY100K H@{1,10}, MRR yes GCN-Align [24] DBP15K(JAPE) H@{1,10,50} yes CL-GNN [27] DBP15K(JAPE) H@{1,10} yes MuGNN [3] DBP15K(JAPE), DWY100K H@{1,10}, MRR yes NAEA [30] DBP15K(JAPE), DWY100K H@{1,10}, MRR no…”

JarKA: Modeling Attribute Interactions for Cross-lingual Knowledge Alignment

“…To combine the effects from attributes and structures, existing works usually learn a combined embedding for each entity, based on which they infer the alignments. For example, JAPE [9] and AttrE [11] refine the structure embeddings by the closeness of the corresponding attribute embeddings. MultiKE [15] map the attribute and structure embeddings into a unified space.…”

“…However, the structures of some KGs are sparse, making it difficult to learn the structure embeddings accurately. Other efforts are made to incorporate the attribute triplets in the form of entity, attribute, value to learn the attribute embeddings of entities [9,11,12,15]. For example, JAPE [9] embeds attributes via attributes' concurrence.…”

“…Wang et al [12] adopt GCNs to embed entities with the one-hot representations of the attributes. Trsedya et al [11] and MultiKE [15] embed the literal values of the attributes. Despite the existing studies on incorporating the attribute triplets to align entities, there are still unsolved challenges.…”

“…Although the attributes "Manufacturer" and "Body style" and their values in English correspond to certain attribute triplets in Chinese, there are still many attribute such as "Designer" and "Engine" in English that cannot find any counterpart in Chinese. However, if we embed an entity by all its attribute triplets and then compare two entities by their attribute embeddings [11,15], the effects of the same attribute triplets will be diluted by other different ones.…”