Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding (1708.05045v2)

Abstract: 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 rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.

Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

The paper entitled "Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding" presents a novel methodology for effectively addressing the challenges associated with entity alignment across knowledge bases (KBs) in different languages. The authors propose a joint embedding model, termed JAPE, that leverages both relationship structures and attribute correlations within KBs, thereby facilitating improved entity alignment without reliance on machine translation.

Key Contributions

1. Attribute-Preserving Embedding Model: The core proposition of this paper is a joint embedding model that preserves attribute data alongside relationship data within a unified vector space. By integrating attributes, the model fills a gap present in previous approaches that often overlooked this critical source of information in cross-lingual scenarios.
2. Experimental Validation: The authors conduct experiments using DBpedia datasets in multiple languages, showing that the proposed method significantly outperforms existing state-of-the-art methods such as JE and MTransE in terms of alignment accuracy.
3. Complementarity with Machine Translation: Although the model does not inherently rely on machine translation, the authors illustrate how JAPE can be effectively combined with translation-based techniques to further enhance alignment performance, demonstrating JAPE’s utility in scenarios where language translations still play a role.

Methodology and Analysis

The methodology introduced—joint attribute-preserving embedding—incorporates two primary components:

• Structure Embedding (SE): Building on the principles of translation-based embedding models such as TransE, SE integrates cross-lingual KB structures to construct a coherent representation space where aligned entities across different languages are close to one another in the vector space.
• Attribute Embedding (AE): This component captures the correlations between attributes by abstracting attribute values to range types and employing a Skip-gram-inspired approach to embedding. AE enhances alignment accuracy by clustering entities with high attribute correlations, thereby refining the embeddings obtained from SE.

The authors present a rigorous evaluation of JAPE against baseline embedding approaches, underscoring its superior performance in entity alignment tasks and its scalability to large-scale datasets. The comprehensive experimentation involves varied proportions of seed alignments, confirming JAPE’s robustness even with minimal seed data.

Implications and Future Directions

The introduction of attribute-preserving embeddings marks a meaningful advancement in cross-lingual entity alignment by addressing the incomplete utilization of attribute data in traditional models. This work opens several avenues for future research:

• Incorporating cross-lingual word embedding techniques to handle diverse and complex attribute values could further enhance the applicability of the model in broader, more heterogeneous settings.
• Extending the framework to leverage hyperplane projections might mitigate challenges associated with multi-mapping relations and thus improve the model's robustness.

The proposed approach holds significant implications for the construction of unified, coherent knowledge bases across languages, enhancing applications in areas such as international semantic web projects, multilingual information retrieval, and AI-driven knowledge discovery systems.

In conclusion, the presented paper marks a significant contribution to the field of cross-lingual KB alignment by innovatively leveraging both structural and attribute-based information, pushing the boundaries of current methodologies and setting a benchmark for future research in this domain.