Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment (2010.03249v2)

Abstract: Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performances by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective evaluation, we propose a hard experimental setting where we select equivalent entity pairs with very different names as the test set. Under both the regular and hard settings, our method achieves significant improvements ($5.10\%$ on average Hits@$1$ in DBP$15$k) over $12$ baselines in cross-lingual and monolingual datasets. Ablation studies on different subgraphs and a case study about attribute types further demonstrate the effectiveness of our method. Source code and data can be found at https://github.com/thunlp/explore-and-evaluate.

• The paper presents AttrGNN, a Graph Neural Network model that enhances entity alignment by integrating both attribute and relation triples in knowledge graphs.
• AttrGNN employs multiple channels (name, literal, digital, structure) and attention mechanisms to process diverse attribute types and their importance for alignment.
• Experimental results show AttrGNN significantly outperforms baselines on benchmark datasets, particularly in challenging settings designed to mitigate name bias.

Overview of Entity Alignment through Attributes, Values, and Structures

The paper "Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment" presents a comprehensive investigation into enhancing entity alignment (EA) in knowledge graphs (KGs) by integrating attribute triples alongside the traditional structure-focused alignment methods. Entity alignment is crucial for creating a unified knowledge representation by linking equivalent entities across diverse KGs. The research introduces an innovative approach utilizing a Graph Neural Network (GNN) model named AttrGNN, which leverages both relation and attribute triples to improve EA performance.

Key Contributions and Methods

The authors address two primary challenges in EA: the incorporation of attributes effectively and the dataset bias towards entity names in existing EA benchmarks.

1. Attributed Graph Neural Network (AttrGNN): The paper proposes AttrGNN, which fuses attribute and relation triples within a unified learning framework. Four distinct GNN channels are employed:
   • Name Channel: Focuses on entity name similarities.
   • Literal Channel: Processes literal string attributes.
   • Digital Channel: Dedicated to numerical attributes.
   • Structure Channel: Exclusively models relation triples.
2. Graph Partition Strategy: To tackle the diversity in attribute types, the authors partition the KG into subgraphs based on attribute characteristics, enabling tailored similarity metrics for different data types.
3. Attention Mechanism with Attribute Importance: AttrGNN incorporates an attention-based value encoder, which dynamically determines the importance of various attributes and values for alignment, thereby improving the model's discriminative power.
4. Hard Experimental Setting: Recognizing the name bias in existing datasets, a new evaluation framework is devised. This setup emphasizes aligning entities with dissimilar names, thereby providing a more rigorous and realistic assessment of EA methodologies.

Experimental Findings

Throughout rigorous testing on both cross-lingual (DBP15k) and monolingual (DWY100k) datasets, AttrGNN demonstrates significant improvements over 12 baseline models. Notably, the model shows an average increase of 5.10% in Hits@1 on DBP15k datasets under regular settings. AttrGNN’s advantage is further validated under the hard setting, maintaining a robust performance despite the stringent evaluation conditions.

Implications and Future Directions

The approach presented in this paper broadens the scope of entity alignment by emphasizing non-structural features, offering new pathways for enhancing the integration of diverse knowledge graphs. The effort to address dataset biases reflects a significant step towards more accurate and practical EA evaluations.

The findings indicate promising avenues for future research, particularly in refining attribute weighting mechanisms and exploring deeper integration of multimodal KG data. While AttrGNN improves alignment by leveraging attribute disparities, it can benefit from further advancements in representation learning models capable of capturing complex interrelations between varied data types within KGs. Incorporating numerical reasoning skills into LLMs, as suggested in the paper, could substantially refine the EA process, especially for datasets where attribute values play a crucial role.

Overall, AttrGNN presents a comprehensive framework addressing key limitations in traditional EA methods, thus setting a foundation for future innovations in the domain of knowledge graph alignment.