11 research outputs found
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
In this work, we take a closer look at the evaluation of two families of
methods for enriching information from knowledge graphs: Link Prediction and
Entity Alignment. In the current experimental setting, multiple different
scores are employed to assess different aspects of model performance. We
analyze the informativeness of these evaluation measures and identify several
shortcomings. In particular, we demonstrate that all existing scores can hardly
be used to compare results across different datasets. Moreover, we demonstrate
that varying size of the test size automatically has impact on the performance
of the same model based on commonly used metrics for the Entity Alignment task.
We show that this leads to various problems in the interpretation of results,
which may support misleading conclusions. Therefore, we propose adjustments to
the evaluation and demonstrate empirically how this supports a fair,
comparable, and interpretable assessment of model performance. Our code is
available at https://github.com/mberr/rank-based-evaluation
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Graph neural networks (GNNs) have emerged as a powerful paradigm for
embedding-based entity alignment due to their capability of identifying
isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart
entities usually have non-isomorphic neighborhood structures, which easily
causes GNNs to yield different representations for them. To tackle this
problem, we propose a new KG alignment network, namely AliNet, aiming at
mitigating the non-isomorphism of neighborhood structures in an end-to-end
manner. As the direct neighbors of counterpart entities are usually dissimilar
due to the schema heterogeneity, AliNet introduces distant neighbors to expand
the overlap between their neighborhood structures. It employs an attention
mechanism to highlight helpful distant neighbors and reduce noises. Then, it
controls the aggregation of both direct and distant neighborhood information
using a gating mechanism. We further propose a relation loss to refine entity
representations. We perform thorough experiments with detailed ablation studies
and analyses on five entity alignment datasets, demonstrating the effectiveness
of AliNet.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
Collective Entity Alignment via Adaptive Features
Entity alignment (EA) identifies entities that refer to the same real-world
object but locate in different knowledge graphs (KGs), and has been harnessed
for KG construction and integration. When generating EA results, current
solutions treat entities independently and fail to take into account the
interdependence between entities. To fill this gap, we propose a collective EA
framework. We first employ three representative features, i.e., structural,
semantic and string signals, which are adapted to capture different aspects of
the similarity between entities in heterogeneous KGs. In order to make
collective EA decisions, we formulate EA as the classical stable matching
problem, which is further effectively solved by deferred acceptance algorithm.
Our proposal is evaluated on both cross-lingual and mono-lingual EA benchmarks
against state-of-the-art solutions, and the empirical results verify its
effectiveness and superiority.Comment: ICDE2