Knowledge Graph Embedding for Link Prediction: A Comparative Analysis
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TLDR
In this paper, a comprehensive comparison of embedding-based link prediction methods is provided, extending the dimensions of analysis beyond what is commonly available in the literature, and the authors experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks.Abstract:
Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.read more
Citations
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A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
TL;DR: This paper surveys 23 recent embedding-based entity alignment approaches and categorizes them based on their techniques and characteristics, and proposes a new KG sampling algorithm, with which to generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation.
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A Survey on Knowledge Graph Embeddings for Link Prediction
TL;DR: A comprehensive survey on KG-embedding models for link prediction in knowledge graphs is provided in this paper, where the authors investigate several representative models that are classified into five categories and provide some new insights into the strengths and weaknesses of existing models.
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