Knowledge Graph Embedding for Link Prediction
By Andrea Rossi, Donatella Firmani, Antonio Matinata, Paolo Merialdo, Denilson Barbosa. ACM Transactions on Knowledge Discovery from Data, 2020
415 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/8c93f3cecf79bd9f8d021f589d095305e281dd2f
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In Artificial Intelligence
Citations in the corpus
- Knowledge Graphs: Opportunities and Challenges (2023, background): “The three main types of triplet fact-based knowledge graph embedding approaches are tensor factorization-based, translation-based, and neural network-based methods (Rossi et al. 2021).”
- Multi-Modal Knowledge Graph Construction and Application: A Survey (2022, background): “Then in the prediction stage, the most matching h in (?, r, t) is found by ranking all candidate head entities h∗ according to a score function like argmaxh∗ φ(h ∗, r, t), where the score function is diverse in different embedding models [154].”
- Neural, symbolic and neural-symbolic reasoning on knowledge graphs (2021, background): “More details of knowledge graph embeddings can be found in (Ji et al., 2020; Rossi et al., 2020).”