CAFE: Knowledge graph completion using neighborhood-aware features

Borrego, Agustín; Ayala, Daniel; Hernández, Inma; Rivero, Carlos R.; Ruiz, David

doi:10.1016/j.engappai.2021.104302

Cited by 22 publications

(14 citation statements)

References 13 publications

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“…Unfortunately, due to the non-deterministic way in which these paths are computed, they may miss relevant information by mere chance. More recently, Borrego et al [27] proposed CAFE, a deterministic approach to exploit the highly connected nature of KGs that does not rely on random paths.…”

Section: Related Workmentioning

confidence: 99%

“…SciCheck 6 is a novel approach for triple classification designed to complete scientific statements in a knowledge graph. It is built on top of the CAFE approach [27] by incorporting a new set of features and heuristics tailored to capture scientific knowledge. SciCheck takes an entire KG in the form of triples as input, and produces one neuralbased classifier for each relation in the KG as output.…”

Section: Scicheckmentioning

confidence: 99%

“…However, in order to train a classifier, negative triples are also needed. To do this, SciCheck follows the same approach as many other related techniques [27,28,38,51,52,53] and generates negative triples by corrupting a positive triple < h, r, t > and replacing t with t ′ , in such a way that < h, r, t ′ > is not part of the original graph.…”

Section: Generating Negative Examplesmentioning

confidence: 99%

“…These methods typically use KG Embedding models (e.g. TransE [24], RotatE [25], ComplEx [26]), path-based features [27,28], or Graph Neural Networks [29].…”

Section: Introductionmentioning

confidence: 99%

“…To address the above issue, in this paper, we introduce SciCheck, a new approach for completing scientific facts in knowledge graphs of research concepts. SciCheck is built on top of the CAFE approach [27] and introduces several new features and heuristics for the scholarly domain.…”

Section: Introductionmentioning

confidence: 99%

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Completing Scientific Facts in Knowledge Graphs of Research Concepts

et al. 2022

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In the last few years, we have witnessed the emergence of several knowledge graphs that explicitly describe research knowledge with the aim of enabling intelligent systems for supporting and accelerating the scientific process. These resources typically characterize a set of entities in this space (e.g., tasks, methods, evaluation techniques, proteins, chemicals), their relations, and the relevant actors (e.g., researchers, organizations) and documents (e.g., articles, books). However, they are usually very partial representations of the actual research knowledge and may miss several relevant facts. In this paper, we introduce SciCheck, a new triple classification approach for completing scientific statements in knowledge graphs. SciCheck was evaluated against nine alternative approaches on seven benchmarks, yielding excellent results. Finally, we provide a real-world use case and applied SciCheck to the Artificial Intelligence Knowledge Graph (AI-KG), a large-scale automatically-generated open knowledge graph including 1.2M statements extracted from the 333K most cited articles in the field of Artificial Intelligence, and generated a new version of this knowledge graph with 300K additional triples.

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Section: Related Workmentioning

confidence: 99%

Section: Scicheckmentioning

confidence: 99%

Section: Generating Negative Examplesmentioning

confidence: 99%

“…These methods typically use KG Embedding models (e.g. TransE [24], RotatE [25], ComplEx [26]), path-based features [27,28], or Graph Neural Networks [29].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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