Abstract
Knowledge graphs (KGs) are widely used for modeling scholarly communication, performing scientometric analyses, and supporting a variety of intelligent services to explore the literature and predict research dynamics. However, they often suffer from incompleteness (e.g., missing affiliations, references, research topics), leading to a reduced scope and quality of the resulting analyses. This issue is usually tackled by computing knowledge graph embeddings (KGEs) and applying link prediction techniques. However, only a few KGE models are capable of taking weights of facts in the knowledge graph into account. Such weights can have different meanings, e.g. describe the degree of association or the degree of truth of a certain triple. In this paper, we propose the Weighted Triple Loss , a new loss function for KGE models that takes full advantage of the additional numerical weights on facts and it is even tolerant to incorrect weights. We also extend the Rule Loss , a loss function that is able to exploit a set of logical rules, in order to work with weighted triples. The evaluation of our solutions on several knowledge graphs indicates significant performance improvements with respect to the state of the art. Our main use case is the large-scale AIDA knowledge graph, which describes 21 million research articles. Our approach enables to complete information about affiliation types, countries, and research topics, greatly improving the scope of the resulting scientometrics analyses and providing better support to systems for monitoring and predicting research dynamics.
Highlights
Science of Science is a rapidly emerging research field that studies the interactions among scientific agents in order to develop tools and policies for accelerating the scientific process [12]
We introduce the Weighted Rule Loss, a loss function that extends the Rule Loss [35] in order to work with weighted triples
Graph Embedding (WGE), the version of DistMult that incorporates the Weighted Triple Loss function, ii) the Uncertain Knowledge Graphs (KGs) Embedding (UKGE), which uses the loss function presented in Chen et al [9], iii) DistMult [65], iv) TransE [4], and v) ComplEx [55] on several datasets
Summary
Science of Science is a rapidly emerging research field that studies the interactions among scientific agents in order to develop tools and policies for accelerating the scientific process [12]. The large increase in the volume of scholarly outputs, such as articles, data sets, and software packages, yields unprecedented opportunities to this field, and results in many challenges. This mass of available information has the potential to support a new generation of intelligent systems for exploring and improving research efforts, but at the same time poses a risk to drastically reduce the effectiveness of previous approaches for analysing available information.
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