Link Prediction in Knowledge Graphs with Numeric Triples Using Clustering

Abstract

Knowledge graphs (KG) include large amounts of structured data in many different domains. Knowledge or information is captured by entities and relationships between them in KG. One of the open problems in knowledge graphs area is prediction, that is predicting new relationships or links between the given existing entities in KG. A recent approach in graph-based learning problems is embedding, in which graphs are represented as low-dimensional vectors. Then, it is easier to make link predictions using these vector representations. We also use graph embedding for graph representations. A sub-problem of link prediction in KG is the link prediction in the presence of literal values, and specifically numeric values, on the receiving end of links. This is a harder problem because of the numeric literal values taking arbitrary values. For such entries link prediction models cannot work, because numeric entities are not embedded in the vector space. There are several studies in this area, but they are all complex approaches. In this study, we propose a novel approach for link prediction in KG in the presence of numerical values. To overcome the embedding problem of numeric values, we used a clustering approach for clustering these numerical values in a knowledge graph and then used the clusters for performing link prediction. Then we clustered the numerical values to enhance the prediction rates and evaluated our method on a part of Freebase knowledge graph, which includes entities, relations, and numerical literals. Test results show that a considerable increase in link prediction rate can be achieved in comparison to previous studies.