Explaining Semantic Text Similarity in Knowledge Graphs

In this paper we explore the application of text similarity for building text-rich knowledge graphs, where nodes describe concepts that relate semantically to each other. Semantic text similarity is a basic task in natural language processing (NLP) that aims at measuring the semantic relatedness of two texts. Transformer-based encoders like BERT combined with techniques like contrastive learning are currently the state-of-the-art methods in the literature. However, these methods act as black boxes where the similarity score between two texts cannot be directly explained from their components (e.g., words or sentences). In this work, we propose a method for similarity explainability for texts that are semantically connected to each other in a knowledge graph. To demonstrate the usefulness of this method, we use the Agenda 2030 which consists of a graph of sustainable development goals (SDGs), their subgoals and the indicators proposed for their achievement. Experiments carried out on this dataset show that the proposed explanations not only provide us with explanations about the computed similarity score but also they allow us to improve the accuracy of the predicted links between concepts.