An empirical study of temporal knowledge graph and link prediction using longitudinal editorial data

Natural Language Processing (NLP) is an application of Machine Learning (ML) which focuses on deriving useful and underlying facts through the semantics in articles to automatically extract insights about how information can be pictured, presented, and interpreted.  Knowledge graphs, as a promising medium for carrying the structured linguistical piece, can be a desired target for learning and visualization through artificial neural networks, in order to identify the absent information and understand the hidden transitive relationship among them. In this study, we aim to construct Temporal Knowledge Graphs of sematic information to facilitate better visualization of editorial data. Further, A neural network-based approach for link prediction is carried out on the constructed knowledge graphs. This study uses news articles in English language, from New York Times (NYT) collected over a period of time for experiments. The sentences in these articles can be decomposed into Part-Of-Speech (POS) Tags to give a triple t = {sub, pred, obj}. A directed Graph G (V, E) is constructed using POS tags, such that the Set of Vertices is the grammatical constructs that appear in the sentence and the Set of Edges is the directed relation between the constructs. The main challenge that arises with knowledge graphs is the storage constraints that arise in lieu of storing the graph information. The study proposes ways by which this can be handled. Once these graphs are constructed, a neural architecture is trained to learn the graph embeddings which can be utilized to predict the potentially missing links which are transitive in nature. The results are evaluated using learning-to-rank metrics such Mean Reciprocal Rank (MRR).