Leveraging Pre-trained Language Models for Time Interval Prediction in Text-Enhanced Temporal Knowledge Graphs

Most knowledge graph completion (KGC) methods learn latent representations of entities and relations of a given graph by mapping them into a vector space. Although the majority of these methods focus on static knowledge graphs, a large number of publicly available KGs contain temporal information stating the time instant/period over which a certain fact has been true. Such graphs are often known as temporal knowledge graphs. Furthermore, knowledge graphs may also contain textual descriptions of entities and relations. Both temporal information and textual descriptions are not taken into account during representation learning by static KGC methods, and only structural information of the graph is leveraged. Recently, some studies have used temporal information to improve link prediction, yet they do not exploit textual descriptions and do not support inductive inference (prediction on entities that have not been seen in training). We propose a novel framework called TEMT that exploits the power of pre-trained language models (PLMs) for text-enhanced temporal knowledge graph completion. The knowledge stored in the parameters of a PLM allows TEMT to produce rich semantic representations of facts and to generalize on previously unseen entities. TEMT leverages textual and temporal information available in a KG, treats them separately, and fuses them to get plausibility scores of facts. Unlike previous approaches, TEMT effectively captures dependencies across different time points and enables predictions on unseen entities. To assess the performance of TEMT, we carried out several experiments including time interval prediction, both in transductive and inductive settings, and triple classification. The experimental results show that TEMT is competitive with the state-of-the-art.Comment: 10 pages, 3 figure

Are knowledge graph embedding models biased, or is it the data that they are trained on?

Recent studies on bias analysis of knowledge graph (KG) embedding models focus primarily on altering the models such that sensitive features are dealt with differently from other features. The underlying implication is that the models cause bias, or that it is their task to solve it. In this paper we argue that the problem is not caused by the models but by the data, and that it is the responsibility of the expert to ensure that the data is representative for the intended goal. To support this claim, we experiment with two different knowledge graphs and show that the bias is not only present in the models, but also in the data. Next, we show that by adding new samples to balance the distribution of facts with regards to specifc sensitive features, we can reduce the bias in the models

Are knowledge graph embedding models biased, or is it the data that they are trained on?

Recent studies on bias analysis of knowledge graph (KG) embedding models focus primarily on altering the models such that sensitive features are dealt with differently from other features. The underlying implication is that the models cause bias, or that it is their task to solve it. In this paper we argue that the problem is not caused by the models but by the data, and that it is the responsibility of the expert to ensure that the data is representative for the intended goal. To support this claim, we experiment with two different knowledge graphs and show that the bias is not only present in the models, but also in the data. Next, we show that by adding new samples to balance the distribution of facts with regards to specifc sensitive features, we can reduce the bias in the models