Link prediction models based on factual knowledge graphs are commonly used in applications such as search and question answering. However, work investigating social bias in these models has been limited. Previous work focused on knowledge graph embeddings, so more recent classes of models achieving superior results by fine-tuning Transformers have not yet been investigated. We therefore present a model-agnostic approach for bias measurement leveraging fairness metrics to compare bias in knowledge graph embedding-based predictions (KG only) with models that use pre-trained, Transformer-based language models (KG+LM). We further create a dataset to measure gender bias in occupation predictions and assess whether the KG+LM models are more or less biased than KG only models. We find that gender bias tends to be higher for the KG+LM models and analyze potential connections to the accuracy of the models and the data bias inherent in our dataset. Finally, we discuss the limitations and ethical considerations of our work. The repository containing the source code and the data set is publicly available at https://github.com/lena-schwert/comparing-bias-in-KG-models.

In this paper, we propose CHOLAN, a modular approach to target end-to-end entity linking (EL) over knowledge bases. CHOLAN consists of a pipeline of two transformer-based models integrated sequentially to accomplish the EL task. The first transformer model identifies surface forms (entity mentions) in a given text. For each mention, a second transformer model is employed to classify the target entity among a predefined candidates list. The latter transformer is fed by an enriched context captured from the sentence (i.e. local context), and entity description gained from Wikipedia. Such external contexts have not been used in state of the art EL approaches. Our empirical study was conducted on two well-known knowledge bases (i.e., Wikidata and Wikipedia). The empirical results suggest that CHOLAN outperforms state-of-the-art approaches on standard datasets such as CoNLL-AIDA, MSNBC, AQUAINT, ACE2004, and T-REx.