We are happy to announce that our paper “An Unsupervised Approach for Question Answering Over Knowledge Graphs” has been published in IEEE Access. IEEE Access publishes articles that are of high interest to readers: original, technically correct, and clearly presented. The scope of this journal comprises all IEEE fields of interest, emphasizing applications-oriented and interdisciplinary articles.
Here is the abstract and the link to the paper:
Tree-KGQA: An Unsupervised Approach for Question Answering Over Knowledge Graphs
By Md Rashad Al Hasan Rony
, Debanjan Chaudhuri
, Ricardo Usbeck
, and Jens Lehmann
Most Knowledge Graph-based Question Answering (KGQA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose Tree-KGQA, an unsupervised KGQA system leveraging pre-trained language models and tree-based algorithms. Entity and relation linking are essential components of any KGQA system. We employ several pre-trained language models in the entity linking task to recognize the entities mentioned in the question and obtain the contextual representation for indexing. Furthermore, for relation linking we incorporate a pre-trained language model previously trained for language inference task. Finally, we introduce a novel algorithm for extracting the answer entities from a KG, where we construct a forest of interpretations and introduce tree-walking and tree disambiguation techniques. Our algorithm uses the linked relation and predicts the tree branches that eventually lead to the potential answer entities. The proposed method achieves 4.5% and 7.1% gains in F1 score in entity linking tasks on LC-QuAD 2.0 and LC-QuAD 2.0 (KBpearl) datasets, respectively, and a 5.4% increase in the relation linking task on LC-QuAD 2.0 (KBpearl). The comprehensive evaluations demonstrate that our unsupervised KGQA approach outperforms other supervised state-of-the-art methods on the WebQSP-WD test set (1.4% increase in F1 score) – without training on the target dataset.