For Ontology Alignment (OA), the task is to align semantically equivalent concepts and relations from different ontologies. This task plays a crucial role in many downstream tasks and applications in academia and industry. Since manually aligning ontologies is inefficient and costly, numerous approaches exist to do this automatically. However, most approaches are tailored to specific domains, are rule-based systems or based on feature engineering, and require external knowledge. The most recent advances in the field of OA rely on the widely proven effectiveness of pre-trained language models to represent the human-generated language that describes the entities in an ontology. However, these approaches additionally require sophisticated algorithms or Graph Neural Networks to exploit an ontology’s graphical structure to achieve state-of-the-art performance. In this work, we present NLFOA, or Natural Language Focused Ontology Alignment, which purely focuses on the natural language contained in ontologies to process the ontology’s semantics as well as graphical structure. An evaluation of our approach on common OA datasets shows superior results when finetuning with only a small number of training samples. Additionally, it demonstrates strong results in a zero-shot setting which could be employed in an active learning setup to reduce human labor when manually aligning ontologies significantly.

Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explore its use to address relation extraction tasks. We extend a relational model that has been shown to be effective in solving word analogies and adapt it to the relation extraction problem. Our experiments show that this approach outperforms the state-of-the-art methods on a relation extraction dataset, opening up a new research direction in discovering implicit relations in text through analogical reasoning.

Automatic open-domain Question Answering has been a long standing research challenge in the AI community. IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson. This paper addresses a specific subtask of Deep QA, consisting of predicting the Lexical Answer Type (LAT) of a question. Our approach is completely unsupervised and is based on PRISMATIC, a large-scale lexical knowledge base automatically extracted from a Web corpus. Experiments on the Jeopardy! data shows that it is possible to correctly predict the LAT in a substantial number of questions. This approach can be used for general purpose knowledge acquisition tasks such as frame induction from text.