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.

Noun phrases and Relation phrases in open knowledge graphs are not canonicalized, leading to an explosion of redundant and ambiguous subject-relation-object triples.Existing approaches to solve this problem take a two-step approach.First, they generate embedding representations for both noun and relation phrases, then a clustering algorithm is used to group them using the embeddings as features.In this work, we propose Canonicalizing Using Variational Autoencoders (CUVA) 1 , a joint model to learn both embeddings and cluster assignments in an end-to-end approach, which leads to a better vector representation for the noun and relation phrases.Our evaluation over multiple benchmarks shows that CUVA outperforms the existing state-of-the-art approaches.Moreover, we introduce CANONICNELL, a novel dataset to evaluate entity canonicalization systems.

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.

Extracting lexico-semantic relations as graphstructured taxonomies, also known as taxonomy construction, has been beneficial in a variety of NLP applications.Recently Graph Neural Network (GNN) has shown to be powerful in successfully tackling many tasks.However, there has been no attempt to exploit GNN to create taxonomies.In this paper, we propose Graph2Taxo, a GNN-based cross-domain transfer framework for the taxonomy construction task.Our main contribution is to learn the latent features of taxonomy construction from existing domains to guide the structure learning of an unseen domain.We also propose a novel method of directed acyclic graph (DAG) generation for taxonomy construction.Specifically, our proposed Graph2Taxo uses a noisy graph constructed from automatically extracted noisy hyponym-hypernym candidate pairs, and a set of taxonomies for some known domains for training.The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain.Experiments on benchmark datasets from science and environment domains show that our approach attains significant improvements correspondingly over the state of the art.