In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one unique negative pattern on each dataset that is hidden from the rule-based approach. Our findings add a unique perspective on the performance difference of different model classes for knowledge graph completion: Models can achieve a predictive performance advantage by penalizing scores of incorrect facts opposed to providing high scores for correct facts.

In this article, I will mostly concentrate on the Hearst patterns, implementation and usage for hypernym extraction. However, I will use Named Entity Recognition (NER) and a dataset of patents; so I recommend checking my previous post in this cycle. Why do we care about patterns in the context of NLP? Because they significantly reduce and simplifies work, basically, it is a simple model. Despite being in the era of Transformer Neural Networks, patterns still can be beneficial.

In this article, I will mostly concentrate on the Hearst patterns, implementation and usage for hypernym extraction. However, I will use Named Entity Recognition (NER) and a dataset of patents; so I recommend checking my previous post in this cycle. Why do we care about patterns in the context of NLP? Because they significantly reduce and simplifies work, basically, it is a simple model. Despite being in the era of Transformer Neural Networks, patterns still can be beneficial.

A folksonomy refers to a collection of user-defined tags with which users describe contents published  on the Web. With the flourish of Web 2.0, folksonomies have become an important mean to develop the Semantic Web. Because tags in folksonomies are authored freely, there is a need to understand the structure and semantics of these tags in various applications. In this paper, we propose a learning approach to create an ontology that captures the hierarchical semantic structure of folksonomies. Our experimental results on two different genres of real world data sets show that our method can effectively learn the ontology structure from the folksonomies.