Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?

Abstract The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies

Watset : automatic induction of synsets from a graph of synonyms

This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources

A Semi-automated Ontology Construction for Legal Question Answering

Open Access via Springer Compact Agreement.Peer reviewedPublisher PD

Multilingual Knowledge Graphs and Low-Resource Languages: A Review

There is a lack of multilingual data to support applications in a large number of languages, especially for low-resource languages. Knowledge graphs (KG) could contribute to closing the gap of language support by providing easily accessible, machine-readable, multilingual linked data, which can be reused across applications. In this paper, we provide an overview of work in the domain of multilingual KGs with a focus on low-resource languages. We review the current state of multilingual KGs along with the different aspects that are crucial for creating KGs with language coverage in mind. Special consideration is given to challenges particular to low-resource languages in KGs. We further provide an overview of applications that yield multilingual KG information as well as downstream applications reusing such multilingual data. Finally, we explore open problems regarding multilingual KGs with a focus on low-resource languages