O nto P opulis, a System for Learning Semantic Classes (2022)

Abstract: Ontopopulis is a multilingual weakly supervised terminology learning algorithm which takes on its input a set of seed terms for a semantic category and an unannotated text corpus. The algorithm learns additional terms, which belong to this category. For example, for the category “environmental disasters” the input seed set in English is environmental disaster, water pollution, climate change. Among the highest ranked new terms which the system learns for this semantic class are deforestation, global warming and so on.

## introduction ontologies are knowledge-representation models describing concepts of a domain, their properties and the semantic relations between them. these models are used in natural language processing and other ai systems. recently ontologies have been built and exploited predominantly in the area of biology and medicine. there are also many other domains for which they have been used: remote sensing, education, environment and security, etc. the most fundamental building block of the ontology model are the concepts of the domain. each concept has lexical representation, which shows how the concept is being referred at the level of a specific language. for example, the concept @xcite in english can be referred to as: t v , t v set, television receiver, and telly. words which describe ontology concepts form the lexical layer of the ontology. ontologies are created mainly manually by domain experts, however in populating their lexical layer, terminology learning algorithms have successfully been exploited. in this paper we will describe such a multilingual algorithm which given a set of ontology concepts learns new set of related concepts. for each concept and language under consideration, the language experts define a small seed set of terms, belonging to the concept and its sub-concepts. for example for the concept disaster the seed set for english can be: disaster, f lood, earthquake, f orestf ire, wildf ire. the seed set is then expanded by the algorithm by learning new terms, referring to the same main concept (disaster) and its subconcepts, for example it will learn words like calamity, tsunami, landslide. these terms belong to the category disaster and its subcategories tsunami and landslide the algorithm was named ontop opulis (ontology learning and population). in this paper we describe the algorithm and its application in the domain of environment for english. ## related work the first algorithms for ontology learning from text started to appear about 20 years ago. currently, many approaches are described in the literature; they are designed to learn generic and domain-specific ontology resources. one of the first comprehensive overview of these approaches is presented by @xcite . more recent surveys of ontology learning from text are presented in @xcite and @xcite . the approach in this paper is inspired by an earlier work, described in @xcite . ## ontopopulis ontopopulis is a multilingual algorithm for learning semantic classes. it does not use any language-specific tools or annotations. the algorithm accepts as an input a set of seed terms for each ontology concept under consideration and an unannotated corpus. for example, for the concept disaster the seed set is disaster, wildf ire, earthquake and for the concept environmental disaster it is environmental disaster, water pollution, climate change the algorithm performs two processing steps to learn the new lexical items for the input concepts: (i) feature extraction and (ii) lexical learning.