Leveraging W ikipedia Navigational Templates for Curating Domain-Specific Fuzzy Conceptual Bases (2021)

Abstract: Domain-specific conceptual bases use key concepts to capture domain scope and relevant information. Conceptual bases serve as a foundation for various downstream tasks, including ontology construction, information mapping, and analysis. However, building conceptual bases necessitates domain awareness and takes time. Wikipedia navigational templates offer multiple articles on the same/similar domain. It is possible to use the templates to recognize fundamental concepts that shape the domain. Earlier work in this domain used Wikipedia’s structured and unstructured data to construct open-domain ontologies, domain terminologies, and knowledge bases. We present a novel method for leveraging navigational templates to create domain-specific fuzzy conceptual bases in this work. Our system generates knowledge graphs from the articles mentioned in the template, which we then process using Wikidata and machine learning algorithms. We filter important concepts using fuzzy logic on network metrics to create a crude conceptual base. Finally, the expert helps by refining the conceptual base. We demonstrate our system using an example of RNA virus antiviral drugs.

## introduction domain-specific conceptual bases are a method for grasping the domain at a high level by capturing the notions that generally make up a domain. while ontology focus on formal representations and system of categories encompassing the domain information and conceptual models focus on linking the general ontological categories @xcite , the conceptual bases are abstract models addressing the most crucial concepts that are invariably found in a domain. aside from defining the scope and outlining the concepts, the conceptual bases may be used for a variety of downstream activities, such as developing less abstract conceptual constructs, such as ontology, or applications such as entity mapping in knowledge graphs, creating instances for named entity recognition, and summarizing or analyzing the domain. creating a conceptual base is a difficult task that necessitates a thorough understanding of the domain and a considerable amount of time to establish the importance of concepts. online sources such as wikipedia contain a vast amount of information on many domains @xcite . in this research, we propose a novel approach to create domain-specific conceptual bases using wikipedia navigational templates @xcite . the navigational templates make it simple to connect similar topics invariably. similar topics are present as navigational boxes at the bottom of the article or sidebars on the right side of the article. our system uses knowledge from the articles in the navigational templates and identifies relevant notions consistently present in various articles of the same field. for this, we parse the articles' information and create a basic knowledge graph. we map the information to their wikidata instances and cluster similar concepts. we apply fuzzy rules based on network metrics to decide the importance of concepts. in the end, the expert cleans and refines the resultant conceptual base to create the final version. our specific contributions are: ## proposed method we show the overview of the method in figure 1 . our system consists of two parts: knowledge curator and conceptual base curator. the knowledge curator extracts information from articles and wikidata to construct a basic knowledge graph, and the conceptual base curator employs machine learning techniques for processing and fuzzy rules to filter the relevant concepts. ## related works many researchers have worked on fuzzy ontology creation and their downstream applications, such as generating taxonomies, ontologies, and conceptual models from various data sources. the use of fuzzy logic for creating concept lattices and ontologies has been studied previously by various researchers. there have been studies regarding fuzzy ontology creation @xcite , @xcite , using fuzzy ontology and concept models in various domain-specific tasks and dataset @xcite , @xcite , @xcite . as opposed to the previous work, we employ fuzzy logic using network metrics attributes. there are a few significant open-domain, community-driven projects for structured knowledge creation. dbpedia @xcite extracts structured information in multiple languages from wikipedia infoboxes. yago @xcite has released various versions, and this also uses wikipedia infoboxes. it also employs wikipedia categories to determine the type of information, which is then mapped to wordnet taxonomy. wikidata @xcite a collaborative database, also links wikipedia data with unique identifiers. apart from the communitydriven projects, researchers also used wikipedia in other open-domain tasks such as document topic classification @xcite , collaborative ontology creation @xcite , semantic conceptual modeling and semantic relatedness interpretation @xcite , explaining facts in ai @xcite , learning named entities @xcite , large-scale taxonomy generation @xcite . researchers also used wikipedia in domain-specific tasks like exploiting wikipedia knowledge for classification tasks @xcite and extracting domain-terms and terminologies from wikipedia @xcite , @xcite , @xcite . in this research, we provide domain-specific conceptual base construction from a small set of articles extracted on-the-fly from wikipedia navigational templates instead of full wiki dumps or other domain-specific corpora/texts. we also exploit unstructured text in addition to the structured information like wikipedia info-boxes and article content structure. ## conclusion we use wikipedia navigational templates to build domain-specific conceptual bases in this study. to compute the relevance of the concepts, our system generates a graph representation of the article's knowledge and uses fuzzy logic on top of its network metrics. with a bit of human intervention, the system outputs a refined conceptual base that can be used further for various downstream purposes.