Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings (2023)

Abstract: In this paper, we propose Vocab-Expander at https://vocab-expander.com, an online tool that enables end-users (e.g., technology scouts) to create and expand a vocabulary of their domain of interest. It utilizes an ensemble of state-of-the-art word embedding techniques based on web text and ConceptNet, a common-sense knowledge base, to suggest related terms for already given terms. The system has an easy-to-use interface that allows users to quickly confirm or reject term suggestions. Vocab-Expander offers a variety of potential use cases, such as improving concept-based information retrieval in technology and innovation management, enhancing communication and collaboration within organizations or interdisciplinary projects, and creating vocabularies for specific courses in education.

## introduction motivation. in many scenarios, it is necessary to create an ontology or other formal model of a domain of interest from scratch. for instance, in the field of technology and innovation management, technology scouts and other end-users without technical skills often use a list of terms for continuously retrieving and scanning texts from different media sources (e.g., news articles, social media, publications, patents) in order to become aware of novel relevant technologies and to create and populate profiles of technologies and actors within a particular domain, such as smart cities. however, coming up with such a vocabulary is typically highly time-consuming and costly due to the domain-specificity (i.e., non-experts have no starting point what to add), the complexity of correctly defining the scope of the domain (e.g., smart cities can range from smart home to energy efficiency to security), the ambiguity of natural language (i.e., the meaning of terms may vary depending on the context in which they are used), and the emergence of new terms over time. current situation. so far, domain experts (e.g., technology scouts in technology and innovation management) still rely heavily on domain expert knowledge (de weck, 2022). several tools for modeling a domain of interest exist, including protegé @xcite and d-terminer @xcite . however, these tools are often considered as "too heavy" for creating only a domainspecific vocabulary instead of an ontology with a specific data model and standardizations (e.g., w3c rdf, owl). furthermore, these tools are typically designed to support the modeling process (e.g., based on an existing text corpus), but do not suggest directly related terms for given terms. contributions. in this paper, we propose the system vocab-expander, available online at https://vocab-expander.com , that enables endusers without technical skills to create and expand a vocabulary of their domain of interest. the system utilizes an ensemble of state-of-the-art word embedding techniques to suggest related terms for already given terms. in addition to word embedding models based on web text, the system also incorporates embeddings based on conceptnet, a common-sense knowledge base. the system is equipped with an easy-to-use interface that allows end-users to quickly confirm or reject term suggestions. the ranking of the suggested terms is based on the number of links they possess to other terms within the vocabulary. the created vocabulary can be listed as a table and visualized as a graph (see figures 1 and 2 ). we also provide an import and export functionality for the vocabularies. use cases. our tool offers a variety of potential use cases. for tasks such as technology and innovation management, it can be used to improve concept-based information retrieval by utilizing the created domain-specific vocabulary as search terms. ## system design the system utilizes an ensemble e of state-of-theart pre-trained word embedding models available in gensim @xcite to suggest related terms for already given terms. specifically, the user can choose one or several of the following models: (1) word2vec-google-news-300 @xcite , (2) glove-wiki-gigaword-300 @xcite , (3) fasttextwiki-news-subwords-300 @xcite , (4) conceptnet-numberbatch-17-06-300 @xcite . words @xmath0, accepted words w a , rejected words w r , and suggested words w s . initially, a user adds one or more words to w a . for each word w @xmath1,j ,w i across e. w sim / ∈ w are added to w s . next, we calculate a score s ws for each suggested word w @xmath2: ## conclusion in this paper, we proposed vocab-expander, an online tool that enables end-users to create and expand a vocabulary of their domain of interest. it uses state-of-the-art word embedding techniques based on web text as well as conceptnet, a common-sense knowledge base, to suggest related terms for already given terms. the system can be used for a variety of purposes such as improving information retrieval, communication and collaboration, creating vocabularies for education, and fine-tuning language models in natural language processing. for the future, we will allow for the integration of domain-specific text corpora (e.g., provided by the domain experts) and provide a functionality to see to which degree the suggested terms occur in the text corpora. furthermore, we plan to evaluate the performance of vocab-expander by means of user studies in different domains and applications.