Research Article
Interactive Domain-Specific Knowledge Graphs from Text: A Covid-19 Implementation
@INPROCEEDINGS{10.1007/978-3-030-77417-2_18, author={Vin\^{\i}cius Melqu\^{\i}ades de Sousa and Vin\^{\i}cius Medina Kern}, title={Interactive Domain-Specific Knowledge Graphs from Text: A Covid-19 Implementation}, proceedings={Data and Information in Online Environments. Second EAI International Conference, DIONE 2021, Virtual Event, March 10--12, 2021, Proceedings}, proceedings_a={DIONE}, year={2021}, month={6}, keywords={Knowledge graphs COVID-19 Information retrieval software Natural language processing Personalized analytics}, doi={10.1007/978-3-030-77417-2_18} }- Vinícius Melquíades de Sousa
Vinícius Medina Kern
Year: 2021
Interactive Domain-Specific Knowledge Graphs from Text: A Covid-19 Implementation
DIONE
Springer
DOI: 10.1007/978-3-030-77417-2_18
Abstract
Information creation runs at a higher rate than information assimilation, creating an information gap for domain specialists that usual information frameworks such as search engines are unable to bridge. Knowledge graphs have been used to summarize large amounts of textual data, therefore facilitating information retrieval, but they require programming and machine learning skills not usually available to domains specialists. To bridge this gap, this work proposes a framework, KG4All (Knowledge Graphs for All), to allow for domain specialists to build and interact with a knowledge graph created from their own chosen corpus. In order to build the knowledge graph, a transition-based system model is used to extract and link medical entities, with tokens represented as embeddings from the prefix, suffix, shape and lemmatized features of individual words. We used abstracts from the COVID-19 Open Research Dataset Challenge (CORD-19) as corpus to test the framework. The results include an online prototype and correspondent source code. Preliminary results show that it is possible to automate the extraction of entity relations from medical text and to build an interactive user knowledge graph without programming background.
