MApp-KG

Mobile app repositories serve as large-scale crowdsourced information systems used for various document-based software engineering tasks, leveraging product descriptions, user reviews, and other natural language documents. Particularly, feature extraction (i.e., identifying functionalities or capabilities of a mobile app mentioned in these documents) is key for product recommendation, topic modelling, and feedback analysis. However, researchers often face domain-specific challenges in mining these repositories, including the integration of heterogeneous data sources, large-scale data collection, normalization and ground-truth generation for feature-oriented tasks. In this paper, we introduce MApp-KG, a combination of software resources and data artefacts in the field of mobile app repositories aimed at supporting feature-oriented knowledge generation tasks. Our contribution provides a framework for automatically constructing a knowledge graph that models a domain-specific catalog of natural language documents related to mobile applications. We distribute MApp-KG through a public triplestore, enabling its immediate use for future research and replication of our findings.

MApp-KG is available at http://gessi-chatbots.essi.upc.edu:7200/MApp-KG (read-only)

Please cite this research as: Motger, Q.; Franch, X.; Marco, J. MApp-KG: Mobile app knowledge graph for document-based feature knowledge generation. A: International Conference on Advanced Information Systems Engineering. "Intelligent Information Systems, CAiSE Forum 2024: Limassol, Cyprus, June 3-7, 2024: proceedings". Springer, 2024, p. 129-137. ISBN 978-3-031-61000-4. DOI 10.1007/978-3-031-61000-4_15 .