Knowledge Graph Development for App Store Data Modeling

Abstract:

Usage of mobile applications has become a part of our lives today, since every day we use our smartphones for communication, entertainment, business and education. High demand on apps has led to significant growth of supply, yet large offer has caused complications in users’ search of the one suitable application. The authors have made an attempt to solve the problem of facilitating the search in app stores. With the help of a website crawling software a sample of data was retrieved from one of the well-known mobile app stores and divided into 11 groups by types. These groups of data were used to construct a Knowledge Schema – a graphic model of interconnections of data that characterize any mobile app in the selected store. Schema creation is the first step in the process of developing a Knowledge Graph that will perform applications clustering to facilitate users’ search in app stores.

Keywords:

Knowledge graph, Knowledge schema, RDF, App store mining

Full text:

PDF

How to cite:

Rizun, M., & Strzelecki, A. (2019). Knowledge Graph Development for App Store Data Modeling. In A. Siarheyeva, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Information Systems Beyond 2020 (ISD2019 Proceedings). Toulon, France: ISEN Yncréa Méditerranée.

References:

1. Bourgeois, F., Arlaud, P., Studer, P., Perronne1, J.-M.: The Mobile Measure Metrology Knowledge Schema: an Artificial Intelligence Reasoning over Metrology. In: Christ, A. and Quint, F. (eds.) Artificial Intelligence From Research to Application. pp. 3-6. (2019)
2. Broekstra, J., Klein, M., Decker, S., Fensel, D., van Harmelen, F., Horrocks, I.: Enabling knowledge representation on the Web by extending RDF Schema. Comput. Networks. 39 (5), 609-634 (2002)
3. Cade, A., Corn, H. De, Noel, V., Bevilacqua, L.: AI-powered Knowledge Assimilation and Dissemination. (2017)
4. Chang, S.: AppGrouper : Knowledge-graph-based Interactive Clustering Tool for Mobile App Search Results. (2016)
5. Chen, N., Lin, J., Hoi, S.C.H., Xiao, X., Zhang, B., Chen, N., Xiao, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th International Conference on Software Engineering - ICSE 2014. pp. 767-778. ACM Press, New York, New York, USA (2014)
6. Felini, D.: Beyond Today’s Video Game Rating Systems. Games Cult. 10 (1), 106-122 (2015)
7. Finkelstein, A., Harman, M., Jia, Y., Martin, W., Sarro, F., Zhang, Y., Finkelstein, A., Harman, M., Jia, Y., Martin, W., Sarro, F., Zhang, Y.: App Store Analysis : Mining App Stores for Relationships between Customer , Business and Technical Characteristics. UCL Res. Note. 14/10 1-24 (2014)
8. Genc-Nayebi, N., Abran, A.: A systematic literature review: Opinion mining studies from mobile app store user reviews. J. Syst. Softw. 125 207-219 (2017)
9. Harb, G.K.: TED Talks: An Approach for Activating the World Knowledge Schema of EFL Writers. Int. J. Lang. Linguist. 5 (4), 76-85 (2018)
10. Harman, M., Yue Jia, Yuanyuan Zhang: App store mining and analysis: MSR for app stores. In: 2012 9th IEEE Working Conference on Mining Software Repositories (MSR). pp. 108-111. IEEE (2012)
11. Hislop, D., Bosua, R., Remko, H.: Knowledge Management in Organizations. Oxford University Press, Oxford (2018)
12. Hu, W.: Math that moves: Schools embrace the iPad, (2011)
13. Iacob, C., Harrison, R.: Retrieving and analyzing mobile apps feature requests from online reviews. In: 2013 10th Working Conference on Mining Software Repositories (MSR). pp. 41-44. IEEE (2013)
14. Imran, M.K., Rehman, C.A., Aslam, U., Bilal, A.R.: What ’ s organization knowledge management strategy for successful change implementation ? 29 (7), 1097-1117 (2016)
15. Jetschni, J., Meister, V.G.: Schema engineering for enterprise knowledge graphs: A reflecting survey and case study. In: 2017 Eighth International Conference on Intelligent Computing and Information Systems (ICICIS). pp. 271-277. IEEE (2017)
16. Ji, G., Liu, K., He, S., Zhao, J.: Knowledge Graph Completion with Adaptive Sparse Transfer Matrix. Aaai. (2016)
17. Jia, Y., Wang, Y., Lin, H., Jin, X., Cheng, X.: Locally Adaptive Translation for Knowledge Graph Embedding. Thirtieth AAAI Conf. Artif. Intell. (2016)
18. Lai, Y.-H., Huang, F.-F., Chiou, P.-Y.: Analysis of User Feedback in The Mobile App Store Using Text Mining: A Case Study of Google Fit. In: 2017 IEEE 8th International Conference on Awareness Science and Technology (ICAST). pp. 50-54. (2017)
19. Lee, G., Raghu, T.S.: Determinants of Mobile Apps’ Success: Evidence from the App Store Market. J. Manag. Inf. Syst. 31 (2), 133-170 (2014)
20. Malyshev, S., Krötzsch, M., González, L., Gonsior, J., Bielefeldt, A.: Getting the Most Out of Wikidata: Semantic Technology Usage in Wikipedia’s Knowledge Graph. October (2018)
21. Martin, W., Harman, M., Jia, Y., Sarro, F., Zhang, Y.: The App Sampling Problem for App Store Mining. In: 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories. pp. 123-133. IEEE (2015)
22. Martin, W., Sarro, F., Jia, Y., Zhang, Y., Harman, M.: A Survey of App Store Analysis for Software Engineering. IEEE Trans. Softw. Eng. 43 (9), 817-847 (2017)
23. McIlroy, S., Ali, N., Hassan, A.E.: Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store. Empir. Softw. Eng. 21 (3), 1346-1370 (2016)
24. Mehri, R., Valtchev, P.: Mining Schema Knowledge from Linked Data on the Web. In: Lecture Notes in Computer Science. pp. 261-273. Springer, Berlin, Heidelberg (2017)
25. Meng, G., Xue, Y., Siow, J.K., Su, T., Narayanan, A., Liu, Y.: AndroVault: Constructing Knowledge Graph from Millions of Android Apps for Automated Analysis. (2017)
26. Moon, C.N.: Predictive Modeling of Complex Graphs as Context and Semantics Preserving Vector Spaces. (North Carolina State University, ProQuest Dissertations Publishing,), (2018)
27. Nayebi, M., Cho, H., Ruhe, G.: App store mining is not enough for app improvement. Empir. Softw. Eng. 23 (5), 2764-2794 (2018)
28. Olszak, C.M., Ziemba, E.: Knowledge management curriculum development: Linking with real business needs. In: Issues in Informing Science and Information Technology. pp. 235-248. (2010)
29. Pagano, D., Maalej, W.: User feedback in the appstore: An empirical study. In: 2013 21st IEEE International Requirements Engineering Conference (RE). pp. 125-134. IEEE (2013)
30. Paulheim, H.: Knowledge graph refinement: A survey of approaches and evaluation methods. Semant. Web. 8 (3), 489-508 (2016)
31. Piao, G.: Semantics-aware User Modeling and Recommender Systems in Online Social Networks. 208 (2018)
32. Pomp, A., Paulus, A., Jeschke, S., Meisen, T.: ESKAPE: Information Platform for Enabling Semantic Data Processing. In: Proceedings of the 19th International Conference on Enterprise Information Systems. pp. 644-655. (2017)
33. Popping, R.: Knowledge Graphs and Network Text Analysis. Soc. Sci. Inf. 42 (1), 91-106 (2003)
34. Rizun, M.: Knowledge Graph: Theory and Application. Acta Univ. Lodz. Folia Oeconomica. 3 (342), 7-19 (2019)
35. Suh, Y., Lee, H., Park, Y.: Analysis and visualisation of structure of smartphone application services using text mining and the set-covering algorithm: a case of App Store. Int. J. Mob. Commun. 10 (1), 1 (2012)
36. Valente, A., Nassehi, A., Newman, S.T., Tolio, T.A.M.: A STEP Compliant Knowledge Based Schema for the Manufacture of Composites in the Aerospace Industry. In: Advances in Intelligent and Soft Computing. pp. 1509-1525. Springer, Berlin, Heidelberg (2010)
37. Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph and text jointly embedding. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pp. 1061-1591. (2014)
38. Zamula, D., Muromtsev, D., Zhukova, N.: reviewed paper Mobile Museum Guides Applications based on Knowledge Graphs. In: REAL CORP 2017. pp. 365-372. (2017)