Abstract
As knowledge increases tremendously each and every day, there is a need for means to manage and organize it, so as to utilize it when needed. For example, for finding solutions to technical/engineering problems. An alternative for achieving this goal is through knowledge mapping that aims at indexing the knowledge. Nevertheless, searching for knowledge in such maps is still a challenge. In this paper, we propose an algorithm for knowledge searching over maps created by ME-MAP, a mapping approach we developed. The algorithm is a greedy one that aims at maximizing the similarity between a query and existing knowledge encapsulated in ME-maps. We evaluate the efficiency of the algorithm in comparison to an expert judgment. The evaluation indicates that the algorithm achieved high performance within a bounded time. Though additional examination is required, the sought algorithm can be easily adapted to other modeling languages for searching models.
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References
Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., Vrgoč, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. (CSUR) 50(5), 1–40 (2017)
Bragilovski, M., Makias, Y., Shamshila, M., Stern, R., Sturm, A.: Searching for class models. In: Augusto, A., Gill, A., Nurcan, S., Reinhartz-Berger, I., Schmidt, R., Zdravkovic, J. (eds.) BPMDS/EMMSAD -2021. LNBIP, vol. 421, pp. 277–292. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79186-5_18
Ferrucci, D., et al.: Building Watson: an overview of the DeepQA project. AI Mag. 31(3), 59–79 (2010)
Garud, R.: On the distinction between know-how, know-why, and know-what. Adv. Strateg. Manag. 14, 81–101 (1997)
Greenstein, L.: Assessing 21st Century Skills: A Guide to Evaluating Mastery and Authentic Learning. SAGE Publications, Thousand Oaks (2012)
Han, S., Zou, L., Yu, J.X., Zhao, D.: Keyword search on RDF graphs-a query graph assembly approach. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 227–236 (2017)
Hu, S., Zou, L., Zhang, X.: A state-transition framework to answer complex questions over knowledge base. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2098–2108 (2018)
Khwaja, S., Alshayeb, M.: Survey on software design-pattern specification languages. ACM Comput. Surv. 49(1), 1–35 (2016)
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings 18th International Conference on Data Engineering, pp. 117–128. IEEE (2002)
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Novak, J., Cañas, A.: The theory underlying concept maps and how to construct them (2006)
Pareti, E.H., Klein, P.: The human know-how dataset (2014). https://doi.org/10.7488/ds/1394
Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. (TODS) 34(3), 1–45 (2009)
Reinhartz-Berger, I.: Towards automatization of domain modeling. Data Knowl. Eng. 69(5), 491–515 (2010)
Sarewitz, D., Nelson, R.R.: Progress in know-how: its origins and limits. Innov. Technol. Gov. Global. 3(1), 101–117 (2008)
Stern, R., Kalech, M., Felner, A.: Finding patterns in an unknown graph. AI Commun. 25(3), 229–256 (2012)
Sturm, A., Gross, D., Wang, J., Yu, E.: Means-ends based know-how mapping. J. Knowl. Manag. 21, 454–473 (2017)
Wang, Y., Khan, A., Wu, T., Jin, J., Yan, H.: Semantic guided and response times bounded top-k similarity search over knowledge graphs. In: 36th International Conference on Data Engineering (ICDE), pp. 445–456. IEEE (2020)
Yadollahi, A., Shahraki, A.G., Zaiane, O.R.: Current state of text sentiment analysis from opinion to emotion mining. ACM Comput. Surv. 50(2), 1–33 (2017)
Yu, E., Giorgini, P., Maiden, N., Mylopoulos, J.: Social Modeling for Requirements Engineering. The MIT Press, Cambridge (2011)
Zheng, W., Zou, L., Peng, W., Yan, X., Song, S., Zhao, D.: Semantic SPARQL similarity search over RDF knowledge graphs. Proc. VLDB Endow. 9(11), 840–851 (2016)
Zhu, G., Iglesias, C.A.: Sematch: semantic similarity framework for knowledge graphs. Knowl.-Based Syst. 130, 30–32 (2017)
Zou, L., Huang, R., Wang, H., Yu, J.X., He, W., Zhao, D.: Natural language question answering over RDF: a graph data driven approach. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp 313–324 (2014)
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This research was partially supported by the Data Science Research Center at Ben-Gurion University of the Negev (DSRC@BGU).
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Bragilovski, M., Makias, Y., Shamshila, M., Stern, R., Sturm, A. (2021). Model-Based Knowledge Searching. In: Ghose, A., Horkoff, J., Silva Souza, V.E., Parsons, J., Evermann, J. (eds) Conceptual Modeling. ER 2021. Lecture Notes in Computer Science(), vol 13011. Springer, Cham. https://doi.org/10.1007/978-3-030-89022-3_20
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