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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Analyzing longitudinal Data in Knowledge Graphs utilizing shrinking pseudo-triangles

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DOI: http://dx.doi.org/10.15439/2022F45

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 323327 ()

Full text

Abstract. This paper aims to analyze longitudinal data in knowledge graphs. Knowledge graphs play a central role for linking different data. While multiple layers for data from different sources are considered, there is only very limited research on longitudinal data in knowledge graphs. However, knowledge graphs are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers and time points within graphs impact methods and algorithms developed for single-purpose networks. This manuscript investigates the impact of a modeling of longitudinal data in multiple layers on retrieval algorithms. In particular, (a) we propose a first draft of a generic model for longitudinal data in multi-layer knowledge graphs, (b) we develop an experimental environment to evaluate a generic retrieval algorithm on random graphs inspired by computational social sciences. We present a knowledge graph generated on German job advertisements comprising data from different sources, both structured and unstructured, on data between 2011 and 2021. The data is linked using text mining and natural language processing methods. We further (c) present two different shrinking techniques for structured and unstructured layers in knowledge based on graph structures like triangles and pseudo-triangles. The presented approach (d) shows that on the one hand, the initial research questions, on the other hand the graph structures and topology have a great impact on the structures and efficiency for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations, we will (e) make suggestions for additional research on particular graph structures that have a great impact on the analysis of knowledge graph structures.


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