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4 Annals of Computer Science and Information Systems

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An Efficient Approach Towards the Generation and Analysis of Interoperable Clinical Data in a Knowledge Graph

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Dörpinghaus J. , Weil V. , Schaaf S. , Hübenthal T.

2021

tom Vol. 25

59--68

Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Contextual information is widely used for NLP and knowledge discovery in life sciences since it highly influences the exact meaning of natural language and also queries for data. The contributions of this paper are (1) an efficient approach towards interoperable data, (2) a runtime analysis of 14 real world use cases represented by graph queries and (3) a unique view on clinical data and its application combining methods of algorithmic optimisation, graph theory and data science.

Centrality measures in multi-layer knowledge graphs

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Dorpinghaus J. , Weil V. , Duing C. , Sommer M. W.

Annals of Computer Science and Information Systems

2022

tom Vol. 32

163--170

Knowledge graphs play a central role for linkingdifferent data which leads to multiple layers. Thus, they are widely used in big data integration, especially for connecting data from different domains. Few studies have investigated the questions how multiple layers within graphs impact methods and algorithms developed for single-purpose networks, for example social networks. This manuscript investigates the impact on the centrality measures of graphs with multiple layers compared to a those measures in single-purpose graphs. In particular, (a) we develop an experimental environment to (b) evaluate two different centrality measures --- degree and betweenness centrality --- on random graphs inspired by social network analysis: small-world and scale-free networks. The presented approach (c) shows that the graph structures and topology has a great impact on its robustness for additional data stored. Although the experimental analysis of random graphs allows us to make some basic observations we will (d) make suggestions for additional research on particular graph structures that have a great impact on the stability of networks.

A Minimum set-cover problem with several constraints

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Dörpinghaus J. , Düing C. , Weil V.

Annals of Computer Science and Information Systems

2019

tom Vol. 18

115--122

A lot of problems in natural language processing can be interpreted using structures from discrete mathematics. In this paper we will discuss the search query and topic finding problem using a generic context-based approach. This problem can be described as a a Minimum Set Cover Problem with several constraints. The goal is to find a minimum covering of documents with the given context for a fixed weight function. The aim of this problem reformulation is a deeper understanding of both the hierarchical problem using union and cut as well as the non-hierarchical problem using the union. We thus choose a modeling using bipartite graphs and suggest a novel reformulation using an integer linear program as well as novel graph-theoretic approaches.

Analyzing longitudinal Data in Knowledge Graphs utilizing shrinking pseudo-triangles

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Dörpinghaus J. , Weil V. , Binnewitt J.

Annals of Computer Science and Information Systems

2022

tom Vol. 30

323--327

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.