Giant Knowledge Graphs

Data and Knowledge Management, sometimes also called Information Management, is a core topic of Data Engineering and Data Mining. It is also a interdisciplinary field touching economics (how efficient and expensive is the solution?), psychology (do people use this solution in a way that was intended?) and of course informatics.

 

A Knowledge Graph (sometimes also called a semantic network) is a systematic way to connect information and data to knowledge. It is thus a crucial concept on the way to generate knowledge and wisdom, to search within data, information and knowledge. As described above, the context is the most important topic to generate knowledge or even wisdom. Thus, connecting Knowledge Graphs with context is a key feature. In this project we want to establish a novel systematic approach to knowledge discovery using contexts in Knowledge Graphs. For this, we enrich the existing graph structures and build a context hypergraph.

 

We create a proof-of-concept giant knowledge graph based on biomedical literature (see SCAIView). This is the basis for answering semantic questions, graph queries and extensions based on NLP and Text Mining. We will discuss perspectives for personalised medicine and knowledge discovery as well as FAIRification of clinical and biomedical research data.

 

[1] Dörpinghaus, J. et al. “Context graph for biomedical research data: A FAIR and open approach towards reproducible research in Medicine”, 3rd Annual MAQC Society Conference 2019.

[2] Dörpinghaus, J. et al. "SCAIView–A Semantic Search Engine for Biomedical Research Utilizing a Microservice Architecture." Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems – SEMANTiCS2018, 2018.

This figure is an illustration of the initial document and context graph. A PubMed node is the source of document nodes (green). There are several context annotations like article type (red), keywords (gray), authors (orange) and journal /yellow). Authors have additional context (affiliations, gray).
© Fraunhofer SCAI

This figure is an illustration of the initial document and context graph. A PubMed node is the source of document nodes (green). There are several context annotations like article type (red), keywords (gray), authors (orange) and journal /yellow). Authors have additional context (affiliations, gray).