Applied Semantics

Making scientific content computable

The Applied Semantics research group focuses on the following major topics:

  • Making data FAIR (Findable, Accessible, Interoperable and Reusable)
  • Shared Semantics
  • Knowledge Discovery
  • Cause-effect Mechanistic Knowledge Graphs

The big data paradigm is highly relevant to the biomedical field with its ever increasing growth of scientific publications  – and omics data. However, it is often a challenge to capture and organize relevant scientific knowledge from unstructured text in literature. When it comes to data, problems often arise with insufficient reusability or low quality of the data.

The Applied Semantics group makes both data and knowledge computable and analyzable through data curation, standardization and data management. Our group has expertise in shared semantics, which is the basis for interoperability of data and knowledge. We can achieve semantic interoperability by relating biological entities to standard terminologies or ontologies. Ontologies and terminologies also serve as the basis for semantic based knowledge discovery systems.

Another focus of the group is on knowledge graphs which consist of cause-and-effect mechanisms extracted from scientific publications. Knowledge graphs are multimodal, disease-specific and comprise biological entities ranging from the genetic level to the phenotypic level together with various relationships among them. Our current knowledge graphs cover neurodegenerative, psychiatric and metabolic disorders.

Models that integrate data and knowledge form the basis of our approaches to precision medicine. They allow us to analyze patient-level data taking into account the state of knowledge about disease mechanisms. Some of the main applications resulting from the combination of knowledge graphs and data are the stratification of patients and the identification of comorbidities based on mechanisms, personalized medicine, and drug repurposing.

Tools & Resources

The group Applied Semantics offers a variety of tools and software that help to query, visualize the knowledge graphs and analyze it along with data.


Depending on the scope of the research area we are working, we develop ontologies and terminologies. We develop common ontologies as well as disease specific ones.

Disease Models

One of the main focus is to build knowledge graphs from existing knowledge in literature as well as augmenting it with the data from public databases such as KEGG, REACTOME, WikiPathways etc.

Selected Publications

  • Kodamullil AT, Younesi E, Naz M, Bagewadi S, Hofmann-Apitius M. "Computable cause-and-effect models of healthy and Alzheimer's disease states and their mechanistic differential analysis." Alzheimer's & dementia: the journal of the Alzheimer's Association 11.11 (2015): 1329-1339.
  • Kodamullil AT, Zekri F, Sood M, Hengerer B, Canard L, McHale D, Hofmann-Apitius M. Trial watch: Tracing investment in drug development for Alzheimer disease. Nat Rev Drug Discov. 2017 Dec;16(12):819.
  • Karki R, Kodamullil AT, Hoyt CT, Hofmann-Apitius M. Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm. BMC Bioinformatics. 2019 Oct 11;20(1):494.
  • Domingo-Fernández D, Kodamullil AT, Iyappan A, Naz M, Emon MA, Raschka T, Karki R, Springstubbe S, Ebeling C, Hofmann-Apitius M. Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): a web server for mechanism enrichment. Bioinformatics. 2017 Nov 15;33(22):3679-3681.
  • Naz M, Kodamullil AT, Hofmann-Apitius M. Reasoning over genetic variance information in cause-and-effect models of neurodegenerative diseases. Brief Bioinform. 2016 May;17(3):505-16.