Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Miñarro-Giménez, JA; Kreuzthaler, M; Schulz, S.
Knowledge Extraction from MEDLINE by Combining Clustering with Natural Language Processing.
AMIA Annu Symp Proc. 2015; 2015(12): 915-924.
[OPEN ACCESS]
PubMed
- Leading authors Med Uni Graz
- Minarro-Gimenez Jose Antonio
- Co-authors Med Uni Graz
- Kreuzthaler Markus Eduard
- Schulz Stefan
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Abstract:
- The identification of relevant predicates between co-occurring concepts in scientific literature databases like MEDLINE is crucial for using these sources for knowledge extraction, in order to obtain meaningful biomedical predications as subject-predicate-object triples. We consider the manually assigned MeSH indexing terms (main headings and subheadings) in MEDLINE records as a rich resource for extracting a broad range of domain knowledge. In this paper, we explore the combination of a clustering method for co-occurring concepts based on their related MeSH subheadings in MEDLINE with the use of SemRep, a natural language processing engine, which extracts predications from free text documents. As a result, we generated sets of clusters of co-occurring concepts and identified the most significant predicates for each cluster. The association of such predicates with the co-occurrences of the resulting clusters produces the list of predications, which were checked for relevance.
- Find related publications in this database (using NLM MeSH Indexing)
- Cluster Analysis -
- Databases, Factual -
- Humans -
- Information Storage and Retrieval -
- MEDLINE -
- Medical Subject Headings -
- Natural Language Processing -
- Semantics -