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Discovering relations between indirectly connected biomedical concepts
#MMPMID26150906
Weissenborn D
; Schroeder M
; Tsatsaronis G
J Biomed Semantics
2015[]; 6
(?): 28
PMID26150906
show ga
BACKGROUND: The complexity and scale of the knowledge in the biomedical domain
has motivated research work towards mining heterogeneous data from both
structured and unstructured knowledge bases. Towards this direction, it is
necessary to combine facts in order to formulate hypotheses or draw conclusions
about the domain concepts. This work addresses this problem by using indirect
knowledge connecting two concepts in a knowledge graph to discover hidden
relations between them. The graph represents concepts as vertices and relations
as edges, stemming from structured (ontologies) and unstructured (textual) data.
In this graph, path patterns, i.e. sequences of relations, are mined using
distant supervision that potentially characterize a biomedical relation. RESULTS:
It is possible to identify characteristic path patterns of biomedical relations
from this representation using machine learning. For experimental evaluation two
frequent biomedical relations, namely "has target", and "may treat", are chosen.
Results suggest that relation discovery using indirect knowledge is possible,
with an AUC that can reach up to 0.8, a result which is a great improvement
compared to the random classification, and which shows that good predictions can
be prioritized by following the suggested approach. CONCLUSIONS: Analysis of the
results indicates that the models can successfully learn expressive path patterns
for the examined relations. Furthermore, this work demonstrates that the
constructed graph allows for the easy integration of heterogeneous information
and discovery of indirect connections between biomedical concepts.
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