1. Ontology-based Word Sense Disambiguation
For Scientific Literature
Roman Prokofyev, Gianluca Demartini, Philippe Cudre-Mauroux, Alexey
Boyarsky and Oleg Ruchayskiy
eXascale Infolab
University of Fribourg, Switzerland
March 25, ECIR 2013, Moscow

2. Problem definition
Supersymmetric Standard Model
State Space Model
Sequential Standard Model
• Machine translation: correct lexical choice.
• Information retrieval: ambiguity in queries, result diversification, etc.
• Knowledge extraction: proper text analysis and classification (our case).
Our contribution: leveraging the structure of communitybased ontology to improve correct sense identification.
Datasets
• ScienceWISE abstract dataset + SW ontology
http://sciencewise.info
• MSH abstract dataset + ontology from bioontology.org
Available at http://exascale.info/papers/ecir2013disambig

3. Base models
• Concept Context Vectors
Star formation efficiency (SFE)
(Instability, 4), (Supernova, 2), (Milky Way, 3),…
• Document Concept Context Vectors
1
(Milky way, 1), (Electron neutrino, 1), (Electron antineutrino, 1),…
2
(Local analysis, 1), (White dwarf, 3), (Poynting-Robertson effect, 1), …
Min distance
Minimum over the ontological
paths to other concepts in the
document

4. Ontology shortest path
Average distance to other
concepts in the document
Nearest neighbors
Co-occurring 1-hop neighbors
from the ontology

5. Graph models evaluation
Approach
Precision (ScienceWISE) Precision (MSH)
Min Distance
0.8882
0.6728
Ontology Shortest Path
0.8646
0.5677
Nearest neighbors
0.7393
0.7237
Combined approaches
Approach
Precision (ScienceWISE) Precision (MSH)
Naïve Bayes
0.8513
0.6731
Binary CCV
0.9334
0.9077
+ Ontology Shortest Path
0.9444
0.8077
+ Nearest neighbors
0.9453
0.9060