DOCTORAL ABSTRACT

Semantic Search as Inference:
Applications in Health Informatics
Bevan Koopman1,2
1Australian e-Health Research Centre, CSIRO, Brisbane, Australia
2
Queensland University of Technology, Brisbane, Australia
bevan.koopman@csiro.auAbstract
In this thesis, we present models for semantic search: Information Retrieval (IR) models
that elicit the meaning behind the words found in documents and queries rather than simply
matching keywords. This is achieved by the integration of structured domain knowledge anddata-driven information retrieval methods.
The research is set within health informatics to tackle the unique challenges within thisdomain; specifically, how to bridge the ‘semantic gap’; that is, how to overcome the mismatch
between raw medical data and the way human beings interpret it. Bridging the semantic gap
involves addressing two issues: semantics; that is, aligning the meaning or concepts behindwords found in documents and queries; and leveraging inference, which utilises semantics toinfer relevant information.
Three semantic search models — all utilising concept-based rather than term-based representations — are developed; these include: the Bag-of-concepts model, which utilises concepts
from the SNOMED CT medical ontology as its underlying representation; the Graph-based
Concept Weighting model, which captures concept dependence and importance in a novel
weighting function; and the core contribution of the thesis, the Graph INference model (GIN):a unified theoretical model of semantic search as inference, achieved by the integration ofstructured domain knowledge (ontologies) and statistical, information retrieval methods. It
is the GIN that provides the necessary mechanism for inference to bridge the semantic gap.
All three models are empirically evaluated using clinical queries and a real-world collectionof clinical records taken from the TREC Medical Records Track (MedTrack).
Our evaluation shows that the use of concept-based representations in the Bag-of-concepts
model leads to improved retrieval effectiveness. When concepts are combined within theGraph-based Concept Weighting model, further improvements are possible. The evaluation ofGIN highlighted that its inference mechanism is suited to hard queries — those that perform
poorly on a term-based system. In-depth analysis also revealed that the GIN returnedmany new documents not retrieved by term-based systems and therefore never evaluatedfor relevance as part of the TREC MedTrack. This highlights that using current IR testcollections, where semantic search systems did not contribute to the pool, may underestimate
the effectiveness of semantic search systems.
This work represents a significant step forward in the integration of structured domain
knowledge and data-driven information retrieval methods. Furthermore, the thesis provides

ACM SIGIR Forum116

Vol. 48 No. 2 December 2014

an understanding of inference — when and how it should be applied for effective semantic
search. It shows that queries with certain characteristics benefit from inference, while others
do not. The detailed investigation into the evaluation of semantic search systems shows how
current IR test collections may underestimate effectiveness of such systems and new techniques for evaluation are suggested. The Graph Inference model, although developed within
the medical domain, is generally defined and has implications in other areas, including web
search, where an emerging research trend is to utilise structured knowledge resources formore effective semantic search.
Supervisors: Peter Bruza, Laurianne Sitbon, Michael Lawley
Available: http://koopman.id.au

ACM SIGIR Forum117

Vol. 48 No. 2 December 2014