In this thesis we investigate the possibility to integrate domain-specific knowledge into biomedical information retrieval (IR). Recent decades have shown a fast growing interest in biomedical research, reflected by an exponential growth in scientific literature. Biomedical IR is concerned with the disclosure of these vast amounts of written knowledge. Biomedical IR is not only important for end-users, such as biologists, biochemists, and bioinformaticians searching directly for relevant literature but also plays an important role in more sophisticated knowledge discovery. An important problem for biomedical IR is dealing with the complex and inconsistent terminology encountered in biomedical publications. Multiple synonymous terms can be used for single biomedical concepts, such as genes and diseases. Conversely, single terms can be ambiguous, and may refer to multiple concepts. Dealing with the terminology problem requires domain knowledge stored in terminological resources: controlled indexing vocabularies and thesauri. The integration of this knowledge in modern word-based information retrieval is, however, far from trivial. This thesis investigates the problem of handling biomedical terminology based on three research themes.
The first research theme deals with robust word-based retrieval. Effective retrieval models commonly use a word-based representation for retrieval. As so many spelling variations are present in biomedical text, the way in which these word-based representations are obtained affect retrieval effectiveness. We investigated the effect of choices in document preprocessing heuristics on retrieval effectiveness. This investigation included stop-word removal, stemming, different approaches to breakpoint identification and normalisation, and character n-gramming. In particular breakpoint identification and normalisation (that is determining word parts in biomedical compounds) showed a strong effect on retrieval performance. A combination of effective preprocessing heuristics was identified and used to obtain word-based representations from text for the remainder of this thesis.
The second research theme deals with concept-based retrieval. We investigated two representation vocabularies for concept-based indexing, one based on the Medical Subject Headings thesaurus, the other based on the Unified Medical Language System metathesaurus extended with a number of gene and protein dictionaries.
We investigated the following five topics.
We compared different classification systems to obtain concept-based document and query representations automatically. We proposed two classification methods based on statistical language models, one based on K-Nearest Neighbours (KNN) and one based on Concept Language Models (CLM).
For a selection of classification systems we carried out a document classification experiment in which we investigated to what extent automatic classification could reproduce manual classification. The proposed KNN system performed well in comparison to the out-of-the-box systems. Manual analysis indicated the improved exhaustiveness of automatic classification over manual classification. Retrieval based on only concepts was demonstrated to be significantly less effective than word-based retrieval. This deteriorated performance could be explained by errors in the classification process, limitations of the concept vocabularies and limited exhaustiveness of the concept-based document representations. Retrieval based on a combination of word-based and automatically obtained concept-based query representations did significantly improve word-only retrieval. In an artificial setting, we compared the optimal retrieval performance which could be obtained with word-based and concept-based representations. Contrary to our intuition, on average a single word-based query performed better than a single concept-based representation, even when the best concept term precisely represented part of the information need.
We investigated to what extent the relatedness between pairs of concepts as indicated by human judgements could be automatically reproduced. Results on a small test set indicated that a method based on comparing concept language models performed particularly well in comparison to systems based on taxonomy structure, information content and (document) association.
In the third and last research theme of this thesis we propose a framework for concept-based retrieval. We approached the integration of domain knowledge in monolingual information retrieval as a cross-lingual information retrieval (CLIR) problem. Two languages were identified in this monolingual setting: a word-based representation language based on free text, and a concept-based representation language based on a terminological resource. Similar to what is common in traditional CLIR, queries and documents are translated into the same representation language and matched. The cross-lingual perspective gives us the opportunity to adopt a large set of established CLIR methods and techniques for this domain. In analogy to established CLIR practise, we investigated translation models based on a parallel corpus containing documents in multiple representations and translation models based on a thesaurus. Surprisingly, even the integration of very basic translation models showed improvements in retrieval effectiveness over word-only retrieval. A translation model based on pseudo-feedback translation was shown to perform particularly well. We proposed three extensions to a basic cross-lingual retrieval model which, similar to previous approaches in established CLIR, improved retrieval effectiveness by combining multiple translation models. Experimental results indicate that, even when using very basic translation models, monolingual biomedical IR can benefit from a cross-lingual approach to integrate domain knowledge.
Directions for future work are using these concepts for communication between user and retrieval system, extending upon the translation models and extending CLIR-enhanced concept-based retrieval outside the biomedical domain.
See also: SSR