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Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02 Podcast
Professional Search in Pharmaceutical Research

Professional Search in Pharmaceutical Research

Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02 vor 16 Jahren

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In the mid 90s, visiting libraries – as means of retrieving the
latest literature – was still a common necessity among
professionals. Nowadays, professionals simply access information by
‘googling’. Indeed, the name of the Web search engine market leader
“Google” became a synonym for searching and retrieving information.
Despite the increased popularity of search as a method for
retrieving relevant information, at the workplace search engines
still do not deliver satisfying results to professionals. Search
engines for instance ignore that the relevance of answers (the
satisfaction of a searcher’s needs) depends not only on the query
(the information request) and the document corpus, but also on the
working context (the user’s personal needs, education, etc.). In
effect, an answer which might be appropriate to one user might not
be appropriate to the other user, even though the query and the
document corpus are the same for both. Personalization services
addressing the context become therefore more and more popular and
are an active field of research. This is only one of several
challenges encountered in ‘professional search’: How can the
working context of the searcher be incorporated in the ranking
process; how can unstructured free-text documents be enriched with
semantic information so that the information need can be expressed
precisely at query time; how and to which extent can a company’s
knowledge be exploited for search purposes; how should data from
distributed sources be accessed from into one-single-entry-point.
This thesis is devoted to ‘professional search’, i.e. search at the
workplace, especially in industrial research and development. We
contribute by compiling and developing several approaches for
facing the challenges mentioned above. The approaches are
implemented into the prototype YASA (Your Adaptive Search Agent)
which provides meta-search, adaptive ranking of search results,
guided navigation, and which uses domain knowledge to drive the
search processes. YASA is deployed in the pharmaceutical research
department of Roche in Penzberg – a major pharmaceutical company –
in which the applied methods were empirically evaluated. Being
confronted with mostly unstructured free-text documents and having
barely explicit metadata at hand, we faced a serious challenge.
Incorporating semantics (i.e. formal knowledge representation) into
the search process can only be as good as the underlying data.
Nonetheless, we are able to demonstrate that this issue can be
largely compensated by incorporating automatic metadata extraction
techniques. The metadata we were able to extract automatically was
not perfectly accurate, nor did the ontology we applied contain
considerably “rich semantics”. Nonetheless, our results show that
already the little semantics incorporated into the search process,
suffices to achieve a significant improvement in search and
retrieval. We thus contribute to the research field of
context-based search by incorporating the working context into the
search process – an area which so far has not yet been well
studied.