1.4 Research Questions

The central question governing this thesis is: “How can we leverage concept languages to improve information access?” In particular, we will be looking at methods and algorithms to improve the query or its representation using concept languages in the context of generative language models. Instead of creating, defining, or using such languages directly, however, we will leverage the natural language use associated with the concepts to improve information access. Our central research question leads to a set of more specific research questions that will be answered in the following chapters.

After we have provided a theoretical and methodological foundation of IR, we look at the case of using relevance information to improve a user’s query. A typical method for improving queries is updating the estimate of the language model of the query, a process known as query modeling. Relevance feedback is a commonly used mechanism to improve queries and, hence, end-to-end retrieval performance. It uses relevance assessments (either explicit, implicit, or assumed) on documents retrieved in response to a query to update the query. Core relevance feedback models for language modeling include the relevance modeling and the model-based feedback approach. They both operate under different assumptions with respect to how to treat the set of feedback documents as well as each individual feedback document. Therefore, we propose two models that take the middle ground between these two approaches. Furthermore, an extensive comparison between these models is lacking, both in experimental terms, i.e., under the same experimental conditions, and in theoretical terms. We ask:

RQ 1.
What are effective ways of using relevance feedback information for query modeling to improve retrieval performance?
Can we develop a relevance feedback model that uses evidence from both the individual feedback documents and the set of feedback documents as a whole? How does this model relate to other query modeling approaches using relevance feedback? Is there any difference when using explicit relevance feedback instead of pseudo relevance feedback?
How do the models perform on different test collections? How robust are our two novel models on the various parameters query modeling offers and what behavior can we observe for the related models?

Inspired by relevance feedback methods, we then develop a two-step method that uses concepts (in the form of document-level annotations) to estimate a conceptual language model. In the first step, the query is translated into a conceptual representation. In a process we call conceptual query modeling, feedback documents from an initial retrieval run are used to obtain a conceptual query model. This model represents the user’s information need at the level of concepts rather than that of the terms entered by the user. In the second step, we translate the conceptual query model back into a contribution to the textual query model. We investigate the effectiveness of our conceptual language models by placing them in the broader context of common retrieval models, including those using relevance feedback information. We organize the following research question around a number of subquestions.

RQ 2.
What are effective ways of using conceptual information for query modeling to improve retrieval performance?
What is the relative retrieval effectiveness of our method with respect to the standard language modeling and conventional pseudo relevance feedback approach?
How portable is our conceptual language model? That is, what are the results of the model across multiple concept languages and test collections?
Can we say anything about which evaluation measures are helped most using our model? Is it mainly a recall or precision-enhancing device?

We then move beyond annotated documents and take a closer look at directly identifying concepts with respect to a user’s query. The research questions we address are the following.

RQ 3.
Can we successfully address the task of mapping search engine queries to concepts using a combination of information retrieval and machine learning techniques?
What is the best way of handling a query? That is, what is the performance when we map individual n-grams in a query instead of the query as a whole?
As input to the machine learning algorithms we extract and compute a wide variety of features, pertaining to the query terms, concepts, and search history. Which type of feature helps most? Which individual feature is most informative?
Machine learning generally comes with a number of parameter settings. We ask: what are the effects of varying these parameters?

After we have looked at mapping queries to concepts, we apply relevance feedback techniques to the natural language texts associated with each concept and obtain query models based on this information The guiding intuition is that, similar to our conceptual query models, concepts are best described by the language use associated with them. In other words, once our algorithm has determined which concepts are meant by a query, we employ the language use associated with those concepts to update the query model. We ask:

RQ 4.
What are the effects on retrieval performance of applying pseudo relevance feedback methods to texts associated with concepts that are automatically mapped from ad hoc queries?
What are the differences with respect to pseudo relevance estimations on the collection? And when the query models are estimated using pseudo relevance estimations on the concepts’ texts?
Is the approach mainly a recall- or precision-enhancing device? Or does it help other aspects, such as promoting diversity?