There exist several unexplored avenues of research that are either opened or have not yet been fully addressed by the work presented in this thesis. Here we list these, in no particular order.
In Chapters 4, 5, and 7, we have measured the “quality” of query models by their resulting retrieval performance. While one could argue that improving end-to-end retrieval performance should be the ultimate goal of improving query model estimations, other ways by which to explicitly measure the quality of the query models themselves should be investigated. Examples of such measures would include perplexity measures or scores related to query clarity . What existing measures cannot account for, however, is an intrinsic measure of diversity with which different topical aspects of a query model could be quantified.
In Chapter 7 we have used a mapping from queries to concepts to automatically estimate query models and shown that this resulted in improved retrieval performance. Besides having the potential of automatically improving the retrieval performance on certain topics we believe that, similar to our observations in Chapter 5, the biggest improvements may be realized when a user selects the most relevant concepts. Future work should indicate if this is a valid assumption and whether such conceptual representations are appreciated by and useful to an end user.
In Chapters 5 and 6 we have obtained an explicit conceptual representation of the query. Several ideas may be employed to improve the performance of this step. For example, a form of query segmentation could be used to identify significant phrases in the queries . Such information could then be used to inform the conceptual mapping process. Additional features can also be added, for example structural ones such as those pertaining to the structure of the ontology. Although we have found that the method presented in Chapter 6 obtained convincing results and improvements over the two baselines, we believe that further improvements may be obtained by considering the graph structure of DBpedia (or the LOD cloud in general). One example of such an improvement could be to use the candidate concepts and the graph structure to “zoom in” on a relevant subgraph of the knowledge structure. This information could subsequently be used for disambiguation purposes, by determining the concepts closest to or contained in this graph. Indeed, in the work presented in this thesis, we have not made any explicit use of any relations (known, discovered, or otherwise) between concepts. In  we have introduced a method which uses language modeling estimations to determine the relatedness of two concepts, an approach which is taken further by Trieschnigg et al. . Such estimations effectively “anchor” the perceived meaning of concepts in the language use surrounding each concept and we believe that this avenue of research deserves further investigation when mapping queries to concepts. Furthermore, related concepts may also be used to perform a kind of “semantic smoothing,” in the context of our proposed conceptual query models.
Our task definition in Chapter 6 required fairly strict matches between the queries and DBpedia concepts, comparable to finding skos:exactMatch or even owl:equivalentClass relations in an ontology matching task. However, our task can also be interpreted in a more liberal sense, where not only exact matches but also semantically related concepts are suggested [26, 218]. For example, when a query contains a book title that is not represented by a concept in DBpedia, we could suggest its author (assuming the book title is mentioned in the author’s Wikipedia page). Similarly, instances for which no concept is found can be linked to a more general concept. We believe that our approach can be adapted to incorporate such semantically related general instances of a specific concept could be defined as a correct concept for mapping.
One other aspect that we intend to investigate in the context of Chapter 6 is how to incorporate information from other parts of the LOD cloud. Our current approach has focused on DBpedia, which might be too limited for some queries. We have shown in  that, although DBpedia covers the open domain well, it does not exhaustively cover entity-related information. Future work should indicate whether traversing links to other, connected knowledge repositories would yield additional relevant concepts. It would also be interesting to consider more “noisy” types of concept languages that were excluded from the thesis, such as Twitter hashtags or del.icio.us tags . Another interesting angle would be to consider a form of automatic keyphrase extraction in this context [116, 220]. Recent research into supervised topic models and labeled LDA [39, 255], as well as work done for word sense disambiguation  and topic identification  could also provide an interesting link between observed text and concept languages.
Furthermore, another abstraction layer may be imposed upon concepts in the form of concept types. Examples of such types are sets of Wikipedia articles, grouped together by a common category or by a shared infobox. In previous work we have shown that information pertaining to such concept types can be useful for generating query suggestions for rare or unseen queries [217, 218] and query log analysis . In this thesis we have solely looked at concepts in their own right, discarding any potential type-based information. If and how this kind of information can be used to improve ad hoc retrieval is an interesting continuation of work presented in the thesis.
Recently, several evaluation campaigns have started to investigate methods for retrieving entities, a task known as entity finding [25, 87]. Both of these define entities as Wikipedia articles, much in the same way as we have used Wikipedia articles as concepts. So, phrased in this way, the goal is not to use Wikipedia based information for ad hoc retrieval but rather the other way around: use documents as evidence towards concept retrieval. Some of the models presented in this thesis (for example those presented in Chapter 6) can be modified or applied to this new task. Another interesting application would be so-called undirected informational queries , where the information need is “open” and the user is solely interested in learning more on a certain topic. We are currently only taking the first steps in these new directions and future work should indicate in which ways the methods and models developed in the thesis can be applied to such tasks.
Finally, answering information needs which contain an explicit relationship type between concepts is a current research topic, as witnessed by a dedicated task at the recently launched TREC Entity Track. As we have shown in , this particular task is currently one of the best candidates for developing techniques which will bridge the gap between semantic web technologies and information retrieval. In  we further show that both semantic web and IR approaches fair well on the task of related entity finding, with each yielding unique sets of relevant results. We have argued then and maintain the position now that semantic web and IR are two sides of the same coin. Especially with the advent of DBpedia, YAGO, and, more generically, the LOD cloud, semantic web requires IR techniques and methodologies for handling the growing volumes of data, whereas IR needs a form of semantic anchoring of the obtained results.