Exploiting synergy between ontologies and recommender systems

Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations.Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured

Transforming Graph Representations for Statistical Relational Learning

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

Parallel computing in information retrieval - An updated review

The progress of parallel computing in Information Retrieval (IR) is reviewed. In particular we stress the importance of the motivation in using parallel computing for Text Retrieval. We analyse parallel IR systems using a classification due to Rasmussen [1] and describe some parallel IR systems. We give a description of the retrieval models used in parallel Information Processing.. We describe areas of research which we believe are needed

Integrating information seeking and information structuring: spatial hypertext as an interface to the digital library.

Information seeking is the task of finding documents that satisfy the information needs of a person or organisation. Digital Libraries are one means of providing documents to meet the information needs of their users - i.e. as a resource to support information seeking. Therefore, research into the activity of information seeking is key to the development and understanding of digital libraries. Information structuring is the activity of organising documents found in the process of information seeking. Information structuring can be seen as either part of information seeking, or as a sepárate, complementary activity. It is a task performed by the seeker themselves and targeted by them to support their understanding and the management of later seeking activity. Though information structuring is an important task, it receives sparse support in current digital library Systems. Spatial hypertexts are computer software Systems that have been specifically been developed to support information structuring. However, they seldom are connected to Systems that support information seeking. Thus to day, the two inter-related activities of information seeking and information structuring have been supported by disjoint computer Systems. However, a variety of research strongly indicates that in physical environments, information seeking and information structuring are closely inter-related activities. Given this connection, this thesis explores whether a similar relationship can be found in electronic information seeking environments. However, given the absence of a software system that supports both activities well, there is an immédiate practical problem. In this thesis, I introduce an integrated information seeking and structuring System, called Garnet, that provides a spatial hypertext interface that also supports information seeking in a digital library. The opportunity of supporting information seeking by the artefacts of information structuring is explored in the Garnet system, drawing on the benefits previously found in supporting one information seeking activity with the artefacts of another. Garnet and its use are studied in a qualitative user study that results in the comparison of user behaviour in a combined electronic environment with previous studies in physical environments. The response of participants to using Garnet is reported, particularly regarding their perceptions of the combined system and the quality of the interaction. Finally, the potential value of the artefacts of information structuring to support information seeking is also evaluated