Information Retrieval Research Group

 

PATHS: Personalised Access to Cultural Heritage Spaces

The vision of the PATHS project is to enable: personalised paths through digital library collections; offer suggestions about items to look at and assist in their interpretation; and support the user in knowledge discovery and exploration. We aim to make it easy for users to explore cultural heritage material by taking them along a trial, or pathway, created by experts, by themselves or by other users.

PROMISE: Participative Research labOratory for Multimedia and Multilingual Information Systems Evaluation

Large-scale worldwide experimental evaluations provide fundamental contributions to the advancement of state-of-the-art techniques through common evaluation procedures, regular and systematic evaluation cycles, comparison and benchmarking of the adopted approaches, and spreading of knowledge. In the process, vast amounts of experimental data are generated that beg for analysis tools to enable interpretation and thereby facilitate scientific and technological progress

Understanding the Annotation Process: Annotation for Big Data

Data is being collected and created at the fastest rate in human history; by the far the vast majority of this is in digital format. Allied with this, what was previously "offline" information can now be digitised quickly and cheaply e.g. old mAHRCanuscripts, maps etc. This vast collection of existing and new information creates new opportunities and also difficulties. For a lot of this information to be useful it must be categorised and annotated in some way, so that sense can be made of the data and also so that the correct data can be accessed more easily. In this innovative project we aim to gain a better understanding of this annotation process so that we can provide guidelines, approaches and processes for providing the most cost effective and accurate annotations for data sets.

VisualSense: Tagging visual data with semantic descriptions

The visual sense project aims at mining automatically the semantic content of visual data to enable "machine reading" of images. In recent years, we have witnessed significant advances in the automatic recognition of visual concepts (VCR). These advances allowed for the creation of systems that can automatically generate keyword-based image annotations. The goal of this project is to move a step forward and predict semantic image representations that can be used to generate more informative sentence-based image annotations. Thus, facilitating search and browsing of large multi-modal collections. More specifically, the project targets three case studies, namely image annotation, re-ranking for image search, and automatic image illustration of articles.

Developing a Taxonomy of Search Sessions

The project seeks to develop a categorisation scheme to describe common patterns of user-system interaction behaviour as recorded in search engine log files. In particular the project is focused on sessions, a period of continued usage that provides multiple unit of interaction with which to study how people use search systems. Search (or query) logs are created as the users of search systems (e.g. web search engines and library catalogues) interact with them to find relevant information.