Challenges and opportunities of context-aware information access

Ubiquitous computing environments embedding a wide range of pervasive computing technologies provide a challenging and exciting new domain for information access. Individuals working in these environments are increasingly permanently connected to rich information resources. An appealing opportunity of these environments is the potential to deliver useful information to individuals either from their previous information experiences or external sources. This information should enrich their life experiences or make them more effective in their endeavours. Information access in ubiquitous computing environments can be made "context-aware" by exploiting the wide range context data available describing the environment, the searcher and the information itself. Realizing such a vision of reliable, timely and appropriate identification and delivery of information in this way poses numerous challenges. A central theme in achieving context-aware information access is the combination of information retrieval with multiple dimensions of available context data. Potential context data sources, include the user's current task, inputs from environmental and biometric sensors, associated with the user's current context, previous contexts, and document context, which can be exploited using a variety of technologies to create new and exciting possibilities for information access

Integrating social media with existing knowledge and information for crisis response

Existing studies on social media in the context of crisis have studied the content of items and their patterns of transmission. However, social media content generated during a crisis will generally be unstructured and only reflect the immediate experiences of the authors, while the volumes of data created can make rapid interpretation very challenging. Crisis situations can be characterized with various expected attributes. In many situations there will be large amounts of information relevant to the situation already available. We argue that existing natural language engineering technologies can be integrated with emerging social media content utilization techniques for more powerful exploitation of social media content in crisis response

Beyond English text: Multilingual and multimedia information retrieval.

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Improving organic recollection with memory prosthetics

The limitations of human memory are well documented [ERJ07]. As exposure to information continues to increase, information-based memory problems will become even more commonplace and systems will be needed to support peoples’ organic memory (OM). A potential solution, that has been a popular research focus in recent years, is to create prosthetic memory devices (PM) –digital stores containing all of an individual’s information, media content and context data [GBL+02]. The idea is to create a resource where the user can lookup forgotten information if and when the need arises. There are obvious trade-offs between the permanence and reliability of PM against the speed of access that can be achieved with OM [KW07] and consequently, most research in this area has aimed at improving facilities to search within these stores e.g. [KJ10]. While there is obvious merit in this approach, one problem is that as search facilities improve and costs of re-finding decrease, it is possible that users will become overly reliant on PM with detrimental effects on OM, comparable to the effects of continued calculator use on mental arithmetic abilities. Our work will complement re-finding research by investigating how PM content can be used to facilitate improved organic recollection, reducing the need to re-find. In this position statement, we outline the fundamental approach and present some key open research questions to be addressed by our research

Exploring narrative presentation for large multimodal lifelog collections through card sorting

Using lifelogging tools, personal digital artifacts are collected continuously and passively throughout each day. The wealth of information such an archive contains on our life history provides novel opportunities for the creation of digital life narratives. However, the complexity, volume and multimodal nature of such collections create barriers to achieving this. Nine participants engaged in a card-sorting activity designed to explore practices of content reduction and presentation for narrative composition. We found the visual modalities to be most fluent in communicating experience with other modalities serving to support them and that the users employed the salient themes of the story to organise, arrange and facilitate filtering of the content

DCU at VideoClef 2008

We describe a baseline system for the VideoCLEF Vid2RSS task. The system uses an unaltered off-the-shelf Information Retrieval system. ASR content is indexed using default stemming and stopping methods. The subject categories are populated by using the category label as a query on the collection, and assigning the retrieved items to that particular category. We describe the results of the system and provide some high-level analysis of its performance

When to cross Over? Cross-language linking using Wikipedia for VideoCLEF 2009

We describe Dublin City University (DCU)'s participation in the VideoCLEF 2009 Linking Task. Two approaches were implemented using the Lemur information retrieval toolkit. Both approaches rst extracted a search query from the transcriptions of the Dutch TV broadcasts. One method rst performed search on a Dutch Wikipedia archive, then followed links to corresponding pages in the English Wikipedia. The other method rst translated the extracted query using machine translation and then searched the English Wikipedia collection directly. We found that using the original Dutch transcription query for searching the Dutch Wikipedia yielded better results

Applying the KISS principle for the CLEF-IP 2010 prior art candidate patent search task

We present our experiments and results for the DCU CNGL participation in the CLEF-IP 2010 Candidate Patent Search Task. Our work applied standard information retrieval (IR) techniques to patent search. In addition, a very simple citation extraction method was applied to improve the results. This was our second consecutive participation in the CLEF-IP tasks. Our experiments in 2009 showed that many sophisticated approach to IR do not improve the retrieval effectiveness for this task. For this reason of we decided to apply only simple methods in 2010. These were demonstrated to be highly competitive with other participants. DCU submitted three runs for the Prior Art Candidate Search Task, two of these runs achieved the second and third ranks among the 25 runs submitted by nine different participants. Our best run achieved MAP of 0.203, recall of 0.618, and PRES of 0.523

Venturing into the labyrinth: the information retrieval challenge of human digital memories

Advances in digital capture and storage technologies mean that it is now possible to capture and store one’s entire life experiences in a Human Digital Memory (HDM). However, these vast personal archives are of little benefit if an individual cannot locate and retrieve significant items from them. While potentially offering exciting opportunities to support a user in their activities by providing access to information stored from previous experiences, we believe that the features of HDM datasets present new research challenges for information retrieval which must be addressed if these possibilities are to be realised. Specifically we postulate that effective retrieval from HDMs must exploit the rich sources of context data which can be captured and associated with items stored within them. User’s memories of experiences stored within their memory archive will often be linked to these context features. We suggest how such contextual metadata can be exploited within the retrieval process

DCU search runs at MediaEval 2012: search and hyperlinking task

We describe the runs for our participation in the Search sub-task of the Search and Hyperlinking Task at MediaEval 2012. Our runs are designed to form a retrieval baseline by using time-based segmentation of audio transcripts incorporating pause information and a sliding window to define the retrieval segments boundaries with a standard language modelling information retrieval strategy. Using this baseline system runs based on transcripts provided by LIUM were better for all evaluation metrics, than those using transcripts provided by LIMSI