Information access tasks and evaluation for personal lifelogs

Emerging personal lifelog (PL) collections contain permanent digital records of information associated with individuals’ daily lives. This can include materials such as emails received and sent, web content and other documents with which they have interacted, photographs, videos and music experienced passively or created, logs of phone calls and text messages, and also personal and contextual data such as location (e.g. via GPS sensors), persons and objects present (e.g. via Bluetooth) and physiological state (e.g. via biometric sensors). PLs can be collected by individuals over very extended periods, potentially running to many years. Such archives have many potential applications including helping individuals recover partial forgotten information, sharing experiences with friends or family, telling the story of one’s life, clinical applications for the memory impaired, and fundamental psychological investigations of memory. The Centre for Digital Video Processing (CDVP) at Dublin City University is currently engaged in the collection and exploration of applications of large PLs. We are collecting rich archives of daily life including textual and visual materials, and contextual context data. An important part of this work is to consider how the effectiveness of our ideas can be measured in terms of metrics and experimental design. While these studies have considerable similarity with traditional evaluation activities in areas such as information retrieval and summarization, the characteristics of PLs mean that new challenges and questions emerge. We are currently exploring the issues through a series of pilot studies and questionnaires. Our initial results indicate that there are many research questions to be explored and that the relationships between personal memory, context and content for these tasks is complex and fascinating

Workshop on evaluating personal search

The first ECIR workshop on Evaluating Personal Search was held on 18th April 2011 in Dublin, Ireland. The workshop consisted of 6 oral paper presentations and several discussion sessions. This report presents an overview of the scope and contents of the workshop and outlines the major outcomes

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

Exploring Memory Cues to Aid Information Retrieval from Personal LifeLog Archives

The expansion of personal information archives and the emerging field of Personal Lifelogs (PLs) are creating new challenges for information retrieval (IR). While studies have demonstrated the difficulties of IR for these massive data collection [1], we should also think about how we can opportunities and benefits from integrating these data sources as a component of “digital memories” , considering their rich connections with the users‟ memory. We observed that most existing approaches to personal archive IR are mostly technology-driven. Although in recent years studies in Personal Information management (PIM) have claimed to make use of the human memory features, and many works have been reported as investigating well-remembered features of computer files (documents, email, photos). Yet, these explorations are usually confined to the attributes or feature that current computer file systems or technology have provided. I believe that there are important and potentially useful data attributes that these studies have ignored. In addition, current personal search interfaces provide searching options based on what is available in the system, e.g. require users to fill in the calendar date, regardless of the fact that people actually don‟t often encode „time‟ in such a way. My PhD project aims to explore what users actually tend to recall in different personal achieve information seeking tasks, how to present searching options to cater for the right type or format of information that users can recall, and how to exploit this information in an IR system for personal lifelog archives. In this paper, I discuss the limits and advantages of some related work, and present my current and proposed study, with an outlook of an interface that I plan to develop to explore my proposals

LIKES: Educating the Next Generation of Knowledge Society Builders

Although information technology (IT) is used extensively in the education of all disciplines, the computing-related fields are facing tremendous challenges, such as declining student enrollment and a lack of representation from minorities and women. Strengthening the connection between computing and other fields could help instructors to integrate IT in their teaching and to support the learning of students, who will become the next generation of Knowledge Society builders. Presently, this connection is weak due to the lack of interdisciplinary collaboration and mutual understanding among faculty in computing and other fields. Our ongoing effort entitled “Living in the KnowlEdge Society (LIKES) Community Building Project” aims to build a community that will define a socially-relevant way to make systemic changes in how computing and IT concepts are taught and applied in both computing and other fields. In this paper, we review previous efforts in this area and summarize our project’s achievements and lessons learned. We also provide recommendations on integrating IT into other curricula and on strengthening interdisciplinary collaborations

A new metric for patent retrieval evaluation

Patent retrieval is generally considered to be a recall-oriented information retrieval task that is growing in importance. Despite this fact, precision based scores such as mean average precision (MAP) remain the primary evaluation measures for patent retrieval. Our study examines different evaluation measures for the recall-oriented patent retrieval task and shows the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and user search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the expected search effort of patent searchers

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017