Information retrieval as semantic inference: a Graph Inference model applied to medical search

This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem—the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search.     

Reflections on historical series of art prices: Reitlinger's data revisited

This note of research argues that Reitlinger's series, and all other series such as Meyer's which are based on official auction sales reports, suffer from significant distortions. This distortion is caused by two main factors: a) the absence of data relating to the identity and the profession of the market agents involved in the transactions recorded in time series; b) the criteria of selection adopted to construct the historical series of prices.     

Does social labelling encourage child schooling and discourage child labour in Nepal?

This paper explores the determinants of child labour vis-à-vis child schooling. It further examines the influence of non-governmental organisations (NGOs) which are engaged in social labelling, on the incidence of child labour and schooling trade-off. The empirical results show that the probability of child schooling increases as well as child labour decreases if a carpet firm has adopted a labelling scheme. It can also be shown that labelling NGOs have a significant positive influence on sending the ex-child labourers to school.     

The predictive validity of using admissions testing and multiple mini-interviews in undergraduate university admissions

The aim of this study was to evaluate the predictive validity of a two-step admissions procedure that included a cognitive ability test followed by multiple mini-interviews (MMIs) used to assess non-cognitive skills, compared to grade-based admissions relative to subsequent drop-out rates and academic achievement after one and two years of study. The participants consisted of the entire population of 422 psychology students who were admitted to the University of Southern Denmark between 2010 and 2013. The results showed significantly lower drop-out rates after the first year of study, and non-significant lower drop-out rates after the second year of study for the admission procedure that included the assessment of non-cognitive skills though the MMI and the admissions test. Furthermore, this admission procedure resulted in a significant lower risk of failing the final exam after the first and second year of study, compared to the grade-based admissions procedure. Finally, students admitted through the MMI and the admissions test scored significantly higher on academic learning self-efficacy and critical thinking compared to students selected by grades. The implications for higher education admissions are discussed.     

An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014

Since its inception in 2013, one of the key contributions of the CLEF eHealth evaluation campaign has been the organization of an ad-hoc information retrieval (IR) benchmarking task. This IR task evaluates systems intended to support laypeople searching for and understanding health information. Each year the task provides registered participants with standard IR test collections consisting of a document collection and topic set. Participants then return retrieval results obtained by their IR systems for each query, which are assessed using a pooling procedure. In this article we focus on CLEF eHealth 2013 and 2014s retrieval task, which saw topics created based on patients’ information needs associated with their medical discharge summaries. We overview the task and datasets created, and the results obtained by participating teams over these two years. We then provide a detailed comparative analysis of the results, and conduct an evaluation of the datasets in the light of these results. This twofold study of the evaluation campaign teaches us about technical aspects of medical IR, such as the effectiveness of query expansion; the quality and characteristics of CLEF eHealth IR datasets, such as their reliability; and how to run an IR evaluation campaign in the medical domain.     

Displacement and Supplemental Effects of the Mobile Internet on Fixed Internet Use

Mobile devices facilitate the use of mobile services that are similar to services used via fixed Internet connections. The objective of this article is to assess displacement effects between fixed and mobile Internet services, and to research the role of technical capabilities of mobile handsets and demographics as moderators. This study is based on a sample of 628 respondents who have access to fixed and mobile Internet services. The respondents' usage of services was analyzed. An online questionnaire was used to collect data on the use of information, communication, entertainment, and transaction services. Mobile Internet reinforces, rather than replaces, Internet usage. When people possess an iPhone^® or (mobile) laptop, this reinforcement effect is present for entertainment services. With respect to transaction services, the Blackberry^® makes a stronger case. With the exception of gender, demographic characteristics do not have a moderating effect. Women display a stronger reinforcement effect than men in relation to transaction services. To the best of our knowledge, no study to date has investigated the displacement effects of fixed Internet services by the mobile handset. This empirical study contributes to the literature regarding the displacement effects of mobile Internet services.