Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Enriching Community Networks by supporting deliberation

The increasing shift of attention from e-government to e-governance, even at the local level, requires technological solutions designed to support deliberative processes. We believe that to answer the request for local governance it is necessary to put at stake the background accumulated by community networks for undertaking the development (the design, implementation and testing) of a so-cio-technical, computer-enabled, trusted environment for e-participation enriched with deliberative tools. We call this environment Deliberative Community Net-work, in order to stress that its main goal is to overcome the intrinsic limits of community and civic networks by introducing deliberative facilities that provide support to the decision-making processes. The paper presents the conceptual framework behind the design of Deliberative Community Networks, their logical architecture and a first prototype developed for supporting public dialog in the occasion of the 2006 Municipal Elections in Milan, Italy. The feedbacks from this experience, presented in the conclusions, are the input for the next release of the system currently under development