Retrieving and Categorizing Bioinformatics Publications through a MultiAgent System
| Autor: | Eloisa Vargiu, Andrea Addis, Giuliano Armano, Andrea Manconi |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
0303 health sciences
Computer science Process (engineering) RSS 05 social sciences Information access Document management system computer.file_format Scientific literature computer.software_genre Bioinformatics News aggregator Newspaper Personalization 03 medical and health sciences 0509 other social sciences 050904 information & library sciences computer 030304 developmental biology |
| Zdroj: | Computational Biology and Applied Bioinformatics |
| DOI: | 10.5772/19488 |
| Popis: | The huge and steady increase of available digital documents, together with the corresponding volume of daily updated contents, makes the problem of retrieving and categorizing documents and data a challenging task. To this end, automated content-based document management systems have gained a main role in the field of intelligent information access (Armano et al., 2010). Web retrieval is highly popular and presents a technical challenge due to the heterogeneity and size of the Web, which is continuously growing (see (Huang, 2000), for a survey). In particular, it becomes more and more difficult for Web users to select contents that meet their interests, especially if contents are frequently updated (e.g., news aggregators, newspapers, scientific digital archives, RSS feeds, and blogs). Supporting users in handling the huge and widespread amount of Web information is becoming a primary issue. Among other kinds of information, let us concentrate on publications and scientific literature, largely available on the Web for any topic. As for bioinformatics, it can be observed that the steady work of researchers, in conjunction with the advances in technology (e.g., high-throughput technologies), has arisen in a growing amount of known sequences. The information related with these sequences is daily stored in the form of scientific articles. Digital archives like BMC Bioinformatics1, PubMed Central2 and other online journals and resources are more and more searched for by bioinformaticians and biologists, with the goal of downloading articles relevant to their scientific interests. However, for researchers, it is still very hard to find out which publications are in fact of interest without an explicit classification of the relevant topics they describe. Traditional filtering techniques based on keyword search are often inadequate to express what the user is really searching for. This principle is valid also in the field of scientific publications retrieval, where researchers could obtain a great benefit from the adoption of automated tools able to search for publications related with their interests. To be effective in the task of selecting and suggesting to a user only relevant publications, an automated system should at least be able (i) to extract the required information and (ii) to encode and process it according to a given set of categories. Personalization could also be provided according to user needs and preferences. |
| Databáze: | OpenAIRE |
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