Scientific

DEiXTo related papers

icon-article-96x96DEiXTo extracts cultural events form various sites and feeds the database of myVisitPlanner, a personalized itinerary planning system for tourism. Additionally, DEiXToBot agents perform focused Google Search looking for new sources/sites of cultural events. A web crawler and a page classifier developed by the project’s consortium, help in this task.

icon-article-96x96After almost 7 years of success stories, we wrote a short descriptive paper about DEiXTo. From now on, if you want to reference DEiXTo in your thesis, project or scientific work, please use the following reference:

  • F. Kokkoras, K. Ntonas, N. Bassiliades. “DEiXTo: A web data extraction suite“, In proc. of the 6th Balkan Conference in Informatics (BCI-2013), September 19-21, 2013, Thessaloniki, Greece, pp. 9-12, ACM New York, USA, 2013.

deixto-cloud

icon-article-96x96DEiXTo was a key module in the federated open source code search engine OCEAN, which simultaneously asks, in real time, existing open source code search engine sites and aggregates the results. The following paper detail the way we overcome the integration obstacles, by combining provided APIs, browser automation and web content extraction techniques.

icon-article-96x96DEiXTo has been successfully used in combination with “MOpiS”, a multiple opinion summarization algorithm. DEiXTo collects opinion data from on-line shops in real-time and MOpiS generates summaries of these opinions by taking into consideration implicit metadata information that accompanies the customers’ opinions. MOpiS is described in the following paper:

A limited version of MOpiS is available online, for real-time demonstration.

icon-article-96x96DEiXTo has been successfully used in “ReSum”, a Review Summarization Algorithm which was the final year thesis of Efstratia Lampridou, a BSc student in the Department of Informatics – LPIS Lab (Aristotle University of Thessaloniki). Extraction rules built with GUI DEiXTo are used to collect on-line product reviews posted by customers of on-line shops. ReSum summarizes those reviews in a way described in the following paper:

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