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By Nicola Ferro

The study domain names of data retrieval and databases have frequently followed diverse methods to info administration. even if, in recent times, there was an expanding cross-fertilization one of the fields and now many learn demanding situations are transversal to them. With this in brain, a iciness tuition used to be prepared in Bressanone, Italy, in February 2013, in the context of the EU-funded learn venture PROMISE (Participative examine Laboratory for Multimedia and Multilingual details platforms Evaluation). PROMISE aimed toward advancing the experimental overview of complicated multimedia and multilingual details structures so that it will aid members, advertisement entities and groups, who layout, enhance, hire and increase such complicated structures. the final aim of PROMISE used to be to bring a unified atmosphere accumulating facts, wisdom, instruments and methodologies and to aid the consumer neighborhood interested by experimental overview. This publication constitutes the result of the PROMISE wintry weather tuition 2013 and includes nine invited lectures from the learn domain names of data retrieval and databases plus brief papers of the simplest pupil poster awards. a wide number of subject matters are coated, together with databases, info retrieval, experimental evaluate, metrics and records, semantic seek, key-phrase seek in databases, semi-structured seek, review either in details retrieval and databases, crowdsourcing and social media.

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Extra resources for Bridging Between Information Retrieval and Databases: PROMISE Winter School 2013, Bressanone, Italy, February 4-8, 2013. Revised Tutorial Lectures

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1 Semantic Search Queries Since semantic search queries need to contain both text-based keywords and formal SPARQL-like queries over the ontology, they are often referred to as 38 K. Bontcheva, V. Tablan, and H. Cunningham hybrid queries. The Semplore system [33], for example, uses conjunctive hybrid query graphs, similar to SPARQL, but enhanced with a “virtual” concept called keyword concept W . A similar approach has been taken in the Broccoli system [21], which has a special occurs − with relation, the value of which is the free text keyword.

Crestani Table 3. Top 10 emoticons in each dataset with their relative frequency as a percentage of all emoticon occurrences. We omit the few counts for WSJ,AP,FT since they are not informative. ). 04 user-generated documents (Kongregate, Twitter, Myspace, Slashdot) present a large number of colloquial and informal tokens, such as emoticons and shoutings, that are used to improve the expressiveness of the communication. In the standard collections containing professional edited documents (WSJ, AP, FT), instead, the communication remains on a formal and neutral level (having these collection almost zero counts for emoticons and shoutings).

Bontcheva, V. Tablan, and H. Cunningham Fig. 1. Example Semantic Annotation in GATE [26] resources, including latitude and longitude, the URI of the parent country, and the URIs of the relevant NUTS administrative regions. Information Extraction (IE), a form of natural language analysis, has become the natural language processing technology of choice, for bridging the gap between unstructured text and formal knowledge expressed in ontologies. Ontology-Based IE (OBIE) is IE which is adapted specifically for the semantic annotation task [27].

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