By Nicola Ferro
The examine domain names of knowledge retrieval and databases have normally followed varied techniques to info administration. although, lately, there was an expanding cross-fertilization one of the fields and now many study demanding situations are transversal to them. With this in brain, a iciness college was once equipped in Bressanone, Italy, in February 2013, in the context of the EU-funded examine venture PROMISE (Participative examine Laboratory for Multimedia and Multilingual info structures Evaluation). PROMISE geared toward advancing the experimental evaluate of complicated multimedia and multilingual details structures as a way to help participants, advertisement entities and groups, who layout, strengthen, hire and increase such complicated platforms. the general objective of PROMISE was once to bring a unified atmosphere gathering facts, wisdom, instruments and methodologies and to aid the consumer group interested in experimental assessment. This ebook constitutes the end result of the PROMISE wintry weather college 2013 and comprises nine invited lectures from the examine 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, details retrieval, experimental assessment, metrics and information, semantic seek, key-phrase seek in databases, semi-structured seek, assessment either in info retrieval and databases, crowdsourcing and social media.
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Additional resources for Bridging Between Information Retrieval and Databases: PROMISE Winter School 2013, Bressanone, Italy, February 4-8, 2013. Revised Tutorial Lectures
C Springer-Verlag Berlin Heidelberg 2014 32 K. Bontcheva, V. Tablan, and H. Cunningham powerful searches for documents, based on knowledge and relationships that are external to those documents. For example, a query on ﬂooding in the UK would retrieve a document about ﬂoods in Sheﬃeld, even if the latter does not explicitly mention the UK. As can be seen from these example, what is required here is a source of knowledge that there are several cities called “Cambridge”, one in the UK (which is a country) and one in Massachusetts, which is a state in the USA.
Therefore, semantic search tries to oﬀer users more precise and relevant results, by using semantics. Frequently this semantics is encoded in ontologies, which can be deﬁned as “a formal speciﬁcation of a shared conceptualisation” . Alternatively, Google refers to such semantics as knowledge graphs and to semantic search as “searching for things, not strings” . Semantic search requires some natural language processing techniques for understanding word meaning. Since some of the most frequently used searches are for persons, locations, organisations, and other named entities , one of the most widely used techniques for interpreting this meaning are named entity recognition  and semantic annotation .
In brief, RDFa (or Resource Description Framework in attributes) is a W3C standard. It adds a set of attribute-level extensions to HTML and XHTML, to describe rich metadata, embedded within Web documents. org is a similar endeavour, which is supported by many of the web search engines, including Bing, Google, Yahoo, and Yandex. g. a restaurant’s phone number and address . Even though useful for some queries, such metadata nevertheless falls short of enabling the kinds of semantic queries discussed above.