Innovations in Intelligent Machines-4: Recent Advances in by Patrice Bellot, Ludovic Bonnefoy, Vincent Bouvier, Frédéric

By Patrice Bellot, Ludovic Bonnefoy, Vincent Bouvier, Frédéric Duvert, Young-Min Kim (auth.), Colette Faucher, Lakhmi C. Jain (eds.)

This examine quantity is a continuation of our earlier volumes on clever laptop. it's divided into 3 elements. half I offers with mammoth facts and ontologies. It contains examples on the topic of the textual content mining, rule mining and ontology. half II is on knowledge-based structures. It contains context-centered platforms, wisdom discovery, interoperability, consistency and platforms of structures. the ultimate half is on functions. The functions contain prediction, selection optimization and assessment.

This booklet is directed to the researchers who desire to discover the sphere of data engineering further.

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Additional resources for Innovations in Intelligent Machines-4: Recent Advances in Knowledge Engineering

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Collecting and even indexing this information may be easy but using them for analytic purpose and decision-making may be difficult. Let consider the following problem: a company wants to monitor what their customers think about their new smartphone. They obviously cannot read each review or comment about on the Web and one needs to exploit information retrieval (filtering the Web) and information extraction (opinion mining). g. size of the screen, autonomy, OS…), to locate segment of texts dealing with each feature then to extract from these segments the expressed opinion.

We cannot guess what kind of person is ‘‘Jackson’’: is he a politician, an actor or a singer? In this example, one might assume that Jackson refers to ‘‘Mickael Jackson’’ knowing that he owned a theme park called ‘‘Neverland’’. This example shows that without contextual knowledge and by limiting analysis to a single sentence, associating a semantic type to an entity may be very difficult. Such knowledge is of course difficult to gather and formalize in order to be used by a named entity recognizer.

Delimiting boundaries is another challenge for named entity recognition. Is ‘‘Jackson, Alabama’’ correspond to one entity or to two separate entities: one people and one city, two people, two cities? Actually, there is no a single definitive answer. It completely depends on the context. Relationship extraction is the second historical task in Information Extraction [16, 27, 40, 68]. First implementations focused on a set of predefined relation 1 Large Scale Text Mining Approaches for Information Retrieval and Extraction 13 between entities of predefined types.

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