By David W. Embley, Stephen W. Liddle (auth.), Wilfred Ng, Veda C. Storey, Juan C. Trujillo (eds.)
This e-book constitutes the refereed lawsuits of the thirty second overseas convention on Conceptual Modeling, ER 2013, held in Hong Kong, China, in November 2013. The 23 complete and 17 brief papers offered have been conscientiously reviewed and chosen from 148 abstracts and 126 complete papers submissions. The papers are prepared in topical sections on modeling and reasoning, basics of conceptual modeling, company strategy modeling, community modeling, information semantics, safety and optimization, ontology-based modeling, looking and mining, conceptual modeling and purposes, demonstration papers.
Read or Download Conceptual Modeling: 32th International Conference, ER 2013, Hong-Kong, China, November 11-13, 2013. Proceedings PDF
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Extra info for Conceptual Modeling: 32th International Conference, ER 2013, Hong-Kong, China, November 11-13, 2013. Proceedings
This process has been designed in an automatic and domain-independent way, exploiting unsupervised techniques and the web as a social scale learning source. We exploited these modular ontologies for semantic search, combining them with case-based reasoning. A case is defined by a set of similar queries associated with its relevant results. The case base is used both for ontology module learning and for contextualizing the search process. Module-based similarity is used to retrieve similar cases and to provide end users with alternative documents recommendations.
Understanding and converting an end-user’s natural language input to a valid structured query in an ad-hoc fashion is still a challenging issue. Following this direction, we have proposed a framework for Q&A systems able to define a mapping between recognized semantics of a user’s questions to a structured query model that can be executed on arbitrary data sources . It is based on popular standards like RDF and SparQL and is therefore very easy to adapt to other domains or use cases. Personalization and recommendation techniques are useful to suggest data warehouse queries and help an analyst pursue its exploration.
For example, a person should have only one mother. Because the data is obtained through information extraction and through other means such as crowd sourcing and wiki-like updates by the general public, we allow conﬂicting information to enter into the system, resulting in a myriad of constraint violations: “I’m my own grandpa”, as the saying goes, occurs in the actual (fairly massive) amount of data collected so far . Big Data quality  will become a huge issue for our family-history application.
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