Extracting relevant information using E-R models{2}

Jay Urbain’s (2012), “User-driven Relational Models for Entity-Relation Search and Extraction,” submission was a workshop at the 1st Joint International Workshop on Entity-oriented and Semantic Search. Urbain’s research introduced a method to querying databases and data that will not only increase retrieval time but also the relevance of the information extracted. Furthermore, extraction using entities and the depending relations will present new relevant information and knowledge to the user.
After covering the first chapter in our text for CIS 305, we briefly get acquainted with database modeling and the conventions of entities and relations. Urbain’s paper uses entity-relation model to devise a search algorithm that utilizes the relationships of entities in a search term that can out-perform other methods. Most importantly, relevant information of what is queried can present new knowledge that the user may have not thought to search or had known was related. Search systems like information extraction are limited to the general set of relations and entities. While information retrieval systems are time consuming in finding relevant information.
I find this very relevant to the project example given in our text describing a scenario of a database project. As a systems analyst, you have to think about the future of the database and how the client will use it and may need to query a database for more than just facts but also to forecast. Developing a system that can provide relevance along with accuracy can make the database much more robust and in the end make the client an advocate for your business.

Urbain, Jay. (2012). User-driven Relational Models for Entity-Relation Search and Extraction. 1st Joint International Workshop on Entity-Oriented and Semantic Search, 12-16 Aug. 2012
doi: 10.1145/2379307.2379312. Retrieved September 30, 2012.