Abstract
For bridging the gap between information retrieval (IR) and databases (DB), this article focuses on the logical view. We claim that IR should adopt three major concepts from DB, namely inference, vague predicates and expressive query languages. By regarding IR as uncertain inference, probabilistic versions of relational algebra and Datalog yield very powerful inference mechanisms for IR as well as allowing for more flexible systems. For dealing with various media and data types, vague predicates form a natural extension of text retrieval methods to attribute values, thus switching from propositional to predicate logic. A more expressive IR query language should support joins, be able to compute aggregated results, and allow for restructuring of the result objects.
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Keywords
- Relational Algebra
- Information Retrieval System
- Vague Predicate
- Probabilistic Database
- Closed World Assumption
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Fuhr, N. (2014). Bridging Information Retrieval and Databases. In: Ferro, N. (eds) Bridging Between Information Retrieval and Databases. PROMISE 2013. Lecture Notes in Computer Science, vol 8173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54798-0_5
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DOI: https://doi.org/10.1007/978-3-642-54798-0_5
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