NDLI: A case for probabilistic logic for scalable patent retrieval

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A case for probabilistic logic for scalable patent retrieval

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A case for probabilistic logic for scalable patent retrieval

Content Provider	ACM Digital Library
Author	Azzam, Hany Roelleke, Thomas Klampanos, Iraklis A.
Abstract	Patent retrieval has emerged as an important application of information retrieval. Inherent properties of patent searching, such as large corpora, document length and the use of terminology have created the need for alternative approaches to searching. Logic-based information retrieval, as it is modelled by DB+IR systems, can accommodate these needs through its power of abstraction and the use of database-friendly query languages. However, there is a trade-off between expressiveness and efficiency. We propose to tackle such efficiency issues through distribution and parallelisation. In this paper we present our arguments in favour of a parallelised patent searching solution built on top of a probabilistic DB+IR system. Our contributions are both conceptual as well as technical. We demonstrate the flexibility of this approach by modelling two resource selection algorithms in probabilistic logic, expressed in probabilistic Datalog -- a rule-based language designed for expressing database-related tasks. Then, we provide early experimental indications which support the feasibility and technical soundness of this approach.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781605588094
DOI	10.1145/1651343.1651345
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-11-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Design Datalog Probabilistic Patent Modular Logic Db+ir
Content Type	Text
Resource Type	Article

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