Search (4 results, page 1 of 1)

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Abstract

Although retrieval systems based on probabilistic models will rank the objects (e.g., documents) being retrieved according to the probability of some matching criterion (e.g., relevance), they rarely yield an actual probability, and the scoring function is interpreted to be purely ordinal within a given retrieval task. In this brief communication, it is shown that some scoring functions possess the likelihood property, which means that the scoring function indicates the likelihood of matching when compared to other retrieval tasks, which is potentially more useful than pure ranking although it cannot be interpreted as an actual probability. This property can be detected by using two modified effectiveness measures: entire precision and entire recall.

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Date

22. 3.2003 19:27:36

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Date

5. 6.2006 11:22:23

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Abstract

We propose an approach to content-based Distributed Information Retrieval based on the periodic and incremental centralization of full-content indices of widely dispersed and autonomously managed document sources. Inspired by the success of the Open Archive Initiative's (OAI) Protocol for metadata harvesting, the approach occupies middle ground between content crawling and distributed retrieval. As in crawling, some data move toward the retrieval process, but it is statistics about the content rather than content itself; this grants more efficient use of network resources and wider scope of application. As in distributed retrieval, some processing is distributed along with the data, but it is indexing rather than retrieval; this reduces the costs of content provision while promoting the simplicity, effectiveness, and responsiveness of retrieval. Overall, we argue that the approach retains the good properties of centralized retrieval without renouncing to cost-effective, large-scale resource pooling. We discuss the requirements associated with the approach and identify two strategies to deploy it on top of the OAI infrastructure. In particular, we define a minimal extension of the OAI protocol which supports the coordinated harvesting of full-content indices and descriptive metadata for content resources. Finally, we report on the implementation of a proof-of-concept prototype service for multimodel content-based retrieval of distributed file collections.