Abstract. This paper suggests the use of proximity measurement in combination with the Okapi probabilistic model. First, using the Okapi system, our investigation was carried out in a distributed retrieval framework to calculate the same relevance score as that achieved by a single centralized index. Second, by applying a term-proximity scoring heuristic to the top documents returned by a keyword-based system, our aim is to enhance retrieval performance. Our experiments were conducted using the TREC8, TREC9 and TREC10 test collections, and show that the suggested approach is stable and generally tends to improve retrieval effectiveness especially at the top documents retrieved.

We have investigated two major issues in Distributed Information Retrieval (DIR), namely: collection selection and search results merging. While most published works on these two issues are based on pre-stored metadata, the approaches described in this paper involve extracting the required information at the time the query is processed. In order to predict the relevance of collections to a given query, we analyse a limited number of full documents (e.g., the top five documents) retrieved from each collection and then consider term proximity within them. On the other hand, our merging technique is rather simple since input only requires document scores and lengths of results lists. Our experiments evaluate the retrieval effectiveness of these approaches and compare them with centralised indexing and various other DIR techniques (e.g., CORI [2][3][23]). We conducted our experiments using two testbeds: one containing news articles extracted from four different sources (2 GB) and another containing 10 GB of Web pages. Our evaluations demonstrate that the retrieval effectiveness of our simple approaches is worth considering.

No abstract

We have investigated two major issues in Distributed Information Retrieval (DIR), namely: collection selection and search results merging. While most published works on these two issues are based on pre-stored metadata, the approaches described in this paper involve extracting the required information at the time the query is processed.In order to predict the relevance of collections to a given query, we analyse a limited number of full documents (e.g., the top five documents) retrieved from each collection and then consider term proximity within them. On the other hand, our merging technique is rather simple since input only requires document scores and lengths of results lists. Our experiments evaluate the retrieval effectiveness of these approaches and compare them with centralised indexing and various other DIR techniques (e.g. , CORI [2][3][23]).We conducted our experiments using two testbeds: one containing news articles extracted from four different sources (2 GB) and another containing 10 GB of Web pages. Our evaluations demonstrate that the retrieval effectiveness of our simple approaches is worth considering.