Name
Abstract | ||
|---|---|---|
Keyword search within databases has become a hot topic within the research community as databases store increasing amounts of information. Users require an effective method to retrieve information from these databases without learning complex query languages (viz. SQL). Despite the recent research interest, performance and search effectiveness have not received equal attention, and scoring functions in particular have become increasingly complex while providing only modest benefits with regards to the quality of search results. An analysis of the factors appearing in existing scoring functions suggests that some factors previously deemed critical to search effectiveness are at best loosely correlated with relevance. We consider a number of these different scoring factors and use machine learning to create a new scoring function that provides significantly better results than existing approaches. We simplify our scoring function by systematically removing the factors with the lowest weight and show that this version still outperforms the previous state-of-the-art in this area. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1145/2063576.2063820 | CIKM |
Keywords | Field | DocType |
research community,recent research interest,complex query language,better result,relational keyword search,scoring function,search effectiveness,different scoring factor,keyword search,new scoring function,search result,query language,ranking,user requirements,learning to rank,machine learning,score function | SQL,Keyword density,Data mining,Learning to rank,Query language,Information retrieval,Ranking,Computer science,Effective method,Keyword search | Conference |
Citations | PageRank | References |
6 | 0.48 | 44 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joel Coffman | 1 | 57 | 2.90 |
| Alfred C. Weaver | 2 | 473 | 55.79 |