FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384204114> ?p ?o ?g. }

• W4384204114 endingPage "35" @default.
• W4384204114 startingPage "1" @default.
• W4384204114 abstract "In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches, and these optimized parameters are then used as the search configuration for all subsequent queries. A better approach, however, would be to adapt the parameters to fit the query at hand. Selective query expansion is one such an approach, in which the system decides automatically whether or not to expand the query, resulting in two possible search configurations. This approach was extended recently to include many other parameters, leading to many possible search configurations where the system automatically selects the best configuration on a per-query basis. One problem with this approach is the system training, which requires evaluation of each training query with every possible configuration. In real-world systems, so many parameters and possible values must be evaluated that this approach is impractical, especially when the system must be updated frequently, as is the case for commercial search engines. In general, the more configurations, the greater the effectiveness when configuration selection is appropriate but also the greater the risk of decreasing effectiveness in the case of an inappropriate configuration selection. To determine the ideal configurations to be used for each query in real-world systems, we have developed a method in which a limited number of possible configurations are pre-selected, then used in a meta-search engine that decides the best search configuration for each query. We define a risk-sensitive approach for configuration pre-selection that considers the risk-reward tradeoff between the number of configurations kept and system effectiveness. We define two alternative risk functions to apply to different goals. For final configuration selection, the decision is based on query feature similarities. We compare two alternative risk functions on two query types (ad hoc and diversity) and compare these to more sophisticated machine learning based methods. We find that a relatively small number of configurations (20) selected by our risk-sensitive model is sufficient to obtain results close to the best achievable results for each query. Effectiveness is increased by about 15% according to the P@10 and nDCG@10 evaluation metrics when compared to traditional grid search using a single configuration and by about 20% when compared to learning to rank documents. Our risk-sensitive approach works for both diversity- and ad hoc oriented searches. Moreover, the similarity-based selection method outperforms the more sophisticated approaches. Thus, we demonstrate the feasibility of developing per-query information retrieval systems, which will guide future research in this direction." @default.
• W4384204114 created "2023-07-14" @default.
• W4384204114 creator A5035274820 @default.
• W4384204114 creator A5080180898 @default.
• W4384204114 date "2023-08-21" @default.
• W4384204114 modified "2023-10-17" @default.
• W4384204114 title "Selective Query Processing: A Risk-Sensitive Selection of Search Configurations" @default.
• W4384204114 cites W1605510967 @default.
• W4384204114 cites W1968927634 @default.
• W4384204114 cites W1980730196 @default.
• W4384204114 cites W1985554184 @default.
• W4384204114 cites W1990589796 @default.
• W4384204114 cites W1994401877 @default.
• W4384204114 cites W2004887161 @default.
• W4384204114 cites W2007815473 @default.
• W4384204114 cites W2012327829 @default.
• W4384204114 cites W2025486348 @default.
• W4384204114 cites W2027359109 @default.
• W4384204114 cites W2042024605 @default.
• W4384204114 cites W2060030398 @default.
• W4384204114 cites W2065472179 @default.
• W4384204114 cites W2077789969 @default.
• W4384204114 cites W2081557480 @default.
• W4384204114 cites W2087051999 @default.
• W4384204114 cites W2087131461 @default.
• W4384204114 cites W2087696423 @default.
• W4384204114 cites W2101626488 @default.
• W4384204114 cites W2102563107 @default.
• W4384204114 cites W2104049510 @default.
• W4384204114 cites W2104588805 @default.
• W4384204114 cites W2104679989 @default.
• W4384204114 cites W2108862644 @default.
• W4384204114 cites W2109154214 @default.
• W4384204114 cites W2110202502 @default.
• W4384204114 cites W2112006025 @default.
• W4384204114 cites W2113640060 @default.
• W4384204114 cites W2115939989 @default.
• W4384204114 cites W2118751096 @default.
• W4384204114 cites W2132314908 @default.
• W4384204114 cites W2148416599 @default.
• W4384204114 cites W2155030693 @default.
• W4384204114 cites W2155229791 @default.
• W4384204114 cites W2159859920 @default.
• W4384204114 cites W2162059449 @default.
• W4384204114 cites W2338477879 @default.
• W4384204114 cites W2340394573 @default.
• W4384204114 cites W2388783363 @default.
• W4384204114 cites W2535211222 @default.
• W4384204114 cites W2536532331 @default.
• W4384204114 cites W2749959226 @default.
• W4384204114 cites W2753192203 @default.
• W4384204114 cites W2764149444 @default.
• W4384204114 cites W2772833636 @default.
• W4384204114 cites W2783694856 @default.
• W4384204114 cites W2798597945 @default.
• W4384204114 cites W2798995557 @default.
• W4384204114 cites W2811146799 @default.
• W4384204114 cites W2898640137 @default.
• W4384204114 cites W2904714568 @default.
• W4384204114 cites W2912859456 @default.
• W4384204114 cites W2995888719 @default.
• W4384204114 cites W3208529286 @default.
• W4384204114 cites W4206765718 @default.
• W4384204114 cites W4225091461 @default.
• W4384204114 cites W4252076394 @default.
• W4384204114 cites W976159861 @default.
• W4384204114 doi "https://doi.org/10.1145/3608474" @default.
• W4384204114 hasPublicationYear "2023" @default.
• W4384204114 type Work @default.
• W4384204114 citedByCount "0" @default.
• W4384204114 crossrefType "journal-article" @default.
• W4384204114 hasAuthorship W4384204114A5035274820 @default.
• W4384204114 hasAuthorship W4384204114A5080180898 @default.
• W4384204114 hasBestOaLocation W43842041141 @default.
• W4384204114 hasConcept C118689300 @default.
• W4384204114 hasConcept C119857082 @default.
• W4384204114 hasConcept C124101348 @default.
• W4384204114 hasConcept C157692150 @default.
• W4384204114 hasConcept C164120249 @default.
• W4384204114 hasConcept C177264268 @default.
• W4384204114 hasConcept C192939062 @default.
• W4384204114 hasConcept C199360897 @default.
• W4384204114 hasConcept C23123220 @default.
• W4384204114 hasConcept C41008148 @default.
• W4384204114 hasConcept C81917197 @default.
• W4384204114 hasConcept C97854310 @default.
• W4384204114 hasConcept C99016210 @default.
• W4384204114 hasConceptScore W4384204114C118689300 @default.
• W4384204114 hasConceptScore W4384204114C119857082 @default.
• W4384204114 hasConceptScore W4384204114C124101348 @default.
• W4384204114 hasConceptScore W4384204114C157692150 @default.
• W4384204114 hasConceptScore W4384204114C164120249 @default.
• W4384204114 hasConceptScore W4384204114C177264268 @default.
• W4384204114 hasConceptScore W4384204114C192939062 @default.
• W4384204114 hasConceptScore W4384204114C199360897 @default.
• W4384204114 hasConceptScore W4384204114C23123220 @default.
• W4384204114 hasConceptScore W4384204114C41008148 @default.
• W4384204114 hasConceptScore W4384204114C81917197 @default.

• next