Data fusion according to the principle of polyrepresentation

TY - JOUR

T1 - Data fusion according to the principle of polyrepresentation

AU - Larsen, Birger

AU - Ingwersen, Peter

AU - Lund, Berit

PY - 2009

Y1 - 2009

N2 - We report data fusion experiments carried out on the four best-performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable.The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.

AB - We report data fusion experiments carried out on the four best-performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable.The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.

M3 - Journal article

SN - 2330-1635

VL - 60

SP - 646

EP - 654

JO - American Society for Information Science and Technology. Journal

JF - American Society for Information Science and Technology. Journal

IS - 4

ER -