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Table 7 Comparison of entity detection performance on testing dataset (in percent). SQ and LQ refer to SimpleQuestions and LC-QuAD 2.0 dataset, respectively

From: A better entity detection of question for knowledge graph question answering through extracting position-based patterns

Approach and the dataset(s) used

Acc.

P

R

F1

A. Tested on SQ:

 Dai et al. [7]

75.7

-

-

-

 He and Golub [8]

96.8

-

-

-

 Chao and Li [9]

82.2

-

-

-

 Lukovnikov et al. [10]

82.7

-

-

-

 Azmy et al. [16]

-

-

-

90.3

 Cui et al. [15]

-

97.4

96.1

96.1

 Our approach trained on SQ

99.15

99.25

99.15

99.17

 Our approach trained on SQ, LQ, and QALD

97.1

97.9

97.1

97.4

B. Tested on LQ:

 Evseev and Arkhipov [11]

-

-

-

87

 Our approach trained on LQ

74.09

75.72

74.09

73.87

 Our approach trained on SQ, LQ, and QALD

97.4

98

97.4

97.6

C. Tested on QALD

 Hu, Zou, Yu et al. [13] (QALD 6)

92

–

–

–

 Bakhshi et al. [14] (tested on QALD 9)

78

–

–

–

 *Kuo and Lu [12] (tested on QALD 7)

91

–

–

–

 Our approach: (trained on SQ, LQ, and QALD)

    

  Tested on QALD-1

95.4

–

–

–

  Tested on QALD-2

100

–

–

–

  Tested on QALD-3

100

–

–

–

   Tested on QALD-4

97.5

–

–

–

   Tested on QALD-5

95.1

–

–

–

  Tested on QALD-6

96.4

–

–

–

  Tested on QALD-7

95.4

–

–

–

  Tested on QALD-8

88.2

–

–

–

  Tested on QALD-9

96

–

–

–

  1. * Used a rule-based approach, so no training data is used. ** Kuo and Lu’s performance is not comparable as they used a different training dataset than ours