| Title |
Sherlock: A Semi-automatic Framework for Quiz Generation Using a Hybrid Semantic Similarity Measure
|
|---|---|
| Published in |
Cognitive Computation, August 2015
|
| DOI | 10.1007/s12559-015-9347-7 |
| Pubmed ID | |
| Authors |
Chenghua Lin, Dong Liu, Wei Pang, Zhe Wang |
| Abstract |
In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated quizzes. Difficulty scaling is non-trivial, and it is fundamentally related to cognitive science. We approach the problem with a new angle by perceiving the level of knowledge difficulty as a similarity measure problem and propose a novel hybrid semantic similarity measure using linked data. Extensive experiments show that the proposed semantic similarity measure outperforms four strong baselines with more than 47 % gain in clustering accuracy. In addition, we discovered in the human quiz test that the model accuracy indeed shows a strong correlation with the pairwise quiz similarity. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Malaysia | 1 | 3% |
| Unknown | 36 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 16% |
| Student > Ph. D. Student | 4 | 11% |
| Student > Doctoral Student | 4 | 11% |
| Lecturer | 4 | 11% |
| Student > Bachelor | 3 | 8% |
| Other | 9 | 24% |
| Unknown | 7 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 18 | 49% |
| Engineering | 2 | 5% |
| Unspecified | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Mathematics | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 12 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 December 2015.
All research outputs
#15,690,772
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Outputs from Cognitive Computation
#150
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Outputs of similar age
#156,001
of 265,285 outputs
Outputs of similar age from Cognitive Computation
#2
of 13 outputs
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