-
Qu, R.; Fang, Y.; Bai, W.; Jiang, Y.: Computing semantic similarity based on novel models of semantic representation using Wikipedia (2018)
0.01
0.0064942986 = product of:
0.016235746 = sum of:
0.008341924 = weight(_text_:a in 5052) [ClassicSimilarity], result of:
0.008341924 = score(doc=5052,freq=12.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.15602624 = fieldWeight in 5052, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=5052)
0.007893822 = product of:
0.015787644 = sum of:
0.015787644 = weight(_text_:information in 5052) [ClassicSimilarity], result of:
0.015787644 = score(doc=5052,freq=8.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.19395474 = fieldWeight in 5052, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=5052)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- Computing Semantic Similarity (SS) between concepts is one of the most critical issues in many domains such as Natural Language Processing and Artificial Intelligence. Over the years, several SS measurement methods have been proposed by exploiting different knowledge resources. Wikipedia provides a large domain-independent encyclopedic repository and a semantic network for computing SS between concepts. Traditional feature-based measures rely on linear combinations of different properties with two main limitations, the insufficient information and the loss of semantic information. In this paper, we propose several hybrid SS measurement approaches by using the Information Content (IC) and features of concepts, which avoid the limitations introduced above. Considering integrating discrete properties into one component, we present two models of semantic representation, called CORM and CARM. Then, we compute SS based on these models and take the IC of categories as a supplement of SS measurement. The evaluation, based on several widely used benchmarks and a benchmark developed by ourselves, sustains the intuitions with respect to human judgments. In summary, our approaches are more efficient in determining SS between concepts and have a better human correlation than previous methods such as Word2Vec and NASARI.
- Source
- Information processing and management. 54(2018) no.6, S.1002-1021
- Type
- a
-
Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019)
0.01
0.005948606 = product of:
0.014871514 = sum of:
0.008173384 = weight(_text_:a in 5245) [ClassicSimilarity], result of:
0.008173384 = score(doc=5245,freq=8.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.15287387 = fieldWeight in 5245, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=5245)
0.0066981306 = product of:
0.013396261 = sum of:
0.013396261 = weight(_text_:information in 5245) [ClassicSimilarity], result of:
0.013396261 = score(doc=5245,freq=4.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.16457605 = fieldWeight in 5245, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=5245)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- A cross-level model based on the information processing perspective and trait activation theory was developed and tested in order to investigate the effects of individual-level epistemic motivation and team-level task reflexivity on three different individual contribution behaviors (i.e., adding, deleting, and revising) in the process of knowledge creation on team wikis. Using the Hierarchical Linear Modeling software package and the 2-wave data from 166 individuals in 51 wiki-based teams, we found cross-level interaction effects between individual epistemic motivation and team task reflexivity on different knowledge contribution behaviors on wikis. Epistemic motivation exerted a positive effect on adding, which was strengthened by team task reflexivity. The effect of epistemic motivation on deleting was positive only when task reflexivity was high. In addition, epistemic motivation was strongly positively related to revising, regardless of the level of task reflexivity involved.
- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461
- Type
- a
-
Lou, J.; Fang, Y.; Lim, K.H.; Peng, J.Z.: Contributing high quantity and quality knowledge to online Q&A communities (2013)
0.01
0.005431735 = product of:
0.013579337 = sum of:
0.009632425 = weight(_text_:a in 615) [ClassicSimilarity], result of:
0.009632425 = score(doc=615,freq=16.0), product of:
0.053464882 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046368346 = queryNorm
0.18016359 = fieldWeight in 615, product of:
4.0 = tf(freq=16.0), with freq of:
16.0 = termFreq=16.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0390625 = fieldNorm(doc=615)
0.003946911 = product of:
0.007893822 = sum of:
0.007893822 = weight(_text_:information in 615) [ClassicSimilarity], result of:
0.007893822 = score(doc=615,freq=2.0), product of:
0.08139861 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046368346 = queryNorm
0.09697737 = fieldWeight in 615, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.0390625 = fieldNorm(doc=615)
0.5 = coord(1/2)
0.4 = coord(2/5)
- Abstract
- This study investigates the motivational factors affecting the quantity and quality of voluntary knowledge contribution in online Q&A communities. Although previous studies focus on knowledge contribution quantity, this study regards quantity and quality as two important, yet distinct, aspects of knowledge contribution. Drawing on self-determination theory, this study proposes that five motivational factors, categorized along the extrinsic-intrinsic spectrum of motivation, have differential effects on knowledge contribution quantity versus quality in the context of online Q&A communities. An online survey with 367 participants was conducted in a leading online Q&A community to test the research model. Results show that rewards in the reputation system, learning, knowledge self-efficacy, and enjoy helping stand out as important motivations. Furthermore, rewards in the reputation system, as a manifestation of the external regulation, is more effective in facilitating the knowledge contribution quantity than quality. Knowledge self-efficacy, as a manifestation of intrinsic motivation, is more strongly related to knowledge contribution quality, whereas the other intrinsic motivation, enjoy helping, is more strongly associated with knowledge contribution quantity. Both theoretical and practical implications are discussed.
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.356-371
- Type
- a
