Name
Title | ||
|---|---|---|
Creation of knowledge-added concept maps: time augmention via pairwise temporal analysis. |
Abstract | ||
|---|---|---|
Purpose - Although acknowledged as a principal dimension in the context of text mining, time has yet to be formally incorporated into the process of visually representing the relationships between keywords in a knowledge domain. This paper aims to develop and validate the feasibility of adding temporal knowledge to a concept map via pair-wise temporal analysis (PTA). Design/methodology/approach - The paper presents a temporal trend detection algorithm - vector space model - designed to use objective quantitative pair-wise temporal operators to automatically detect co-occurring hot concepts. This PTA approach is demonstrated and validated without loss of generality for a spectrum of information technologies. Findings - The rigorous validation study shows that the resulting temporal assessments are highly correlated with subjective assessments of experts (n = 136), exhibiting substantial reliability-of-agreementmeasures and average predictive validity above 85 per cent. Practical implications - Using massive amounts of textual documents available on the Web to first generate a concept map and then add temporal knowledge, the contribution of this work is emphasized and magnified against the current growing attention to big data analytics. Originality/value - This paper proposes a novel knowledge discovery method to improve a text-based concept map (i.e. semantic graph) via detection and representation of temporal relationships. The originality and value of the proposed method is highlighted in comparison to other knowledge discovery methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1108/JKM-07-2016-0279 | JOURNAL OF KNOWLEDGE MANAGEMENT |
Keywords | Field | DocType |
Pair-wise temporal analysis (PTA),Technology assessment,Temporal trend detection,Time-augmented concept map,Vector space model (VSM) | Data mining,Concept map,Pairwise comparison,Computer science,Information technology,Knowledge management,Originality,Operator (computer programming),Knowledge extraction,Vector space model,Big data | Journal |
Volume | Issue | ISSN |
21.0 | SP1.0 | 1367-3270 |
Citations | PageRank | References |
1 | 0.36 | 51 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elan Sasson | 1 | 8 | 1.90 |
| Gilad Ravid | 2 | 506 | 46.43 |
| Nava Pliskin | 3 | 399 | 51.92 |