Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps

Elan Sasson; Gilad Ravid; Nava Pliskin

doi:10.1109/APWC-on-CSE.2016.033

Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps

Elan Sasson, Gilad Ravid, Nava Pliskin

Department of Industrial Engineering and Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Despite being important, time and context have yet to be formally incorporated into the process of visually representing the temporal and contextual proximity between keywords in a concept map. In response to the context and time challenges, this study improves automated conventional concept mapping by measuring the temporal and contextual distance between pairs of co-occurring concepts. After generating a conventional concept map, it is temporally and contextually augmented in this work by applying an unsupervised temporal trend detection algorithm and a novel measure of contextual proximity. This proposed approach is demonstrated and validated without loss of generality for a spectrum of information technologies, showing that the resulting assessments of temporal and contextual proximity are highly correlated with subjective assessments of experts. The contribution of this work is emphasized and magnified against the current growing attention to big data analytics in general and to big text-data analytics in particular.

Original language	English
Title of host publication	Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016
Editors	A B M Shawkat Ali, Kabir Mamun, Mohammed Arif Khan
Publisher	Institute of Electrical and Electronics Engineers
Pages	144-152
Number of pages	9
ISBN (Electronic)	9781509057535
DOIs	https://doi.org/10.1109/APWC-on-CSE.2016.033
State	Published - 6 Jun 2017
Event	2016 Asia-Pacific World Congress on Computer Science and Engineering and Asia-Pacific World Congress on Engineering, APWC on CSE/APWCE 2016 - Nadi, Fiji Duration: 4 Dec 2016 → 6 Dec 2016

Conference

Conference	2016 Asia-Pacific World Congress on Computer Science and Engineering and Asia-Pacific World Congress on Engineering, APWC on CSE/APWCE 2016
Country/Territory	Fiji
City	Nadi
Period	4/12/16 → 6/12/16

Keywords

Big text-data analytics
Concept map
Contextual Proximity
Temporal proximity

ASJC Scopus subject areas

Hardware and Architecture
Computer Science (miscellaneous)
Software
Control and Systems Engineering

Access to Document

10.1109/APWC-on-CSE.2016.033

Cite this

Sasson, E., Ravid, G., & Pliskin, N. (2017). Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps. In A. B. M. S. Ali, K. Mamun, & M. A. Khan (Eds.), Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016 (pp. 144-152). Article 7941953 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/APWC-on-CSE.2016.033

Sasson, Elan ; Ravid, Gilad ; Pliskin, Nava. / Measuring Temporal and Contextual Proximity : Big Text-Data Analytics in Concept Maps. Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016. editor / A B M Shawkat Ali ; Kabir Mamun ; Mohammed Arif Khan. Institute of Electrical and Electronics Engineers, 2017. pp. 144-152

@inproceedings{faf4f46eaf084f0a8115c035b9802600,

title = "Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps",

abstract = "Despite being important, time and context have yet to be formally incorporated into the process of visually representing the temporal and contextual proximity between keywords in a concept map. In response to the context and time challenges, this study improves automated conventional concept mapping by measuring the temporal and contextual distance between pairs of co-occurring concepts. After generating a conventional concept map, it is temporally and contextually augmented in this work by applying an unsupervised temporal trend detection algorithm and a novel measure of contextual proximity. This proposed approach is demonstrated and validated without loss of generality for a spectrum of information technologies, showing that the resulting assessments of temporal and contextual proximity are highly correlated with subjective assessments of experts. The contribution of this work is emphasized and magnified against the current growing attention to big data analytics in general and to big text-data analytics in particular.",

keywords = "Big text-data analytics, Concept map, Contextual Proximity, Temporal proximity",

author = "Elan Sasson and Gilad Ravid and Nava Pliskin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 Asia-Pacific World Congress on Computer Science and Engineering and Asia-Pacific World Congress on Engineering, APWC on CSE/APWCE 2016 ; Conference date: 04-12-2016 Through 06-12-2016",

year = "2017",

month = jun,

day = "6",

doi = "10.1109/APWC-on-CSE.2016.033",

language = "English",

pages = "144--152",

editor = "Ali, {A B M Shawkat} and Kabir Mamun and Khan, {Mohammed Arif}",

booktitle = "Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

}

Sasson, E, Ravid, G & Pliskin, N 2017, Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps. in ABMS Ali, K Mamun & MA Khan (eds), Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016., 7941953, Institute of Electrical and Electronics Engineers, pp. 144-152, 2016 Asia-Pacific World Congress on Computer Science and Engineering and Asia-Pacific World Congress on Engineering, APWC on CSE/APWCE 2016, Nadi, Fiji, 4/12/16. https://doi.org/10.1109/APWC-on-CSE.2016.033

Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps. / Sasson, Elan; Ravid, Gilad ; Pliskin, Nava.
Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016. ed. / A B M Shawkat Ali; Kabir Mamun; Mohammed Arif Khan. Institute of Electrical and Electronics Engineers, 2017. p. 144-152 7941953.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Measuring Temporal and Contextual Proximity

T2 - 2016 Asia-Pacific World Congress on Computer Science and Engineering and Asia-Pacific World Congress on Engineering, APWC on CSE/APWCE 2016

AU - Sasson, Elan

AU - Ravid, Gilad

AU - Pliskin, Nava

PY - 2017/6/6

Y1 - 2017/6/6

N2 - Despite being important, time and context have yet to be formally incorporated into the process of visually representing the temporal and contextual proximity between keywords in a concept map. In response to the context and time challenges, this study improves automated conventional concept mapping by measuring the temporal and contextual distance between pairs of co-occurring concepts. After generating a conventional concept map, it is temporally and contextually augmented in this work by applying an unsupervised temporal trend detection algorithm and a novel measure of contextual proximity. This proposed approach is demonstrated and validated without loss of generality for a spectrum of information technologies, showing that the resulting assessments of temporal and contextual proximity are highly correlated with subjective assessments of experts. The contribution of this work is emphasized and magnified against the current growing attention to big data analytics in general and to big text-data analytics in particular.

AB - Despite being important, time and context have yet to be formally incorporated into the process of visually representing the temporal and contextual proximity between keywords in a concept map. In response to the context and time challenges, this study improves automated conventional concept mapping by measuring the temporal and contextual distance between pairs of co-occurring concepts. After generating a conventional concept map, it is temporally and contextually augmented in this work by applying an unsupervised temporal trend detection algorithm and a novel measure of contextual proximity. This proposed approach is demonstrated and validated without loss of generality for a spectrum of information technologies, showing that the resulting assessments of temporal and contextual proximity are highly correlated with subjective assessments of experts. The contribution of this work is emphasized and magnified against the current growing attention to big data analytics in general and to big text-data analytics in particular.

KW - Big text-data analytics

KW - Concept map

KW - Contextual Proximity

KW - Temporal proximity

UR - http://www.scopus.com/inward/record.url?scp=85022071081&partnerID=8YFLogxK

U2 - 10.1109/APWC-on-CSE.2016.033

DO - 10.1109/APWC-on-CSE.2016.033

M3 - Conference contribution

AN - SCOPUS:85022071081

SP - 144

EP - 152

BT - Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016

A2 - Ali, A B M Shawkat

A2 - Mamun, Kabir

A2 - Khan, Mohammed Arif

PB - Institute of Electrical and Electronics Engineers

Y2 - 4 December 2016 through 6 December 2016

ER -

Sasson E, Ravid G , Pliskin N. Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps. In Ali ABMS, Mamun K, Khan MA, editors, Proceedings - Asia-Pacific World Congress on Computer Science and Engineering 2016 and Asia-Pacific World Congress on Engineering 2016, APWC on CSE/APWCE 2016. Institute of Electrical and Electronics Engineers. 2017. p. 144-152. 7941953 doi: 10.1109/APWC-on-CSE.2016.033

Measuring Temporal and Contextual Proximity: Big Text-Data Analytics in Concept Maps

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this