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Second 2012 (vol. 5 no. 2)
pp. 117-129
ASCII Text
x 
 
A. Poulovassilis, P. Selmer, P. T. Wood, "Flexible Querying of Lifelong Learner Metadata," IEEE Transactions on Learning Technologies, vol. 5, no. 2, pp. 117-129, Second, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TLT.2011.38,
author = {A. Poulovassilis and P. Selmer and P. T. Wood},
title = {Flexible Querying of Lifelong Learner Metadata},
journal ={IEEE Transactions on Learning Technologies},
volume = {5},
number = {2},
issn = {1939-1382},
year = {2012},
pages = {117-129},
doi = {http://doi.ieeecomputersociety.org/10.1109/TLT.2011.38},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Learning Technologies
TI - Flexible Querying of Lifelong Learner Metadata
IS - 2
SN - 1939-1382
SP117
EP129
EPD - 117-129
A1 - A. Poulovassilis,
A1 - P. Selmer,
A1 - P. T. Wood,
PY - 2012
KW - Measurement
KW - Educational institutions
KW - Approximation methods
KW - Prototypes
KW - Engineering profession
KW - Information systems
KW - Resource description framework
KW - timelines.
KW - Lifelong learning
KW - careers guidance
KW - semantic web
KW - flexible querying
VL - 5
JA - IEEE Transactions on Learning Technologies
ER -
 
A. Poulovassilis, London Knowledge Lab., Birkbeck, Univ. of London, London, UK
P. Selmer, London Knowledge Lab., Birkbeck, Univ. of London, London, UK
P. T. Wood, Dept. of Comput. Sci. & Inf. Syst., Birkbeck, Univ. of London, London, UK
This paper discusses the provision of flexible querying facilities over heterogeneous data arising from lifelong learners' educational and work experiences. A key aim of such querying facilities is to allow learners to identify possible choices for their future learning and professional development by seeing what others have done. We motivate and describe the development of a prototype system, called ApproxRelax, that provides users with a graphical facility for incrementally constructing their queries and that supports both query approximation and query relaxation, thus allowing for flexible matching of users' queries against the data provided by other learners. We show how the system is able to return results in ranked order of their “distance” from the user's query. Our approach is novel both in its aim of supporting lifelong learners in reflecting on their learning and career choices, and also in its technical foundations that combine for the first time query approximation and query relaxation techniques for querying semistructured data.

[1] A.V. Aho, J.E. Hopcroft, and J.D. Ullman, The Design and Analysis of Computer Algorithms. Addison-Wesley, 1974.
[2] D. Calvanese, G.D. Giacomo, M. Lenzerini, and M.Y. Vardi, "Containment of Conjunctive Regular Path Queries with Inverse," Proc. Seventh Int'l Conf. Principles of Knowledge Representation and Reasoning, pp. 176-185, 2000.
[3] L. Cych, "'Social Networks' in Emerging Technologies for Learning," Coventry, Becta, pp. 32-40, 2006.
[4] S. de Freitas, I. Harrison, G. Magoulas, A. Mee, F. Mohamad, M. Oliver, G. Papamarkos, and A. Poulovassilis, "The Development of a System for Supporting the Lifelong Learner," British J. Educational Technology, vol. 37, no. 6, pp. 867-880, 2006.
[5] P. Dolog, H. Stuckenschmidt, H. Wache, and J. Diederich, "Relaxing RDF Queries Based on User and Domain Preferences," J. Intelligent Information Systems, vol. 33, no. 3, pp. 239-260, 2009.
[6] G. Grahne and A. Thomo, "Regular Path Queries under Approximate Semantics," Annals Math. Artificial Intelligence, vol. 46, nos. 1/2, pp. 165-190, 2006.
[7] H. Huang, C. Liu, and X. Zhou, "Computing Relaxed Answers on RDF Databases," Proc. Ninth Int'l Conf. Web Information Systems Eng., pp. 163-175, 2008.
[8] C.A. Hurtado, A. Poulovassilis, and P.T. Wood, "Query Relaxation in RDF," J. Data Semantics X, pp. 31-61, 2008.
[9] C.A. Hurtado, A. Poulovassilis, and P.T. Wood, "Ranking Approximate Answers to Semantic Web Queries," Proc. Sixth European Semantic Web Conf., pp. 263-277, 2009.
[10] Y. Kanza and Y. Sagiv, "Flexible Queries over Semistructured Data," Proc. 20th ACM Symp. Principles of Databases Systems, pp. 40-51, 2001.
[11] C. Kiefer, A. Bernstein, and M. Stocker, "The Fundamentals of iSPARQL: A Virtual Triple Approach for Similarity-Based Semantic Web Tasks," Proc. Sixth Int'l Semantic Web Conf., pp. 295-309, 2007.
[12] R. Koper, B. Giesbers, P. van Rosmalen, P. Sloep, J. van Bruggen, C. Tattersall, H. Vogten, and F. Brouns, "A Design Model for Lifelong Learning Networks," Interactive Learning Environments, vol. 13, nos. 1/2, pp. 71-92, 2005.
[13] R. Koper and C. Tattersall, "New Directions for Lifelong Learning Using Network Technologies," British J. Educational Technology, vol. 35, no. 6, pp. 689-700, 2004.
[14] C. Liu, J. Li, J.X. Yu, and R. Zhou, "Adaptive Relaxation for Querying Heterogeneous XML Data Sources," Information Systems, vol. 35, no. 6, pp. 688-707, 2010.
[15] F. Mandreoli, R. Martoglia, G. Villani, and W. Penzo, "Flexible Query Answering on Graph-Modeled Data," Proc. 12th Int'l Conf. Extending Database Technology, pp. 216-227, 2009.
[16] A. Poulovassilis and P.T. Wood, "Combining Approximation and Relaxation in Semantic Web Path Queries," Proc. Ninth Int'l Semantic Web Conf., pp. 631-646, 2010.
[17] D. Smith, R. Lilley, I. Marris, and I. Krechowiecka, "A Systematic Review of Research (1998-2004) into the Impact of Career Education and Guidance during Key Stage 4 on Young People's Transition into Post-16 Opportunities," Social Science Research Unit, Inst. of Education, 2005.
[18] Y. Tian and J.M. Patel, "Tale: A tool for Approximate Large Graph Matching," Proc. 24th Int'l Conf. Data Eng., pp. 963-972, 2008.
[19] J.D. Ullman, Principles of Database and Knowledge-Base Systems. Computer Science Press, 1989.
[20] N. van Labeke, G.D. Magoulas, and A. Poulovassilis, "Searching for "People Like Me" in a Lifelong Learning System," Proc. Fourth European Conf. Technology Enhanced Learning, pp. 106-111, 2009.
[21] N. van Labeke, G.D. Magoulas, and A. Poulovassilis, "Personalised Search over Lifelong Learner's Timelines Using String Similarity Measures," Technical Report BBKCS-11-01, Birkbeck, http://www.dcs.bbk.ac.uk/research/techreps/ 2011bbkcs-11-01.pdf, 2011.
[22] N. van Labeke, A. Poulovassilis, and G.D. Magoulas, "Using Similarity Metrics for Matching Lifelong Learners," Proc. Ninth Int'l Conf. Intelligent Tutoring Systems, pp. 142-151, 2008.
[23] C. Veloutsou, J. Lewis, and R. Paton, "University Selection: Information Requirements and Importance," Int'l J. Educational Management, vol. 18, no. 3, pp. 160-171, 2004.

Index Terms:
Measurement,Educational institutions,Approximation methods,Prototypes,Engineering profession,Information systems,Resource description framework,timelines.,Lifelong learning,careers guidance,semantic web,flexible querying
Citation:
A. Poulovassilis, P. Selmer, P. T. Wood, "Flexible Querying of Lifelong Learner Metadata," IEEE Transactions on Learning Technologies, vol. 5, no. 2, pp. 117-129, Second 2012, doi:10.1109/TLT.2011.38
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