Optimal Classifying and Extracting Fuzzy Relationship from Query Using Text Mining Techniques
Text mining techniques are generally applied for
classifying the text, finding fuzzy relations and structures in data
sets. This research provides plenty text mining capabilities. One
common application is text classification and event extraction,
which encompass deducing specific knowledge concerning incidents
referred to in texts. The main contribution of this paper is the
clarification of a concept graph generation mechanism, which is based
on a text classification and optimal fuzzy relationship extraction.
Furthermore, the work presented in this paper explains the application
of fuzzy relationship extraction and branch and bound (BB) method
to simplify the texts.
[1] S. Vashishtha, and Y. Kumar, ”Efficient Retrieval of Text for Biomedical
Domain using Expectation Maximization Algorithm”. IJCSI International
Journal of Computer Science. Issues, Vol. 8, Issue 6, No 1, 2011.
[2] F. Hogenboom, F. Frasincar, U. Kaymak, and F. Jong F, ”An Overview of
Event Extraction from Text. Proceedings of Detection”. Representation
and Workshop on Detection, Representation, and Exploitation of Events
in the Semantic Web (DeRiVE 2011) at Tenth International Semantic Web
Conference (ISWC 2011), Volume 779, pp. 48–57, CEUR-WS.org, Bonn,
2011.
[3] B. Alex, C. Grover, B. Haddow, M. Kabadjov, E. Klein, M. Matthews, S.
Roebuck, R. Tobin, And X. Wang, ”Assisted Curation: Does Text Mining
Really Help?”. Pacific Symposium on Biocomputing 13, pp. 556–567.
PSB, Island of Hawaii, 2008. [4] V. Kanagavalli, and K. Raja, ”Detecting and resolving spatial ambiguity
in text using named entity extraction and self learning fuzzy logic
techniques”. National Conference on Recent Trends in Data Mining
and Distributed Systems, SBN 978-81-909042-5-4, pp. 71–76. NCT2DS,
2011.
[5] D. MacKinnon, L. Goldberg, G. Clarke, D. Elliot, J. Cheong, A. Lapin,
E. Moe, and J. Krull, ”Mediating Mechanisms in a Program to Reduce
Intentions to Use Anabolic Steroids and Improve Exercise Self-Efficacy
and Dietary Behavior”. Prevention Science, Vol. 2, No. 1, pp. 15–28. PS
Press, 2001.
[6] Y. Garten, ”Text Mining of the Scientific Literature to Identify
Pharmacogenomic Interactions”. Stanford University, 2010.
[7] I Donaldson, J. Martin, B. de Bruijn, C. Wolting, V. Lay, B. Tuekam,
S. Zhang, B. Baskin, G.D. Bader, k. Michalickova, T. Pawson, and
C.W.V. Hogue, ”PreBIND and Textomy - mining the biomedical literature
for protein-protein interactions using a support vector machine”. BMC
Bioinformatics, Vol. 4, pp. 11. Springer, 2003.
[8] N. Karamanis, I. Lewin, R. Seal, R. Drysdale, and E. Briscoe, ”Integrating
natural language processing with FlyBase curation”. In Proceedings of
PSB 2007, pp 245-256. PSB Press, Maui, Hawaii, 2007.
[9] T. Murphy, T. McIntosh, and J. Curran, ”In Australian Language
Technology Workshop”, pp. 59-66. ALTW Press, 2006.
[10] S. Samarawickrama, L. Jayaratne, ”Focused Web Crawling Using
Named Entity Recognition For Narrow Domains”. IJRET, http://www.
ijret.org/ 2012.
[11] R. Lau, D. Song, Y. Li, T. Cheung, and J. Hao, ”Toward a Fuzzy
Domain Ontology Extraction Method for Adaptive e-Learning”. IEEE
Transactions On Knowledge And Data Engineering, VOL. 21, NO. 6,
pp. 800–813. IEEE, 2009.
[12] M. Abulaish, and L. Dey, ”Biological relation extraction and query
answering from MEDLINE abstracts using ontology-based text mining”.
Data & Knowledge Engineering Journal, Volume 61, Issue 2, pp.
228-262, 2007.
[13] A. Prasad, S. Ramakrishna, D. Kumar, and B. Padmaja, ”Extraction of
Radiology Reports using Text mining”. (IJCSE) International Journal
on Computer Science and Engineering, Vol. 02, No. 05, pp. 1558–1562.
IJCSE Press, 2010.
[14] M. Rodrigues, and L. Sacks, ”A Scalable Hierarchical Fuzzy Clustering
Algorithm for Text Mining”. In Proceedings of the 5th International
Conference on Recent Advances in Soft Computing. pp. 269–274.
Nottingham, 2004.
[15] S. Jusoh, and H. Alfawareh H, ”Techniques, Applications and
Challenging Issue in Text Mining”. IJCSI International Journal of
Computer Science Issues, Vol. 9, Issue 6, No 2. IJCSI Press, 2012.
[16] S. Ghosh, S. Roy, and S. Bandyopadhyay, ”A tutorial review on Text
Mining Algorithms”. International Journal of Advanced research in
Computer and Communication Engineering, ISSN 2278-1021, Vol. 1,
Issue 4. IJARCCE Press, 2012.
[17] T. Nogueira, H. Camargo, and S. Rezende, ”Fuzzy-DDE: a fuzzy
method for the extraction of document cluster descriptors”. International
Journal of Computer Information Systems and Industrial Management
Applications, ISSN 2150-7988, Vol. 5. pp. 472–479. IJCISIM Press, 2013.
[18] K. Oh, C. Lim, S. Kim, and H. Choi, ”Research Trend Analysis using
Word Similarities and Clusters”. International Journal of Multimedia and
Ubiquitous Engineering, Vol. 8, No. 1. IJMUE Press, Tasmania, 2013.
[19] N. Uramoto, H. Matsuzawa, T. Nagano, A. Murakami, H. Takeuchi,
and K. Takeda, ”A text-mining system for knowledge discovery from
biomedical documents”. IBM Systems Journal, Vol. 43, No. 3. IEEE Press,
Japan, 2004.
[20] L. Magdalena, M. Ojeda-Aciego, and J. Verdegay (eds), ”Extracting
topics in texts: Towards a fuzzy logic approach”. Proceedings of
IPMU’08. pp. 1733–1740. Torremolinos, 2008.
[21] M. Kathuria, N. Duhan, and C. Nagpal, ”Application Of Fuzzy Logic
In Web Mining Domain: A Survey”. International Journal of Advanced
Research in IT and Engineering. ISSN: 2278-6244, Vol. 1, No. 3.
Tamilnadu, 2012.
[22] T. Martin, and M. Azmi-Murad, ”An Incremental Algorithm to
find Asymmetric Word Similarities for Fuzzy Text Mining”. Soft
Computing as Transdisciplinary Science and Technology Advances in Soft
Computing, Vol. 29. pp. 838–847. Springer, 2005.
[23] A. Kaladevi, S. Padmavathy, and S. Theetcchenya, ”Augmentation
of Knowledge Reuse Employing Fuzzy Ontology Based Approach”.
International Journal of Engineering and Management Research, ISSN:
2250-0758, Vol. 3, Issue 2. pp. 17-21. IJMER Press, India, 2013.
[24] B. Anjali, and G. Bamnote, ”Web Document Clustering Using Fuzzy
Equivalence Relations”. Journal of Emerging Trends in Computing and
Information Sciences. ISSN: 2079-8407, Vol. 2, Special Issue. pp. 22–27.
CIS Journal, 2011.
[25] F. Alshuwaier, W. Almutairi, and A. Areshey, ”Smart Search Tools
Using Named Entity Recognition”. Proceeding in Information Technology
and Applications (ITA). ISBN: 978-1-4799-2876-7. pp. 304–311. IEEE,
Chengdu, 2013.
[26] A. Muhammad, and L. Dey, ”Biological ontology enhancement with
fuzzy relations: a text-mining framework”. ISBN: 0-7695-2415-X. pp.
379–385. IEEE, Canada, 2005.
[27] R. Lau, D. Song, Y. Li, T. Cheung, and X. Jin Hao, ”Towards A
Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning”.
CiteSeerX Scientific Literature Digital Library and Search Engine.
CiteSeerX, 2013.
[28] J. Yen, and R. Langari, ”Fuzzy logic: intelligence, control, and
information”. Prentice-Hall, Inc. 1998.
[29] M. Guelpeli, and A. Garcia, ”An Analysis of Constructed Categories
for Textual Classification Using Fuzzy Similarity and Agglomerative
Hierarchical Methods”. Third International IEEE Conference on Signal
and Image Technologies and Internet-Based System. IEEE, Lisboa
Reitoria, 2008.
[30] D. Georgiou, T. Karakasidis, J. Nieto, and A. Torres A, ”Use of
Fuzzy Clustering Technique and Matrices to Classify Amino Acids and
Its Impact to Chou’s Pseudo Amino Acid Composition”. Journal of
Theoretical Biology, Reference: YJTBI 5356. JTB Press, 2008.
[31] M. Sridharan, ”Fuzzy mathematical model for the analysis of
geomagnetic field data”. The Society of Geomagnetism and Earth,
Planetary and Space Sciences (SGEPSS); The Seismological Society of
Japan; Earth Planets Space, 61, pp. 1169-1177, Japan 2009.
[32] N. Archana, P. Girish, and P. Sandip P, ”Improved Membership Function
for Multiclass Clustering with Fuzzy Rule Based Clustering Approach”.
International Journal of Emerging Trends & Technology in Computer
Science (IJETTCS), Volume 3, Issue 5, 2014.
[33] B. Steven, K. Ewan, and L. Edward, ”Chapter from Natural Language
Processing with Python”. Version 3.0, USA, 2014.
[34] A. Kaladevi, and S. Padmavathy, ”Ontology Extraction for E-Learning
- A Fuzzy Based Approach”. International Conference on Computer
Communication and Informatics (ICCCI -2013), INDIA, 2013.
[35] C. Rakhi, ”Domain Keyword Extraction Technique: A New Weighting
Method Based On Frequency Analysis”. ACER 2013, pp. 109-118, CS &
IT-CSCP, 2013.
[36] B. SankaraSubramanian, R. Vasanth Kumar Mehta, ”Contradiction
Analysis in Text Mining: A Fuzzy Logic Approach”. SCSVMV University,
kanchipuram, INDIA, 2009.
[37] M. Foley, ”The Application of Fuzzy Logic in Determining Linguistic
Rules and Associative Membership Functions for the Control of
a Manufacturing Process”. Masters Dissertation. Dublin Institute of
Technology, 2011.
[38] F. Bertran, N. Clara, and J. Ferrer, ”Extending The Roughness Of The
Data Via Transitive Closures Of Similarity Indexes”. Vol. XII, No. 2, pp.
75-84, 2007.
[1] S. Vashishtha, and Y. Kumar, ”Efficient Retrieval of Text for Biomedical
Domain using Expectation Maximization Algorithm”. IJCSI International
Journal of Computer Science. Issues, Vol. 8, Issue 6, No 1, 2011.
[2] F. Hogenboom, F. Frasincar, U. Kaymak, and F. Jong F, ”An Overview of
Event Extraction from Text. Proceedings of Detection”. Representation
and Workshop on Detection, Representation, and Exploitation of Events
in the Semantic Web (DeRiVE 2011) at Tenth International Semantic Web
Conference (ISWC 2011), Volume 779, pp. 48–57, CEUR-WS.org, Bonn,
2011.
[3] B. Alex, C. Grover, B. Haddow, M. Kabadjov, E. Klein, M. Matthews, S.
Roebuck, R. Tobin, And X. Wang, ”Assisted Curation: Does Text Mining
Really Help?”. Pacific Symposium on Biocomputing 13, pp. 556–567.
PSB, Island of Hawaii, 2008. [4] V. Kanagavalli, and K. Raja, ”Detecting and resolving spatial ambiguity
in text using named entity extraction and self learning fuzzy logic
techniques”. National Conference on Recent Trends in Data Mining
and Distributed Systems, SBN 978-81-909042-5-4, pp. 71–76. NCT2DS,
2011.
[5] D. MacKinnon, L. Goldberg, G. Clarke, D. Elliot, J. Cheong, A. Lapin,
E. Moe, and J. Krull, ”Mediating Mechanisms in a Program to Reduce
Intentions to Use Anabolic Steroids and Improve Exercise Self-Efficacy
and Dietary Behavior”. Prevention Science, Vol. 2, No. 1, pp. 15–28. PS
Press, 2001.
[6] Y. Garten, ”Text Mining of the Scientific Literature to Identify
Pharmacogenomic Interactions”. Stanford University, 2010.
[7] I Donaldson, J. Martin, B. de Bruijn, C. Wolting, V. Lay, B. Tuekam,
S. Zhang, B. Baskin, G.D. Bader, k. Michalickova, T. Pawson, and
C.W.V. Hogue, ”PreBIND and Textomy - mining the biomedical literature
for protein-protein interactions using a support vector machine”. BMC
Bioinformatics, Vol. 4, pp. 11. Springer, 2003.
[8] N. Karamanis, I. Lewin, R. Seal, R. Drysdale, and E. Briscoe, ”Integrating
natural language processing with FlyBase curation”. In Proceedings of
PSB 2007, pp 245-256. PSB Press, Maui, Hawaii, 2007.
[9] T. Murphy, T. McIntosh, and J. Curran, ”In Australian Language
Technology Workshop”, pp. 59-66. ALTW Press, 2006.
[10] S. Samarawickrama, L. Jayaratne, ”Focused Web Crawling Using
Named Entity Recognition For Narrow Domains”. IJRET, http://www.
ijret.org/ 2012.
[11] R. Lau, D. Song, Y. Li, T. Cheung, and J. Hao, ”Toward a Fuzzy
Domain Ontology Extraction Method for Adaptive e-Learning”. IEEE
Transactions On Knowledge And Data Engineering, VOL. 21, NO. 6,
pp. 800–813. IEEE, 2009.
[12] M. Abulaish, and L. Dey, ”Biological relation extraction and query
answering from MEDLINE abstracts using ontology-based text mining”.
Data & Knowledge Engineering Journal, Volume 61, Issue 2, pp.
228-262, 2007.
[13] A. Prasad, S. Ramakrishna, D. Kumar, and B. Padmaja, ”Extraction of
Radiology Reports using Text mining”. (IJCSE) International Journal
on Computer Science and Engineering, Vol. 02, No. 05, pp. 1558–1562.
IJCSE Press, 2010.
[14] M. Rodrigues, and L. Sacks, ”A Scalable Hierarchical Fuzzy Clustering
Algorithm for Text Mining”. In Proceedings of the 5th International
Conference on Recent Advances in Soft Computing. pp. 269–274.
Nottingham, 2004.
[15] S. Jusoh, and H. Alfawareh H, ”Techniques, Applications and
Challenging Issue in Text Mining”. IJCSI International Journal of
Computer Science Issues, Vol. 9, Issue 6, No 2. IJCSI Press, 2012.
[16] S. Ghosh, S. Roy, and S. Bandyopadhyay, ”A tutorial review on Text
Mining Algorithms”. International Journal of Advanced research in
Computer and Communication Engineering, ISSN 2278-1021, Vol. 1,
Issue 4. IJARCCE Press, 2012.
[17] T. Nogueira, H. Camargo, and S. Rezende, ”Fuzzy-DDE: a fuzzy
method for the extraction of document cluster descriptors”. International
Journal of Computer Information Systems and Industrial Management
Applications, ISSN 2150-7988, Vol. 5. pp. 472–479. IJCISIM Press, 2013.
[18] K. Oh, C. Lim, S. Kim, and H. Choi, ”Research Trend Analysis using
Word Similarities and Clusters”. International Journal of Multimedia and
Ubiquitous Engineering, Vol. 8, No. 1. IJMUE Press, Tasmania, 2013.
[19] N. Uramoto, H. Matsuzawa, T. Nagano, A. Murakami, H. Takeuchi,
and K. Takeda, ”A text-mining system for knowledge discovery from
biomedical documents”. IBM Systems Journal, Vol. 43, No. 3. IEEE Press,
Japan, 2004.
[20] L. Magdalena, M. Ojeda-Aciego, and J. Verdegay (eds), ”Extracting
topics in texts: Towards a fuzzy logic approach”. Proceedings of
IPMU’08. pp. 1733–1740. Torremolinos, 2008.
[21] M. Kathuria, N. Duhan, and C. Nagpal, ”Application Of Fuzzy Logic
In Web Mining Domain: A Survey”. International Journal of Advanced
Research in IT and Engineering. ISSN: 2278-6244, Vol. 1, No. 3.
Tamilnadu, 2012.
[22] T. Martin, and M. Azmi-Murad, ”An Incremental Algorithm to
find Asymmetric Word Similarities for Fuzzy Text Mining”. Soft
Computing as Transdisciplinary Science and Technology Advances in Soft
Computing, Vol. 29. pp. 838–847. Springer, 2005.
[23] A. Kaladevi, S. Padmavathy, and S. Theetcchenya, ”Augmentation
of Knowledge Reuse Employing Fuzzy Ontology Based Approach”.
International Journal of Engineering and Management Research, ISSN:
2250-0758, Vol. 3, Issue 2. pp. 17-21. IJMER Press, India, 2013.
[24] B. Anjali, and G. Bamnote, ”Web Document Clustering Using Fuzzy
Equivalence Relations”. Journal of Emerging Trends in Computing and
Information Sciences. ISSN: 2079-8407, Vol. 2, Special Issue. pp. 22–27.
CIS Journal, 2011.
[25] F. Alshuwaier, W. Almutairi, and A. Areshey, ”Smart Search Tools
Using Named Entity Recognition”. Proceeding in Information Technology
and Applications (ITA). ISBN: 978-1-4799-2876-7. pp. 304–311. IEEE,
Chengdu, 2013.
[26] A. Muhammad, and L. Dey, ”Biological ontology enhancement with
fuzzy relations: a text-mining framework”. ISBN: 0-7695-2415-X. pp.
379–385. IEEE, Canada, 2005.
[27] R. Lau, D. Song, Y. Li, T. Cheung, and X. Jin Hao, ”Towards A
Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning”.
CiteSeerX Scientific Literature Digital Library and Search Engine.
CiteSeerX, 2013.
[28] J. Yen, and R. Langari, ”Fuzzy logic: intelligence, control, and
information”. Prentice-Hall, Inc. 1998.
[29] M. Guelpeli, and A. Garcia, ”An Analysis of Constructed Categories
for Textual Classification Using Fuzzy Similarity and Agglomerative
Hierarchical Methods”. Third International IEEE Conference on Signal
and Image Technologies and Internet-Based System. IEEE, Lisboa
Reitoria, 2008.
[30] D. Georgiou, T. Karakasidis, J. Nieto, and A. Torres A, ”Use of
Fuzzy Clustering Technique and Matrices to Classify Amino Acids and
Its Impact to Chou’s Pseudo Amino Acid Composition”. Journal of
Theoretical Biology, Reference: YJTBI 5356. JTB Press, 2008.
[31] M. Sridharan, ”Fuzzy mathematical model for the analysis of
geomagnetic field data”. The Society of Geomagnetism and Earth,
Planetary and Space Sciences (SGEPSS); The Seismological Society of
Japan; Earth Planets Space, 61, pp. 1169-1177, Japan 2009.
[32] N. Archana, P. Girish, and P. Sandip P, ”Improved Membership Function
for Multiclass Clustering with Fuzzy Rule Based Clustering Approach”.
International Journal of Emerging Trends & Technology in Computer
Science (IJETTCS), Volume 3, Issue 5, 2014.
[33] B. Steven, K. Ewan, and L. Edward, ”Chapter from Natural Language
Processing with Python”. Version 3.0, USA, 2014.
[34] A. Kaladevi, and S. Padmavathy, ”Ontology Extraction for E-Learning
- A Fuzzy Based Approach”. International Conference on Computer
Communication and Informatics (ICCCI -2013), INDIA, 2013.
[35] C. Rakhi, ”Domain Keyword Extraction Technique: A New Weighting
Method Based On Frequency Analysis”. ACER 2013, pp. 109-118, CS &
IT-CSCP, 2013.
[36] B. SankaraSubramanian, R. Vasanth Kumar Mehta, ”Contradiction
Analysis in Text Mining: A Fuzzy Logic Approach”. SCSVMV University,
kanchipuram, INDIA, 2009.
[37] M. Foley, ”The Application of Fuzzy Logic in Determining Linguistic
Rules and Associative Membership Functions for the Control of
a Manufacturing Process”. Masters Dissertation. Dublin Institute of
Technology, 2011.
[38] F. Bertran, N. Clara, and J. Ferrer, ”Extending The Roughness Of The
Data Via Transitive Closures Of Similarity Indexes”. Vol. XII, No. 2, pp.
75-84, 2007.
@article{"International Journal of Information, Control and Computer Sciences:68987", author = "Faisal Alshuwaier and Ali Areshey", title = "Optimal Classifying and Extracting Fuzzy Relationship from Query Using Text Mining Techniques", abstract = "Text mining techniques are generally applied for
classifying the text, finding fuzzy relations and structures in data
sets. This research provides plenty text mining capabilities. One
common application is text classification and event extraction,
which encompass deducing specific knowledge concerning incidents
referred to in texts. The main contribution of this paper is the
clarification of a concept graph generation mechanism, which is based
on a text classification and optimal fuzzy relationship extraction.
Furthermore, the work presented in this paper explains the application
of fuzzy relationship extraction and branch and bound (BB) method
to simplify the texts.
", keywords = "Extraction, Max-Prod, Fuzzy Relations, Text Mining,
Memberships, Classification.", volume = "9", number = "1", pages = "260-8", }
