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Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining

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@article{IJASEIT1461,
   author = {Hasni Hassan and Noraida Ali and Aznida Hayati Zakaria and Mohd Isa Awang and Abd Rasid Mamat},
   title = {Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {4},
   year = {2017},
   pages = {1413--1418},
   keywords = {formal concept analysis; graph theory; text mining; adjacency matrix},
   abstract = {The growth of the semantic web has fueled the need to search for information based on the understanding of the intent of the searcher, coupled with the contextual meaning of the keywords supplied by the searcher. The common solution to enhance the searching process includes the deployment of formal concept analysis (FCA) theory to extract concepts from a set of text with the use of corresponding domain ontology. However, creating a domain ontology or cross-platform ontology is a tedious and time consuming process that requires validation from domain experts. Therefore, this study proposed an alternative solution called Lattice Mining (LM) that utilizes FCA theory and graph theory. This is because the process of matching a query to related documents is similar to the process of graph matching if both the query and the documents are represented using graphs. This study adopted the idea of FCA in the determination of the concepts based on texts and deployed the lattice diagrams obtained from an FCA tool for further analysis using graph theory. The LM technique employed in this study utilized the adjacency matrices obtained from the lattice outputs and performed a distance measure technique to calculate the similarity between two graphs. The process was realized successively via the implementation of three algorithms called the Relatedness Algorithm (RA), the Adjacency Matrix Algorithm (AMA) and the Concept-Based Lattice Mining (CBLM) Algorithm. A similarity measure between FCA output lattices yielded promising results based on the ranking of the trace values from the matrices. Recognizing the potential of this method, future work includes refinement in the steps of the CBLM algorithm for a more efficient implementation of the process.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1461},
   doi = {10.18517/ijaseit.7.4.1461}
}

EndNote

%A Hassan, Hasni
%A Ali, Noraida
%A Zakaria, Aznida Hayati
%A Awang, Mohd Isa
%A Mamat, Abd Rasid
%D 2017
%T Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining
%B 2017
%9 formal concept analysis; graph theory; text mining; adjacency matrix
%! Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining
%K formal concept analysis; graph theory; text mining; adjacency matrix
%X The growth of the semantic web has fueled the need to search for information based on the understanding of the intent of the searcher, coupled with the contextual meaning of the keywords supplied by the searcher. The common solution to enhance the searching process includes the deployment of formal concept analysis (FCA) theory to extract concepts from a set of text with the use of corresponding domain ontology. However, creating a domain ontology or cross-platform ontology is a tedious and time consuming process that requires validation from domain experts. Therefore, this study proposed an alternative solution called Lattice Mining (LM) that utilizes FCA theory and graph theory. This is because the process of matching a query to related documents is similar to the process of graph matching if both the query and the documents are represented using graphs. This study adopted the idea of FCA in the determination of the concepts based on texts and deployed the lattice diagrams obtained from an FCA tool for further analysis using graph theory. The LM technique employed in this study utilized the adjacency matrices obtained from the lattice outputs and performed a distance measure technique to calculate the similarity between two graphs. The process was realized successively via the implementation of three algorithms called the Relatedness Algorithm (RA), the Adjacency Matrix Algorithm (AMA) and the Concept-Based Lattice Mining (CBLM) Algorithm. A similarity measure between FCA output lattices yielded promising results based on the ranking of the trace values from the matrices. Recognizing the potential of this method, future work includes refinement in the steps of the CBLM algorithm for a more efficient implementation of the process.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1461
%R doi:10.18517/ijaseit.7.4.1461
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 4
%@ 2088-5334

IEEE

Hasni Hassan,Noraida Ali,Aznida Hayati Zakaria,Mohd Isa Awang and Abd Rasid Mamat,"Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4, pp. 1413-1418, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4.1461.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Hassan, Hasni
AU  - Ali, Noraida
AU  - Zakaria, Aznida Hayati
AU  - Awang, Mohd Isa
AU  - Mamat, Abd Rasid
PY  - 2017
TI  - Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4
Y2  - 2017
SP  - 1413
EP  - 1418
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - formal concept analysis; graph theory; text mining; adjacency matrix
N2  - The growth of the semantic web has fueled the need to search for information based on the understanding of the intent of the searcher, coupled with the contextual meaning of the keywords supplied by the searcher. The common solution to enhance the searching process includes the deployment of formal concept analysis (FCA) theory to extract concepts from a set of text with the use of corresponding domain ontology. However, creating a domain ontology or cross-platform ontology is a tedious and time consuming process that requires validation from domain experts. Therefore, this study proposed an alternative solution called Lattice Mining (LM) that utilizes FCA theory and graph theory. This is because the process of matching a query to related documents is similar to the process of graph matching if both the query and the documents are represented using graphs. This study adopted the idea of FCA in the determination of the concepts based on texts and deployed the lattice diagrams obtained from an FCA tool for further analysis using graph theory. The LM technique employed in this study utilized the adjacency matrices obtained from the lattice outputs and performed a distance measure technique to calculate the similarity between two graphs. The process was realized successively via the implementation of three algorithms called the Relatedness Algorithm (RA), the Adjacency Matrix Algorithm (AMA) and the Concept-Based Lattice Mining (CBLM) Algorithm. A similarity measure between FCA output lattices yielded promising results based on the ranking of the trace values from the matrices. Recognizing the potential of this method, future work includes refinement in the steps of the CBLM algorithm for a more efficient implementation of the process.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1461
DO  - 10.18517/ijaseit.7.4.1461

RefWorks

RT Journal Article
ID 1461
A1 Hassan, Hasni
A1 Ali, Noraida
A1 Zakaria, Aznida Hayati
A1 Awang, Mohd Isa
A1 Mamat, Abd Rasid
T1 Graph Theoretic Lattice Mining Based on Formal Concept Analysis (FCA) Theory for Text Mining
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 4
YR 2017
SP 1413
OP 1418
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 formal concept analysis; graph theory; text mining; adjacency matrix
AB The growth of the semantic web has fueled the need to search for information based on the understanding of the intent of the searcher, coupled with the contextual meaning of the keywords supplied by the searcher. The common solution to enhance the searching process includes the deployment of formal concept analysis (FCA) theory to extract concepts from a set of text with the use of corresponding domain ontology. However, creating a domain ontology or cross-platform ontology is a tedious and time consuming process that requires validation from domain experts. Therefore, this study proposed an alternative solution called Lattice Mining (LM) that utilizes FCA theory and graph theory. This is because the process of matching a query to related documents is similar to the process of graph matching if both the query and the documents are represented using graphs. This study adopted the idea of FCA in the determination of the concepts based on texts and deployed the lattice diagrams obtained from an FCA tool for further analysis using graph theory. The LM technique employed in this study utilized the adjacency matrices obtained from the lattice outputs and performed a distance measure technique to calculate the similarity between two graphs. The process was realized successively via the implementation of three algorithms called the Relatedness Algorithm (RA), the Adjacency Matrix Algorithm (AMA) and the Concept-Based Lattice Mining (CBLM) Algorithm. A similarity measure between FCA output lattices yielded promising results based on the ranking of the trace values from the matrices. Recognizing the potential of this method, future work includes refinement in the steps of the CBLM algorithm for a more efficient implementation of the process.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1461
DO  - 10.18517/ijaseit.7.4.1461