JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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Tianyong Hao and Yingying Qu Fig.2: The Minipar output of the example in XML format level. In each level, incremental part, which is the content extended compared with the previous pattern, is judged if it contains a noun or noun phrase. Only the patterns that fulfil constraints are regarded as qualified structural patterns. On the same example, all the extracted structural patterns and their related concepts are shown in Table 2. TABLE 2: The extracted patterns and their related concepts on the same example Structural patterns Concepts what(gov fin) be(gov fin) [N1](gov be) risk what(gov fin) be(gov fin) [N1](gov be) of(gov N1) plug(gov vpsc) [N2](gov plug) risk; hole what(gov fin) be(gov fin) [N1](gov be) of(gov N1) plug(gov vpsc) [N2](gov plug) in(gov N2) [N3](gov in) risk; hole; heart Frequent Pattern Mining Since a single structural pattern may be specific, to improve the performance of matching and learning, frequent patterns are more appropriate to be used for efficient knowledge acquisition. Apriori, as a classic association rule mining method, is applied to mine possible frequent patterns. It is an algorithm for learning association rules and was designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). Given a set of item sets (for instance, sets of retail transactions, each listing individual items purchased), Apriori algorithm attempts to find subsets which are common to at least a 174 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
Toward Automatic Semantic Annotating and Pattern Mining for Domain Knowledge Acquisition minimum number C of the item sets. Apriori uses a “bottom up” approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. Apriori uses breadth-first search and a tree structure to count candidate item sets efficiently. It generates candidate item sets of length k from the item sets of length k-1. Then, it prunes the candidates which have an infrequent sub pattern. According to the downward closure lemma, the candidate set contains all frequent k-length item sets. After that, it scans the transaction database to determine frequent item sets among the candidates. In this paper, threshold support for a pattern candidate p is defined as the number of pattern candidates that contain p. In frequent pattern learning, those candidates whose occurrences are lower the support threshold are removed. Furthermore, two corollaries are applied to reduce calculation complexity. Let X, Y, Z be any three pattern candidates, and observe that if X ⊆ Y and Y ⊆ Z, then support(X) ≥ support(Y) ≥ support(Z), which leads to the following two corollaries: 1) If Z is frequent, then any subset X or Y is also frequent; 2) If X is not frequent, then any superset Y or Z cannot be frequent. Regarding a structural pattern as an item set, a pattern can be split into several items thus can be sent to Apriori for mining. However, the sequences, as order information between items, need be fully considered. After sequence-based Apriori algorithm, the patterns whose recorded occurrence values larger than a threshold are mined as frequent patterns, which are further used in the next section. SEMANTIC ANNOTATING AND RELATIONS LEARNING Semantic Annotating There are a large amount of nouns and noun phrases acquired from corpus labelling. Regarding them as concepts here, it is an essential step to annotate these concepts for semantic representation. In order to annotate them, semantic labels were used as annotation labels based on WordNet (2012), which provides a large database of English lexical items available online. A semantic label here is defined as [Concept1] \ [Concept2], where these two concepts (Concept1 and Concept2) have a relation with Sub(Concept2, Concept1), which means Concept2 is the sub concept of Concept1. The purpose is to assign a label with a super concept label as constraint to reduce potential ambiguities. In this semantic label definition, concepts can be retrieved and labelled using WordNet. For instance, in the following sentence “What is the exact relationship between pulse rate and systolic pressure?”, “pulse rate” have two senses, and thus the super con cepts are: 1) “vital sign”, “sign”, “evidence”, “information”, and so on; and 2) “rate”, “magnitude relation”, “relation”, and so on. Hence, the semantic labels of the “pulse rate” are tagged as “[sign\ vital sign]” and “[magnitude relation \rate]” finally. The annotated concepts with semantic labels are important sources to enrich or even construct the basis of domain knowledge base. To improve annotation efficiency and label concepts in a complex context, these annotated concepts are represented in the form of semantic bank, which can be transferred into knowledge format directly as described in the following section. The idea of semantic bank is from one of our research work (Hao et al., 2009). The Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 175
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Journal of Science & Technology Jou
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International Advisory Board Adarsh
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ut bovine milk allergy by far is th
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Selected Articles from UPM-Malaysia
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ISSN: 0128-7680 © 2013 Universiti
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Aspect of Fatigue Analysis of Compo
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Application of Anthropometric Dimen
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Different Media Formulation on Bioc
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Heng, S. C., Ibrahim, Z. B., Suleim
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Y. Yusuf, J. M. Juoi, Z. M. Rosli,
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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The Empirical Method Modelling of M
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Assessment of Heavy Metal Pollution
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Physico-Chemical and Electrical Pro
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Norhashila Hashim et al. effectiven
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Statistical Analysis Norhashila Has
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Norhashila Hashim et al. Post-hoc a
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Norhashila Hashim et al. Cubero, S.
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S. Ismail and K. A. Mohd Atan 4 4 p
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S. Ismail and K. A. Mohd Atan The f
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Page 138 and 139: INTRODUCTION Tajau, R. et al. A hig
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Page 150 and 151: CONCLUSION Aji, I. S., Zinudin, E.
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Page 168 and 169: REFERENCES N. Maizatul, I. Norazowa
Page 171 and 172: ISSN: 0128-7680 © 2013 Universiti
Page 173 and 174: CBIR Using Colour and Shape Fused F
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Che Mustapha Yusuf, J., Mohd Su’u
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Che Mustapha Yusuf, J., Mohd Su’u
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Az Azrinudin Alidin and Fabio Crest
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Yogan Jaya Kumar, Naomie Salim, Ahm
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REFERENCES Yogan Jaya Kumar, Naomie
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Hasiah Mohamed@Omar, Azizah Jaafar
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The Role of Similarity Measurement
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TABLE 5: Winners MRS The Role of Si
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REFEREES FOR THE PERTANIKA JOURNAL
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Guidelines for Authors Publication
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table should be prepared on a separ
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The Journal’s review process What
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Usability of Educational Computer G
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