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

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Tianyong Hao and Yingying Qu experiment showed that the proposed method could effectively extract knowledge by using the proposed semantic annotating and pattern mining method. With more training sentences involved, the number of knowledge pieces extracted from domain dataset could dramatically increase so as to enrich or even build a new domain knowledge base. TABLE 4: Extracted concepts and relations based on 100 training data Patterns extracted Sentences matched Concepts extracted Relations extracted 272 1,297 1,929 632 With relation learning based on the training data, the annotated relations and semantic bank are potentially desirable to be added into knowledge base. To represent them in a standard knowledge format, two formal knowledge representations - Resource Description Framework (RDF) and Web Ontology Language (OWL) - are applied. RDF is a family of World Wide Web Consortium (W3C) specifications, which were originally designed as a metadata model. It has come to be used as a general method for conceptual description or modelling of information. OWL is a family of knowledge representation languages for authoring ontologies, and is endorsed by W3C (Smith et al., 2004). In this study, the OWL Full was used as our knowledge representation method since it provides compatibility with RDF Schema. The annotated data, either in semantic bank or other relation representation ways, need to be transformed into OWL format. Therefore, a set of knowledge transformation rules (KTR) was defined for this purpose. KTR is an intermediate description logic language, which can be transformed into first-order logic. The involved semantic labels are converted using the following rule and the SubClassOf relation is then represented in OWL, as follows: KTR ∀x, there exist [ x / concept ] →SubClassOf ( concept , x) 1 1 OWL format In the same way, the unit annotation in semantic bank can be converted by the following rule into OWL. It is important to mention that the relation inside a semantic label is already converted using the previous rule. Therefore, the following rule only considers the sub-class outside the semantic labels. KTR ∀y, there exist [ y HAVING concept / concept ] → SubClassOf ( y, concept ) 1 2 OWL format 180 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 2
Toward Automatic Semantic Annotating and Pattern Mining for Domain Knowledge Acquisition The context annotation in semantic bank is more complicated since the words inside a sentence are concordant. Thus, “restriction” was used to represent and the corresponding KTR was defined as follows: KTR OWL format ∀∀ x y, there exist [ x HAVING concept / concept ] WITH [ y HAVING concept / concept ] 3 4 1 2 → SubClassOf ( x, concept ) with constraint : SubClassOf ( y, concept ) ... 2 4 Similarly, the relations can also be transformed into knowledge representation format. In this way, the extracted knowledge can be represented in a standard way automatically and can be applied to enrich domain knowledge base directly. CONCLUSION AND FUTURE WORK A novel method for automatic knowledge acquisition from domain texts is proposed in this paper. Taking “heart disperses” from Yahoo! Answers as the source domain corpus, this method makes use of Minipar to label sentences and extract structural patterns. A semantic bank is further proposed to annotate concepts regarding to sentence context based on WordNet to enhance the semantic representation. With regard to the pattern matching, the relations extracted from training data were applied to the previously extracted concepts. Thus, the concepts, annotated labels, and relations can be converted by knowledge transformation rules to enrich domain knowledge base automatically. As for further development, more experiments with larger dataset and evaluation/comparison of the performances of knowledge extraction will be taken into consideration in the future. REFERENCES Berwick, R. C., Abney, S. P., & Tenny, C. (1991). Principle-Based Parsing: Computation and Psycholinguistics. Kluwer Academic Publishers. Campbell, K., Das, A., & Musen, M. (1994). A Logical Foundation for Representation of Clinical Data. Journal of the American Medical Informatics Association, 1(3), 218-232. Cao, C. G. (2001). Medical Knowledge Acquisition From Encyclopedic Texts. LNCS, 2101, 268-271. Berlin. Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 181
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Journal of Science & Technology Jou
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International Advisory Board Adarsh
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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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Assessment of Heavy Metal Pollution
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TABLE 1: (continued) Physico-Chemic
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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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Lemma 1.1 The equation S. Ismail an
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CONCLUSION S. Ismail and K. A. Mohd
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INTRODUCTION Tajau, R. et al. A hig
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Az Azrinudin Alidin and Fabio Crest
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Yogan Jaya Kumar, Naomie Salim, Ahm
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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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Guidelines for Authors Publication
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table should be prepared on a separ
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Usability of Educational Computer G
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JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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