臺灣博碩士論文加值系統

近年來，本體論(Ontology)的應用在網際網路上變成一個熱門的研究議題。由於本體論具有分享與再利用特定領域知識的能力，可作為人類與應用系統之間溝通的管道，因此本體論應該要能隨著資訊的快速變動而更新，不過隨著資訊科技的發展，新的知識不斷被提出與發現，已建構好的本體論可能已經不符合使用者的需求。因此本體論的更新有其必要性。有鑑於此，本研究提出一個本體論更新的方法，從網際網路中收集相關領域的網頁，從中找出與領域本體論意思相符的詞彙予以加入。首先將所收集的網頁傳送至 CKIP 線上斷詞服務，再利用 POS (Part-of-speech) 標籤屬性取出名詞。接著建立詞-文件矩陣，以 TF-IDF 公式來計算每個詞彙的權重，刪去權重較低的詞彙後，再將此矩陣經由潛在語意分析 (Latent Semantic Analysis, LSA) 來強化每個詞彙的潛在語意特徵，最後產生 LSA 矩陣，我們將 LSA 矩陣轉換到一高維度空間，利用 Hamming distance function 計算詞彙與本體論概念的語意相似度，得到與領域本體論概念語意最相似的新詞彙，並找出新詞彙在本體論中適當的位置插入，完成本體論的更新。根據實驗結果，我們提出的方法具有73%的準確性。藉由這樣的方式，相信可以有效解決人工建構、更新本體論必須耗費的大量時間與人力的缺點。

In recent years, ontology application has become one of the most popular research topics on the Internet. Many researchers use domain ontology to facilitate knowledge sharing and reusing. However, the specification of the ontology is still incompletely now; due to no standard expression and creating method exist for the ontology. The information is changed fast on the Internet, so the knowledge on the ontology often can not really meet the user’s requirement. Therefore, how to create a renewable ontology is necessary. In this paper, we will propose an ontology renewable method which can insert new keywords into the corresponding constructed ontology. The novel method uses TF-IDF (Term Frequency – Inverse Document Frequency) and LSA (Latent Semantic Analysis) to strengthen the semantic characteristic of keywords and transforms the LSA matrix to a high dimensional space based on collected web page. The similar values between keywords and concepts are gotten by comparing the high dimension keyword to the corresponding constructed ontology concepts based on group center similarity. The keyword with the least value of similarity will be inserted into the offspring of the concept on the domain ontology and become the domain concept. The primary experiment results indicate the method has 73% accuracy.

Table of Contents
中文摘要 I
Abstract II
誌謝 III
Table of Contents V
List of Tables VII
List of Figures VIII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Objective 3
1.4 The framework of thesis 3
Chapter 2 Literature Review 4
2.1 Semantic Web 4
2.2 Ontology 6
2.3 Related technologies 7
2.3.1 TF-IDF 8
2.3.2 Latent Semantic Analysis, LSA 9
Chapter 3 Method 11
3.1 Preprocessing web page 11
3.2 Calculating the TF-IDF of keywords and concepts 13
3.3 Finding the latent semantic of the keywords 13
3.4 Operating of ANCOGSD algorithm 14
Chapter 4 Experimental and discussions 19
4.1 The web page preprocessing 19
4.2 TF-IDF of keywords and concepts 21
4.3 The LSA semantic analyses 22
4.4 The semantic distance value 22
4.5 The comparison method 24
4.6 The accuracy 26
Chapter 5 Conclusions and future work 30
References 31
簡歷 36

List of Tables
Table 1. The distance value between the keyword and the clustering center. 17
Table 2. The distance value between the keyword and the concepts. 17
Table 3. Clusters of concepts in the ontology. 23
Table 4. The distance value between keywords and ontology concepts at first round. 23
Table 5. The semantic distance value between the keyword and the ontology concepts. 25
Table 6. The precious of terms. 27
Table 7. Estimation the result in ontology. 29

List of Figures
Figure 1. The Semantic web “Layer Cake”. 4
Figure 2. LSA work flow. 9
Figure 3. Terms-documents matrix. 10
Figure 4. Matrix X decomposed by the SVD and dimension reduction. 10
Figure 5. Inserting the new concept into the ontology framework. 12
Figure 6. Rebuilding the matrix. 13
Figure 7. Cluster of concepts in the ontology. 16
Figure 8. Inserting the keyword into the ontology in the first round. 17
Figure 9. The ontology was upgraded by the new keywords. 18
Figure 10. A basketball ontology. 19
Figure 11. The operation of CKIP system. 20
Figure 12. The operation of LSA can find semantic relationship. 21
Figure 13. The new basketball ontology. 24
Figure 14. Adding a new keyword to the ontology. 25
Figure 15. The new basketball ontology. 26

J. Abasolo and M. Gómez(2000), “MELISA. An ontology-based agent for information retrieval in medicine,” ECDL 2000 Workshop on the Semantic Web Lisbon, pp.73-82.
M. Anwar Hossain and A. El Saddik(2004), “Creating Knowledge and Semantic Web Applications on the Web,” In Proceedings of the 2nd International Symposium on Innovation in Information & Communication Technology, Amman, Jordan.
T. Berners-Lee, M. Fischetti and M. Dertouzos(1999), Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor, Harper San Francisco, 1st edition.
T. Berners-Lee, J. Hendler and O. Lassila(2001, May), The Semantic Web, Scientific American, pp. 34-43.
J. Broekstra, M. Klein, S. Decker, D. Fensel, F. van Harmelen and I. Horrocks(2002), “Enabling knowledge representation on the Web by extending RDF Schema,” Computer Networks, Vol. 39, No. 5, pp. 609-634.
C. V. Buggenhout and W. Ceusters(2005), “A novel view on information content of concepts in a large ontology and a view on the structure and the quality of the ontology,” International Journal of Medical Informatics, Vol. 74, No. 2-4, pp.125-132.
Y. F. Chen(2003), “Automatic Concept-Based Answer-Finding System,” Master Thesis, Institute of Computer and Information Science.
R. C. Chen, J. Y. Liang and R. H. Pan(2005), “Constructing an Ontology based on ART Neural Network,” Proceedings of the 10th Artificial Intelligence and Applications (TAAI-2005), Kaohsiung.
L. Chen, N. Tokuda and A. Nagai(2003), “A new differential LSI space-based probabilistic document classifier,” Information Processing Letters, Vol. 88, No. 5, pp.203-212.
R. H. L. Chiang, C. E. H. Cecil and V. C. Storey(2001), “A smart Web query method for semantic retrieval of Web data,” Data & Knowledge Engineering, Vol. 38, No.1, pp. 63-84.
CKIP on-line Chinese words segmented service, [On-line], Available at http://ckipsvr.iis.sinica.edu.tw/
S. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas and R. Harshman(1990), “Indexing by Latent Semantic Analysis,” Journal of the American Society of Information Science, Vol. 41, No. 6, pp. 391-407.
T. Gruber(1993), “A translation approach to portable ontology specifications,” Knowledge Acquisition, Vol. 5, No. 2, pp. 199-220.
N. Guarino(1998), “Formal Ontology and Information System,” In Proceedings of the Formal Ontology and Information System (FOIS''98), Trento, Italy, pp. 3-15.
L. Han, L. Sun, G. Chen and L. Xie(2006), “ADSS: An approach to determining semantic similarity,” Advances in Engineering Software, Vol. 37, No. 2, pp. 129-132.
C. H. Hsu(2004), “Automatically Constructing Ontology on Semantic Web,” Master Thesis, Department of Information Management.
S. H. Hsu(2003), “Ontology-based Semantic Annotation Authoring and Retrieval,” Master Thesis, Department of Computer Science and Information Engineering.
Y. H. Hung(2004), “Ontological Inference for User Intention Extraction、Query Expansion and Concept-based Retrieval,” Master Thesis, Department of Computer Science and Information Engineering.
E. K. Jacob(2003, April/May), Ontologies and the Semantic Web, Bulletin of the American Society for Information Science and Technology, pp. 19-22.
C. Jenkins, M. Jackson, P. Burden and J. Wallis(1999), “Automatic RDF metadata generation for resource discovery,” Proceeding of the eighth international conference on World Wide Web, Toronto, Canada, pp. 1305-1320.
L. Khan and F. Luo(2002), “Ontology Construction for Information Selection,” 14th IEEE International Conference on Tools with Artificial Intelligence(ICTAI''02), pp. 122-127.
C. C. Kung(2001), “Personalized XML Information Service System with Automatic Object-oriented Ontology Construction Capability,” Master Thesis, Department of Computer Science and Information Engineering.
T. K. Landauer, P. W. Foltz and D. Laham(1998), “An introduction to Latent Semantic Analysis,” Discourse Processes, Vol. 25, pp. 259-284.
C. Y. Lin(2004), “A Study of Multi-Agent Information System Based on Ontology-A Case for Weather Querying,” Master Thesis, Department of Information Management.
A. Maedche and S. Staab(2000), “Discovering Conceptual Relations from Text,” in W. Horn, ed., Proceedings of the 14th European Conference on Artificial Intelligence(ECAI 2000), IOS Press, Berlin, Germany, pp. 321–325.
F. Manola and E. Miller(2004, February), RDF Primer, World Wide Web Consortium [On-line], Available: http://www.w3.org/TR/rdf-primer/, Access time 2004/10/15.
F. Meziane and Y. Rezgui(2004), “A document management methodology based on similarity contents,” Information Sciences—Informatics and Computer Science: An International Journal, Vol.158, No.1, pp.15-36.
E. Miller(2004, January), Weaving Meaning : An Overview of The Semantic Web, World Wide Web Consortium [On-line], Available: http://www.w3.org/2004/Talks/0120-semweb-umich/slide8-0.html, Access time 2005/12/15.
S. Oyama, T. Kokubo and T. Ishida(2004), “Domain-Specific Web Search with Keyword Spices,” IEEE Transactions on knowledge and data engineering, Vol. 16, No.1, pp. 17-27.
C. Patel, K. Supekar, Y. Lee and E. K. Park(2003), “OntoKhoj: a semantic web portal for ontology searching, ranking and classification,” Proceedings of the 5th ACM international workshop on Web information and data management(WIDM 2003), pp. 58-61.
G. Salton and M. J. McGill(1983), Introduction to Modern Information Retrieval, McGraw Hill Book Co..
J. Shen and Y. Yang(2004), “Extending RDF in distributed knowledge-intensive applications,” Future Generation Computer Systems, Vol. 20, No. 1, pp. 27-46.
D. Stefan, H. Frank van, B. Jeen, E. Michael, F. Dieter, H. Ian, K. Michel and M. Sergey(2000), “The Semantic Web - on the respective Roles of XML and RDF,” IEEE Internet Computing, Vol. 4, No. 5, pp. 63-73.
L. M. Stephens and M. N. Huhns(2001), “Consensus Ontologies Reconciling the Semantics of Web PagesWebpage and Agents,” IEEE Internet Computing, Vol. 5, No. 5, pp. 92-95.
K. W. Tan, H. Han and R. Elmasri(2000), “Web Data Cleansing and Preparation for Ontology Extraction using WordNet,” First International Conference on Web Information Systems Engineering(WISE''00), Vol. 2, pp. 11-18.
J. Wei, S. Bressan and B. C. Ooi(2000), “Mining Term Association Rules for Automatic Global Query Expansion: Methodology and Preliminary Results,” In Proceedings of the First International Conference on Web Information Systems Engineering (WISE''00), pp. 366-373.
S. S. Weng, H. J. Tsai, S. C. Liu and C. H. Hsu(2006), “Ontology construction for information classification,” Expert Systems with Applications, Vol. 31, No. 1, pp. 1-12.
A. B. Williams, and C. Tsatsoulis(2000), “An Instance-based Approach for Identifying Candidate Ontology Relations within a Multi-Agent System,” Fourteenth European Conference on Artificial Intelligence, Ontology Learning ECAI-2000 Workshop, Berlin, Germany.
A. B. Williams, A. Padmanabhan and M. Brian Blake(2005), “Experimentation with Local Consensus Ontologies with Implications for Automated Service Composition,” IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 7, pp. 969-981.