A Knowledge-based Approach to Citation Extraction

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A Knowledge-based Approach to Citation Extraction 1,2, Tzong-Han Tsai 1,3, Cheng-Lung Sung 1, Cheng-Wei Lee 1, Shih-Hung Wu 4, Chorng-Shyong Ong 2, Wen-Lian Hsu 1 1 Institute of Information Science,

1 A Knowledge-based Approach to Citation Extraction 1,2, Tzong-Han Tsai 1,3, Cheng-Lung Sung 1, Cheng-Wei Lee 1, Shih-Hung Wu 4, Chorng-Shyong Ong 2, Wen-Lian Hsu 1 1 Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan 2 Department of Information Management, National Taiwan University, Taipei, Taiwan 3 Department of Computer Science and Engineering, National Taiwan University, Taipei, Taiwan 4 Dept. of Computer Science and Information Engineering, Chaoyang Univ. of Technology, Taiwan myday@iis.sinica.edu.tw IEEE IRI /

2 Outline Introduction Proposed Approach Experimental Results and Discussion Related Works Conclusions and Future Research 2/

3 Introduction Integration of the bibliographical information of scholarly publications available on the Internet is an important task in academic research. Accurate reference metadata extraction for scholarly publications is essential for the integration of information from heterogeneous reference sources. We propose a knowledge-based approach to literature mining and focus on reference metadata extraction methods for scholarly publications. INFOMAP: ontological knowledge representation framework Automatically extract the reference metadata. 3/

4 Proposed Approach Reference Data Collection Reference Database Knowledge Representation In INFOMAP KRMap Database Reference Information Extraction Reference Metadata Online Service 4/

5 Phase 1 Reference Data Collection Journal Spider (journal agent) collect journal data from the Journal Citation Reports (JCR) indexed by the ISI and digital libraries on the Web. Citation data source ISI web of science DBLP Citeseer PubMed 5/

6 Phase 2 Knowledge Representation in INFOMAP 6/

7 INFOMAP INFOMAP as ontological knowledge representation framework extracts important citation concepts from a natural language text. Feature of INFOMAP represent and match complicated template structures hierarchical matching regular expressions semantic template matching frame (non-linear relations) matching graph matching Using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different kinds of reference formats or styles. 7/

8 Phase 3 Reference Metadata Extraction Journal Reference styles Bioinformatics style (BIOI) ACM style (ACM) IEEE style (IEEE) APA style (APA) JCB style (JCB) MISQ style (MISQ) Reference style example Davenport, T., DeLong, D., & Beers, M. (1998) Successful knowledge management projects. Sloan Management Review, 39(2), Davenport, T., DeLong, D. and Beers, M Successful knowledge management projects. Sloan Management Review, 39 (2) [1] T. Davenport, D. DeLong, and M. Beers, "Successful knowledge management projects," Sloan Management Review, vol. 39, no. 2, pp , Davenport, T., DeLong, D., & Beers, M. (1998). Successful knowledge management projects. Sloan Management Review, 39(2), Davenport, T., DeLong, D., & Beers, M Successful knowledge management projects. Sloan Management Review 39(2), Davenport, T., DeLong, D., and Beers, M. "Successful knowledge management projects," Sloan Management Review (39:2) 1998, pp Table 1. Examples of different journal reference styles 8/

9 Phase 4 Knowledge-based Reference Metadata Extraction - Online Service 9/

10 Citation Extraction From Text to BixTex W. L. Hsu, "The coloring and maximum independent set problems on planar perfect graphs," J. Assoc. Comput. Machin., (1988), W. L. Hsu, "On the general feasibility test of scheduling lot sizes for several products on one machine," Management Science 29, (1983), W. L. Hsu, "The distance-domination numbers of trees," Operations Research Letters 1, (3), (1982), Figure 3. The system input of knowledge-based Author = {W. L. Hsu}, Title = {The coloring and maximum independent set problems on planar perfect graphs,"}, Journal = {J. Assoc. Comput. Machin.}, Volume = {}, Number = {}, Pages = { }, Year = {1988 Author = {W. L. Hsu}, Title = {On the general feasibility test of scheduling lot sizes for several products on one machine,"}, Journal = {Management Science}, Volume = {29}, Number = {}, Pages = {93-105}, Year = {1983 Author = {W. L. Hsu}, Title = {The distance-domination numbers of trees,"}, Journal = {Operations Research Letters}, Volume = {1}, Number = {3}, Pages = {96-100}, Year = {1982 }} Figure 5. The system output of BibTex Format 10/

11 System Input (Plain text) System Output Output BibTex Figure 6. The online service of knowledge-based RME ( 11/

12 Experimental Results and Discussion Experimental data We used EndNote to collect Bioinformatics citation data for 2004 from PubMed. A total of 907 bibliography records were collected from PubMed digital libraries on the Web. Reference testing data was generated for each of the six reference styles (BIOI, ACM, IEEE, APA, MISQ, and JCB). Randomly selected 500 records for testing from each of the six reference styles. 12/

13 Accuracy of Citation Extraction Definition: We consider a field to be correctly extracted only when the field values in the reference testing data are correctly extracted. The accuracy of citation extraction is defined as follows: Accuracy = Number of correctly Total number extracted of fields fields 13/

14 Experimental results of citation extraction from six reference styles % 99.40% 98.33% 99.77% 99.67% 99.13% 97.87% 95.00% 94.07% 94.70% Accuracy 90.00% Bioinformatics ACM IEEE APA JCB MISQ Average 85.00% 80.00% Author Title Journal Volume Issue Year Pages Overall Average Field 14/

15 Example Results 15/

16 Analysis of the structure of reference styles Field Field Relation Structure Percentage% Author <Author><Year> 54.29% <Author><Title> 42.86% N/A 2.85% Year <Author><Year><Title> 48.57% <Journal><Year><Volume> 20.00% <Issue><Year><Pages> 14.29% <Author><Year><Journal> 5.71% <Pages><Year> 2.86% <Volume><Year><Pages> 2.86% N/A 5.71% Title <Year><Title><Journal> 48.57% <Author><Title><Journal> 42.86% N/A 8.57% Journal <Title><Journal><Volume> 71.43% <Title><Journal><Year> 20.00% <Year><Journal><Volume> 5.71% N/A 2.86% Volume <Journal><Volume><Pages> 40.00% <Journal><Volume><Issue> 31.43% <Year><Volume><Issue> 14.29% <Year><Volume><Pages> 5.71% <Journal><Volume><Volume> 2.86% <Journal><Volume><Year> 2.86% N/A 2.85% Issue <Volume><Issue><Pages> 34.29% <Volume><Issue><Year> 14.29% N/A 51.42% Pages <Volume><Pages> 42.86% <Issue><Pages> 34.29% 16/

17 Related Works Machine learning approaches Citeseer [8, 9, 12] take advantage of probabilistic estimation, which is based on the training sets of tagged bibliographical data, to boost performance. The citation parsing technique of Citeseer can identify titles and authors with approximately 80% accuracy and page numbers with approximately 40% accuracy. Seymore et al. [15] use the Hidden Markov Model (HMM) to extract important fields from the headers of computer science research papers Achieve an overall word accuracy of 92.9% Peng et al. [14] employ Conditional Random Fields (CRF) to extract various common fields from the headers and citations of research papers. Achieve an overall word accuracy of 85.1%(HMM) compared to 95.37%(CRF) and an overall instance accuracy of 10%(HMM) compared to 77.33%(CRF) for paper references. 17/

18 Related Works (Cont.) Rule-based models Chowdhury [3] and Ding et al. [5], use a template mining approach for citation extraction from digital documents. Ding et al. [5] use three templates for extracting information from cited articles (citations) and obtain a quite satisfactory result (more than 90%) for the distribution of information extracted from each unit in cited articles. The advantage of their rule-based model is its efficiency in extracting reference information. However, they treat references in one style only from tagged texts (e.g., references formatted in HTML), whereas our method treats references in more than six reference styles from plain text. 18/

19 Comparison with related works Knowledge-based approach Our proposed knowledge-based RME method for scholarly publications can extract reference information from 907 records in various reference styles with a high degree of precision the overall average field accuracy is 97.87% for six major styles listed in Table % for the MISQ style 87% for other 30 randomly selected styles 19/

20 Conclusions Citation extraction is a challenging problem The diverse nature of reference styles We have proposed a knowledge-based citation extraction method for scholarly publications. The experimental results indicate that, by using INFOMAP, we can extract author, title, journal, volume, number (issue), year, and page information from different reference styles with a high degree of precision. The overall average field accuracy of citation extraction is 97.87% for six major reference styles. 20/

21 Future Research Integrate the ontological and the machine learning approaches to boost the performance of citation information extraction Maximum-Entropy Method (MEM) Hidden Markov Model (HMM) Conditional Random Fields (CRF) Support Vector Machines (SVM) 21/

22 Q & A A Knowledge-based Approach to Citation Extraction 1,2, Tzong-Han Tsai 1,3, Cheng-Lung Sung 1, Cheng-Wei Lee 1, Shih-Hung Wu 4, Chorng-Shyong Ong 2, Wen-Lian Hsu 1 1 Institute of Information Science, Academia Sinica, Nankang, Taipei, Taiwan 2 Department of Information Management, National Taiwan University, Taipei, Taiwan 3 Department of Computer Science and Engineering, National Taiwan University, Taipei, Taiwan 4 Dept. of Computer Science and Information Engineering, Chaoyang Univ. of Technology, Taiwan myday@iis.sinica.edu.tw IEEE IRI /

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