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Ojokoh, Bolanle
- Rule-Based Metadata Extraction for Heterogeneous References
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Authors
Affiliations
1 Department of Computer Science and Technology, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100 871, CN
1 Department of Computer Science and Technology, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100 871, CN
Source
Oriental Journal of Computer Science and Technology, Vol 2, No 2 (2009), Pagination: 101-111Abstract
References form an essential part of electronic scholarly publications. Accurate and automatic reference metadata generation provides scalability, interoperability and usability for digital libraries and their collections. This paper deals with automatic metadata extraction from the references of general digital documents using rule-based approach. It encompasses automatic extraction of metadata from book and journal references. The system consists of four major components: a means of providing reference input (by uploading the file or providing the set of references in the window provided by the browser), the text converter for converting documents into standard text format, the parser for automatically extracting metadata such as reference style, author, title, journal, volume, number (issue), year, and page information and author, title, publisher, place of publication, year and pages information from book and journal references of the converted documents using pre-defined regular expressions, and the browser for displaying the results. The experimental results show that the proposed framework can be used to extract metadata from different reference styles of book and journal references effectively.Keywords
Metadata, Implementation, Experiment, References, Digital Libraries, Information Extraction.Full Text
- Discovering Spatio-Temporal Patterns of Themes in Social Media
Abstract Views :166 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Federal University of Technology, P. M. B. 704, Akure, NG
1 Department of Computer Science, Federal University of Technology, P. M. B. 704, Akure, NG
Source
Oriental Journal of Computer Science and Technology, Vol 9, No 3 (2016), Pagination: 165-176Abstract
Social networking website creates new ways for engaging people belonging to different communities, moral and social values to communicate and share valuable knowledge, therefore creating a large amount of data. The importance of mining social media cannot be over emphasized, due to significant information that are revealed which can be applied in different areas. In this paper, a systematic approach for traversing the content of weblog, considering location and time (spatiotemporal) is proposed. The proposed model is capable of searching for subjects in social media using Boyer Moore Horspool (BMH) algorithm with respect to location and time. BMH is an efficient string searching algorithm, where the search is done in such a way that every character in the text needs not to be checked and some characters can be skipped without missing the subject occurrence. Semantic analysis was carried out on the subject by computing the mean occurrence of the subject with the corresponding predicate and object from the total occurrence of the subject. Experiments were carried out on two datasets: the first category was crawled from twitter website from September to October 2014 and the second category was obtained from spinn3r dataset made available through the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). The results obtained from tracking some subjects such as Islam and Obama shows that the mean occurrence of the analysis of the subject successfully reveals the pattern of the subject over a period of time for a specific location. Evaluation of the system which is based on performance and functionality reveals that the model performs better than some baseline models. The proposed model is capable of revealing spatiotemporal pattern for a subject, and can be applied in any area where spatiotemporal factor is to be considered.Keywords
Boyer-Moore-Horspool Alogrithm, Search Processing, Spatio Temporal Pattern, Sementic Analysis.Full Text
References
- Baumer, E. P. S., Sinclair, j., & Tomlinson, B. (2010).Human factor in computingsystems. America is like metamucil: fostering critical and creative thinking about metaphor in political blogs. Atlanta, GA, USA, 34-45.
- Boyer, R. S., & Moore, J. S., A fast string searching algorithm. 20(10), 762-772, (1977). Communications of Association for Computing Machinery(ACM), New York City.
- Charu, C. A., Text mining in social networks in social network data analytics. (2nd ed.). Springer, 353-374, (2011).
- Blei, D., Ng, A.,& Jordan, M.,Latent Dirichlet allocation, Journal of Machine Learning Research. 3(1), 993–1022, (2003).
- Budak, C., Agrawal, D., & El Abbadi, A., Structural trend analysis for online social networks. Proceedings of the VLDB Endowment, 4(10), 646-656, (2011).
- El-Mabrouk, N., &Crochemore, M. (Ed).(1996) Boyer-More strategy to efficient approximate string matching. Combinatorial Pattern Matching, Labuna Beach, California, France
- Hassan, S., Hurst, M., & Alexey, M. (2009). Event Detection and Tracking in Social Streams. Proceeding of International AAAI Conference on Weblogs and Social Media.Third International AAAI Conference on Weblogs and Social Media. Retrievedfrom http://aaai.org/ocs/index.php/ICWSM/09/paper/view/170/493
- Hume A. and Sunday D., Fast String Searching software—Practice and experience, ACM Digital Library., 21(11), 1221–1248, (1991).
- Jayanta, K. P., &Abhisek, S. , Identifying themes in social media and detectingsentiments. International journal of statistics and applications: 1(1) 14-19, (2011).
- Kumar, R., Novak, P. R., & Tomkins A.,Structure and evolution of blogspace.Commun.ACM,47(12):35-39, 2004,.
- Leetaru, K. H., Culturomics 2.0: Forecasting Large-Scale Human Behavior Using Global News Media Tone In Time And Space. Journal on the Internet, 16 (9), (2011). Retrieved from:http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/3663/3040.
- Liu, B., Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167, (2012).
- Mei, Q., Liu, C., Su, H., & Zhai,C.X.(2006). A probabilistic approach to spatiotemporal theme pattern mining on weblogs, WWW.
- Mike, K., & Steve, M., (2008). Centre for Business Performance. The use of information in decision making-Literature review for the audit commission.Cranfield, U.S.A.
- Mike, T., David, W., &Sukhvinder, U. (2009).Data Mining Emotion in Social Network Communication: Gender differences in MySpaceStatisticalCybermetrics Research Group. School of Computing and Information Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton WV1 1SB, UK.
- Nebel, M. E. (2011). Search Texts-But Fast! The Boyer-More-HorspoolAlgorithm.Springer- VerlagBerlin Heidelberg Germany, 10.1007/978-3-642-15328-0_6.
- Ojokoh, B. A., Olayemi, O. C., &Adewale, O. S., .Generating Recommendation Status of Electronic Products from Online Reviews, 4, 1-10, (2012).Intelligent Control and Automation, doi:10.4236/ica.2013.41001.
- Pang, B., & Lee, L., Opinion mining and sentiment analysis, 2(1), 1-35, (2008).Foundations and Trends in Information Retrieval, U.S.A.
- Roick, O., &Heuser, S., Location based social networks–definition, current state of the art and research Agenda. Transactions in GIS, 17(5), 763-784, (2013).
- Sowjanya,M., Ravindra,K., Kumar,R.Y.,(2014).Application of Concept-Based Mining Model in Text Clustering. International Journal of Computer Science and Information Technologies.5(5)6578-6582,(2014).
- Twitter4J,(2014). Java library for the Twitter API2014.Retrieved from http://twitter4j.org/en/index.html.
- Wang, X. & McCallum, A. (2006).Topics over time: a non-markov continuous-time model of topical trends, SIGKDD.
- Wang, C., Wang, J., Xie X., and Ma W. Y. ( 2007). Mining geographic knowledge using location aware topic model.Proceedings of the 4th ACM Workshop On Geographic Information Retrieval, GIR. 65-70. DOI: 10.1145/1316948.1316967.
- William, M. C, Charlie K. D., & Clifford J. W. (2013).Social Network Analysis with content and Graphs.
- Zielinski, A., Middleton, S. E.,Tokarchuk, L. N., & Wang, X. (2013). Information systems for crisis response and management.Social media text mining and network analysis for decision support in natural crisis management.