Semantically linking events for massive scientific literature research

This paper aims to semantically linking scientific research events implied by scientific and technical literature to support information analysis and information service applications. Literature research is an important method to acquire scientific and technical information which is important for research, development and innovation of science and technology. It is difficult but urgently required to acquire accurate, timely, rapid, short and comprehensive information from the large-scale and fast-growing literature, especially in the big data era. Existing literature-based information retrieval systems focus on basic data organization, and they are far from meeting the needs of information analytics. It becomes urgent to organize and analyze scientific research events related to scientific and technical literature for forecasting development trend of science and technology.,Scientific literature such as a paper or a patent is represented as a scientific research event, which contains elements including when, where, who, what, how and why. Metadata of literature is used to formulate scientific research events that are implied in introduction and related work sections of literature. Named entities and research objects such as methods, materials and algorithms can be extracted from texts of literature by using text analysis. The authors semantically link scientific research events, entities and objects, and then, they construct the event space for supporting scientific and technical information analysis.,This paper represents scientific literature as events, which are coarse-grained units comparing with entities and relations in current information organizations. Events and semantic relations among them together formulate a semantic link network, which could support event-centric information browsing, search and recommendation.,The proposed model is a theoretical model, and it needs to verify the efficiency in further experimental application research. The evaluation and applications of semantic link network of scientific research events are further research issues.,This paper regards scientific literature as scientific research events and proposes an approach to semantically link events into a network with multiple-typed entities and relations. According to the needs of scientific and technical information analysis, scientific research events are organized into event cubes which are distributed in a three-dimensioned space for easy-to-understand and information visualization.

[1]  James Allan,et al.  Topic detection and tracking: event-based information organization , 2002 .

[2]  Heng Ji,et al.  Cross-media Event Extraction and Recommendation , 2016, NAACL.

[3]  David Bawden,et al.  Thesaurus Construction and Use: A Practical Manual , 2000 .

[4]  Stuart J. Russell,et al.  First-Order Probabilistic Models for Information Extraction , 2003 .

[5]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[6]  Junichi Tsujii,et al.  Event extraction for systems biology by text mining the literature. , 2010, Trends in biotechnology.

[7]  Paola Velardi,et al.  Time Makes Sense: Event Discovery in Twitter Using Temporal Similarity , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[8]  Ralph Grishman,et al.  Message Understanding Conference- 6: A Brief History , 1996, COLING.

[9]  Edgar G. Daylight,et al.  A Turing tale , 2014, Commun. ACM.

[10]  Xiangfeng Luo,et al.  Online hot event discovery based on Association Link Network , 2015, Concurr. Comput. Pract. Exp..

[11]  Yunchuan Sun,et al.  Constructing the Web of Events from raw data in the Web of Things , 2014, Mob. Inf. Syst..

[12]  Junsheng Zhang,et al.  Semantic relation computing theory and its application , 2016, J. Netw. Comput. Appl..

[13]  A. Bittar Building a TimeBank for French : a reference Corpus Annotated According to the ISO-TimeML Standard , 2010 .

[14]  Joemon M. Jose,et al.  An interactive interface for visualizing events on Twitter , 2014, SIGIR.

[15]  Hulstijn UvA-DARE ( Digital Academic Repository ) A cognitive view on interlanguage variability , 2010 .

[16]  James Allan,et al.  On-Line New Event Detection and Tracking , 1998, SIGIR Forum.

[17]  Junsheng Zhang,et al.  Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things , 2014, Int. J. Distributed Sens. Networks.

[18]  Sophia Ananiadou Biomedical text mining for semantic search and knowledge discovery , 2012, IHI '12.

[19]  M E Newman,et al.  Scientific collaboration networks. I. Network construction and fundamental results. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[21]  Yunchuan Sun,et al.  An extensible and active semantic model of information organizing for the Internet of Things , 2014, Personal and Ubiquitous Computing.

[22]  James Pustejovsky,et al.  Machine Learning of Temporal Relations , 2006, ACL.

[23]  Yi-Ning Tu,et al.  Indices of novelty for emerging topic detection , 2012, Inf. Process. Manag..

[24]  Hai Zhuge,et al.  Schema Theory for Semantic Link Network , 2008, 2008 Fourth International Conference on Semantics, Knowledge and Grid.

[25]  Yuan Hongyong Probability for disaster chains in emergencies , 2010 .

[26]  Huilin Wang,et al.  Discovering Associations among Semantic Links , 2009, 2009 International Conference on Web Information Systems and Mining.

[27]  Junsheng Zhang,et al.  Managing Resources in Internet of Things with Semantic Hyper-Network Model , 2012, 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[28]  Véronique Malaisé,et al.  Design and use of the Simple Event Model (SEM) , 2011, J. Web Semant..

[29]  Daren Yu,et al.  Measuring the preferential attachment mechanism in citation networks , 2008 .

[30]  Junsheng Zhang,et al.  Weaving the Semantic Link Network of Events , 2010, 2010 Sixth International Conference on Semantics, Knowledge and Grids.

[31]  G. Altmann Science and Linguistics , 1993 .

[32]  Ellen M. Voorhees,et al.  The efficiency of inverted index and cluster searches , 1986, SIGIR '86.

[33]  Jun'ichi Tsujii,et al.  Corpus annotation for mining biomedical events from literature , 2008, BMC Bioinformatics.

[34]  Hai Zhuge Resource space model, its design method and applications , 2004, J. Syst. Softw..

[35]  Nicholas Asher,et al.  Reference to abstract objects in discourse , 1993, Studies in linguistics and philosophy.

[36]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[37]  S. Lord,et al.  Cause-effect relation between hyperfibrinogenemia and vascular disease , 2003 .

[38]  Judea Pearl COMMENTS ON NEUBERG'S REVIEW OF CAUSALITY , 2003, Econometric Theory.

[39]  Heng Ji,et al.  EventCube: multi-dimensional search and mining of structured and text data , 2013, KDD.

[40]  Hai Zhuge,et al.  Schema Theory for Semantic Link Network , 2008, 2008 Fourth International Conference on Semantics, Knowledge and Grid.

[41]  H. Small,et al.  Identifying emerging topics in science and technology , 2014 .

[42]  Xiang Ji,et al.  Topic evolution and social interactions: how authors effect research , 2006, CIKM '06.

[43]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[44]  Panpan Yin,et al.  Evaluation of literature frontier based on latent semantic analysis , 2012, 2012 IEEE Symposium on Robotics and Applications (ISRA).

[45]  D. Cases,et al.  How can we investigate citation behavior?: a study of reasons for citing literature in communication , 2000 .

[46]  Junsheng Zhang,et al.  Building text-based temporally linked event network for scientific big data analytics , 2016, Personal and Ubiquitous Computing.

[47]  E. Garfield Citation analysis as a tool in journal evaluation. , 1972, Science.

[48]  Emmon Bach,et al.  The algebra of events , 1986, The Language of Time - A Reader.

[49]  Sophia Ananiadou,et al.  Extracting semantically enriched events from biomedical literature , 2012, BMC Bioinformatics.

[50]  G Stix,et al.  The mice that warred. , 2001, Scientific American.

[51]  L. G. Neuberg CAUSALITY: MODELS, REASONING, AND INFERENCE, by Judea Pearl, Cambridge University Press, 2000 , 2003, Econometric Theory.