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Event Causality Extraction from Natural Science Literature

Biswanath Barik, Erwin Marsi, Pinar Öztürk
2016 Research in Computing Science  
Automatic extraction of causal knowledge from text content is a challenging task.  ...  Generally, the approaches of causal relation identification proposed in the literature target specific domain such as online news or biomedicine as the domain has significant influence on causality expressions  ...  The causal relations extracted from a collection of research papers of the domain can then be used for causal reasoning with the help of domain knowledge to discover new facts or unknown hypotheses of  ... 
doi:10.13053/rcs-117-1-8 fatcat:m6w7q7himrdshjs6tsd2uqf5jy

An Automated Learner for Extracting New Ontology Relations

Amaal Saleh Hassan Al Hashimy, Narayanan Kulathuramaiyer
2012 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT)  
And as it includes semantic processing, the results produced still need enhancements and the outcome was always limited in terms of domain or coverage.  ...  This approach to semantics is concerned with psychological facts associated with the meaning of words.  ...  Khoo in [10] acquired causal knowledge with manually created syntactic patterns specifically for the MEDLINE text database.  ... 
doi:10.1109/acsat.2012.95 fatcat:n35gxierijf3lh7cjsbhukzmyi

Bridging Text Mining and Bayesian Networks

Sandeep Raghuram, Yuni Xia, Mathew Palakal, Josette Jones, Dave Pecenka, Eric Tinsley, Jean Bandos, Jerry Geesaman
2009 2009 International Conference on Network-Based Information Systems  
This system does not contain a text mining utility as yet and the data required from this operation is manually filled into a relational database.  ...  In [24], a system was also developed for acquiring causal knowledge from text.  ... 
doi:10.1109/nbis.2009.102 dblp:conf/nbis/RaghuramXPJPTBG09 fatcat:w5mmhiyr3rbtzd2ljgjqomvwmy

Database Issues in Knowledge Discovery and Data Mining

Chris Rainsford, John Roddick
1999 Australasian Journal of Information Systems  
The terms "Knowledge Discovery in Databases" and "Data Mining" have been adopted for a field of research dealing with the automatic discovery of knowledge impb'cit within databases.  ...  This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems  ...  The term Data Mining, or Knowledge Discovery in Databases (KDD), has been adopted for a field of research dealing with the discovery of information or knowledge from data held in more or less structured  ... 
doi:10.3127/ajis.v6i2.310 fatcat:57zzkqzw2bdgndhjp5tumgicd4

A DYNAMIC TEMPORAL NEURO FUZZY INFERENCE SYSTEM FOR MINING MEDICAL DATABASES

Nadim
2012 Journal of Computer Science  
This FTCM is generated from the medical temporal database records of diabetic patients where the medical diagnosis is performed by converting the fuzzy cognetive maps into a fuzzy temporal rule based inference  ...  This study proposes a new temporal mining system to discover temporal dependencies between the concepts of a complex causal system by building a Fuzzy Temporal Cognitive Map (FTCM) by extending the FCM  ...  In pre processing thousands of records from the medical dataset are integrated into one relational database table and the records fail to match with the format, can be deleted from the record set.  ... 
doi:10.3844/jcssp.2012.1924.1931 fatcat:buuf6b3zkrhubmlvovnec7dtty

A bibliography of temporal, spatial and spatio-temporal data mining research

John F. Roddick, Myra Spiliopoulou
1999 SIGKDD Explorations  
With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry.  ...  This short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatiotemporal data mining.  ...  The domain of temporal mining focuses on the discovery of causal relationships among events that may be ordered in time and may be causally related.  ... 
doi:10.1145/846170.846173 fatcat:7w6savnhebd6xocjsks25hsjqi

Fuzzy cognitive map approach to web-mining inference amplification

K Lee
2002 Expert systems with applications  
The ®rst phase is to apply the association rule mining, and the second phase is to transform the association rules into FCM-driven causal knowledge bases.  ...  The third phase is dedicated to amplifying the inference by developing the causal knowledge-based inference equivalence property, which was derived from analyzing the inference mechanism of FCMs.  ...  Fig. 4 . 4 Prepocessing: (a) raw web-log database, (b) processed web-log database. Fig. 5 . 5 Transformation into causal knowledge base.  ... 
doi:10.1016/s0957-4174(01)00054-9 fatcat:lcvzil2nj5ehdivj7his4beegq

A Multi-intelligent Agent Architecture for Knowledge Extraction: Novel Approaches for Automatic Production Rules Extraction

Mohammed Abbas Kadhim, M. Afshar Alam, Harleen Kaur
2014 International Journal of Multimedia and Ubiquitous Engineering  
Firstly, we are constructing an Expert Mining Intelligent Agent (EMIA) able to interview with domain experts for mining problem solving knowledge as production rules in a specific diagnosis domain.  ...  In this paper, multi-intelligent agent architecture has been proposed for automatic knowledge extraction from its resources (domain experts and text documents).  ...  Additionally, we produce a brief review of the existing studies related with knowledge extraction from natural language text documents.  ... 
doi:10.14257/ijmue.2014.9.2.10 fatcat:uxe4zxz2m5aorbsjj354s5ytbi

An Overview : Temporal - Side of Sequential Patterns Discovery

Ahmed Aburodes Assaid Alkilany
2013 International Journal of Data Mining & Knowledge Management Process  
The motivation behind this paper is to give preliminary knowledge about a temporal data mining ,as well as presenting and evaluation of the most known algorithms for discovering Temporal-side of Sequential  ...  Temporal data means a data which have incorporated with the concept of time, to maintain past, present and future data.  ...  In corporation with knowledge discovery process, most widely used Allen's interval algebra, as well as first order temporal logic.  ... 
doi:10.5121/ijdkp.2013.3101 fatcat:aft7sf6tm5gb7hxilakoi6dn2y

Data Mining and Knowledge Reuse for the Initial Systems Design and Manufacturing: Aero-engine Service Risk Drivers

N.Morar, R. Roy, J. Mehnen, L.E. Redding, A. Harrison
2013 Procedia CIRP  
The study applied semantic data mining as a methodology for an efficient and the consistent data capture, representation, and analysis.  ...  The paper concludes with missing information links and future research directions.  ...  The authors use text mining tools based on keywords and ontologies within the engineering domain to extract information from different text documents.  ... 
doi:10.1016/j.procir.2013.08.002 fatcat:ohzqtj5mjrg5hp4c5wlvchxs2m

Big Data and Causality

Hossein Hassani, Xu Huang, Mansi Ghodsi
2017 Annals of Data Science  
Data Mining, the process of uncovering hidden information from Big Data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world.  ...  an overview table of Data Mining applications in causality analysis domain as a reference directory.  ...  Text mining in computational linguistics [22, 23], French text mining [31], medical database text mining [39], MSN search engine queries from temporal logs [40], open domain text with classification  ... 
doi:10.1007/s40745-017-0122-3 fatcat:y5fixid4ujgsjlgrcfceq5nosu

A MANUFACTURING FAILURE ROOT CAUSE ANALYSIS IN IMBALANCE DATA SET USING PCA WEIGHTED ASSOCIATION RULE MINING

Phaik-Ling Ong, Yun-Huoy Choo, Azah Kamilah Muda
2015 Jurnal Teknologi  
database.  ...  The result shows that PCA-WARM is capable in capturing pattern from the data of industrial process. These patterns are proven able to explain industrial failure.  ...  The domain expert will be gathered again to verify the related data that are needed for the process of data mining.  ... 
doi:10.11113/jt.v77.6496 fatcat:7r6ou7il7nawrov5nhcymu6rei

High-Level Information Fusion for Risk and Accidents Prevention in Pervasive Oil Industry Environments [chapter]

Nayat Sanchez-Pi, Luis Martí, José Manuel Molina, Ana Cristina Bicharra Garcia
2014 Communications in Computer and Information Science  
process.  ...  Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications.  ...  Knowledge discovery (KDD) is the process of extracting and refining useful knowledge from large databases. KDD stages are: inductive learning, deductive verification and human intuition.  ... 
doi:10.1007/978-3-319-07767-3_19 fatcat:fupkyy6twrf2dbr4x4rthygquy

Applying Dynamic Causal Mining in Retailing

Yi Wang
2008 POLIBITS Research Journal on Computer Science and Computer Engineering With Applications  
With the fast development of information technology, retailers are suffering from the excess of information. Too much information can be a problem. However, more information creates more opportunity.  ...  This paper suggests that applying association mining techniques can further improve the dealing of information overload in a web oriented retailing environment.  ...  Part 1: -Preprocessing: Removal of the "least" causal data from database Part 2: -Mining: Formation of a rule set that covers all training examples with minimum number of rules Part 3: -Checking: Check  ... 
doi:10.17562/pb-37-7 fatcat:ytokixu4uvfgfa64yerpfsdx7y

AutoBayesian: Developing Bayesian Networks Based on Text Mining [chapter]

Sandeep Raghuram, Yuni Xia, Jiaqi Ge, Mathew Palakal, Josette Jones, Dave Pecenka, Eric Tinsley, Jean Bandos, Jerry Geesaman
2011 Lecture Notes in Computer Science  
Information closely related to Bayesian network usually includes the causal associations, statistics information and experimental results.  ...  text mining.  ...  The knowledge-driven approach involves using an expert's domain knowledge to derive the causal associations; and the data driven approach derives the mappings from data which can then be validated by the  ... 
doi:10.1007/978-3-642-20152-3_37 fatcat:wnj3vueftzdsromxpcbmqk34dm
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