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Automated knowledge discovery in advanced knowledge management

Marko Grobelnik, Dunja Mladenić, John Davies
2005 Journal of Knowledge Management  
Findings Knowledge discovery techniques provide to be very appropriate for many problems related to knowledge management.  ...  . probabilistic, various kinds of logic, visualizations), on different scale (small data-sets or terra bytes), and for different purpose (e.g. prediction, segmentation, explanation).  ...  Network of Excellence (IST-2002-506778).  ... 
doi:10.1108/13673270510622500 fatcat:l7sdmkplzzf6vgtsrekmp7hb7m

Transductive Learning of Logical Structures from Document Images [chapter]

Michelangelo Ceci, Corrado Loglisci, Donato Malerba
2011 Studies in Computational Intelligence  
The classifier takes advantage of discovered emerging patterns that permit us to qualitatively characterize classes.  ...  To face these problems, we investigate the application of a relational classifier that works in the transductive setting.  ...  Lynn Rudd for reading the final version.  ... 
doi:10.1007/978-3-642-22913-8_6 fatcat:bcwi6xj4ivfdlebmd3ojiphtya

Document Image Understanding through Iterative Transductive Learning [chapter]

Michelangelo Ceci, Corrado Loglisci, Lucrezia Macchia, Donato Malerba, Luciano Quercia
2013 Communications in Computer and Information Science  
The relational setting is justified by the multi-modal nature of the data we are dealing with, while transduction is justified by the possibility of exploiting the large amount of information conveyed  ...  in the unlabeled layout components.  ...  [8] ) for the task of emerging patterns discovery but only one attempt [3] has been done to deal with relational data.  ... 
doi:10.1007/978-3-642-35834-0_13 fatcat:it6cxtz6ezhwho7jnw22wmvaiy

Web mining in soft computing framework: relevance, state of the art and future directions

S.K. Pal, V. Talwar, P. Mitra
2002 IEEE Transactions on Neural Networks  
The reason for considering web mining, a separate field from data mining, is explained.  ...  Index Terms-Artificial neural networks (ANNs), data mining, fuzzy logic (FL), genetic algorithms (GAs), information retrieval (IR), knowledge discovery, pattern recognition, rough sets (RSs), search engines  ...  Mining 1) is termed as knowledge discovery in texts (KDT) or text data mining or text mining. Text mining is a well-developed subject and full coverage of it is beyond the scope of this survey.  ... 
doi:10.1109/tnn.2002.1031947 pmid:18244512 fatcat:a2ea5nfnczgjlpwsbwe6ebt5hi

Machine Learning and Security Applications in Digital Library

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
This paper presents the applications of machine learning machine learning applications in the digital library. Using machine learning it is possible to search and retrieve non-textual information.  ...  A systematic review of literature is also done and with the help of citation mapping in Web of Science citation network analysis is presented  ...  In whole book recognition, image recognition is used with automatic adaptation. The scanned image is to be OCRed for text searching.  ... 
doi:10.35940/ijitee.a4718.119119 fatcat:afbz5i443je63mndgcputepubi

Sentiment Analysis [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
S Cross-References Stream Mining A subfield of knowledge discovery called stream mining addresses the issue of rapidly changing data.  ...  For instance, this can include the adaptation and implementation of subgroup discovery techniques to solving open problems in the area of contrast set mining and emerging patterns.  ...  In contrast, SDP assumes a relational or firstorder logical representation of an MDP (as given in Fig. 1 ) to exploit the existence of domain ob-jects, relations over these objects, and the ability to  ... 
doi:10.1007/978-1-4899-7687-1_100512 fatcat:ce4yyqo2czftzcx2kbauglh3fu

Spike-Timing-Dependent Plasticity [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
S Cross-References Stream Mining A subfield of knowledge discovery called stream mining addresses the issue of rapidly changing data.  ...  For instance, this can include the adaptation and implementation of subgroup discovery techniques to solving open problems in the area of contrast set mining and emerging patterns.  ...  In contrast, SDP assumes a relational or firstorder logical representation of an MDP (as given in Fig. 1 ) to exploit the existence of domain ob-jects, relations over these objects, and the ability to  ... 
doi:10.1007/978-1-4899-7687-1_774 fatcat:2jprihjaxfbtpb3ttwuuz3u34y

Text Classification Techniques: A Literature Review

2018 Interdisciplinary Journal of Information, Knowledge, and Management  
extraction possibilities in the field of data mining.  ...  Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery.  ...  This type of learning employs small amount of labeled data and large amount of unlabeled data for training.  ... 
doi:10.28945/4066 fatcat:6dio5bpajjf77lkrs7xdtciveu

Incremental Learning: Areas and Methods – A Survey

Prachi Joshi
2012 International Journal of Data Mining & Knowledge Management Process  
While the areas of applications in data mining are growing substantially, it has become extremely necessary for incremental learning methods to move a step ahead.  ...  The tremendous growth of unlabeled data has made incremental learning take up a big leap.  ...  From the related study it is worth to mention that most of the incremental clustering for pattern discovery rely on similarity measure between the data points, where as some are managed by threshold.  ... 
doi:10.5121/ijdkp.2012.2504 fatcat:vv4go3hvvngalerdo6jxjdo6la

PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning [article]

Rongzhi Zhang, Yue Yu, Pranav Shetty, Le Song, Chao Zhang
2022 arXiv   pre-print
We study interactive weakly-supervised learning – the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model.  ...  Our proposed model, named PRBoost, achieves this goal via iterative prompt-based rule discovery and model boosting.  ...  TACRED: We use the rules in Zhou et al. (2020) for the relation extraction task. Their rules are in the form of relation phrases, which include the entity pair and a keyword. 2.  ... 
arXiv:2203.09735v1 fatcat:ufkssmhk3veuthfzslhd77pj4u

Learning for Biomedical Information Extraction: Methodological Review of Recent Advances [article]

Feifan Liu, Jinying Chen, Abhyuday Jagannatha, Hong Yu
2016 arXiv   pre-print
In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.  ...  Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological  ...  [33] summarized text and data mining advances and emerging applications for biological discovery, which is domain specific and task oriented, focusing on applications of natural language processing  ... 
arXiv:1606.07993v1 fatcat:7d5om7zxxzhoviiriasrfwg3xi

Opinion Mining [chapter]

2017 Encyclopedia of Machine Learning and Data Mining  
Synonyms Instance language Definition The observation language used by a machine learning system is the language in which the observations it learns from are described.  ...  Excellent starting points to become familiar with this field are Relational Data Mining by Džeroski and Lavraè (2001) and Logical and Relational Learning by De Raedt (2008) .  ...  Ontology learning relates to the phase of building the ontology using semiautomatic methods based on text mining or machine learning.  ... 
doi:10.1007/978-1-4899-7687-1_100511 fatcat:oluapsjgxzh6nlkqujjj562lzi

A Survey on Transfer Learning

Sinno Jialin Pan, Qiang Yang
2010 IEEE Transactions on Knowledge and Data Engineering  
For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature  ...  A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution.  ...  ACKNOWLEDGMENTS The authors thank the support of Hong Kong CERG Project 621307 and a grant from NEC China Lab.  ... 
doi:10.1109/tkde.2009.191 fatcat:pwex3mhwcvcwzp32ekksklzt4e

Machine Learning with World Knowledge: The Position and Survey [article]

Yangqiu Song, Dan Roth
2017 arXiv   pre-print
Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer  ...  Particularly, labeling large amount of data for each domain-specific problem can be very time consuming and costly.  ...  of the organizations that supported the work.  ... 
arXiv:1705.02908v1 fatcat:t4fypa6h3vampcp64eosvppsfe

What Can We Learn about Fall Risk Factors from EHR Nursing Notes? A Text Mining Study

Ragnhildur I. Bjarnadottir, Robert J. Lucero
2018 eGEMs  
Text mining procedures were performed on RN's narrative notes following the traditional steps of knowledge discovery.  ...  While chart abstraction has been used to operationalize risk factors, few studies have examined registered nurses' (RNs') narrative notes as a source of actionable data.  ...  The lexicon generated in this study is both expert-and data-driven and can be adopted and/or adapted in other text mining analyses for external validation.  ... 
doi:10.5334/egems.237 pmid:30263902 pmcid:PMC6157016 fatcat:etjrhk6errh2plugnqxnlaj3mu
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