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AuthorTitleYearJournal/ProceedingsReftypeDOI/URL
Poelmans, J., Ignatov, D. I., Kuznetsov, S. O. & Dedene, G. Formal concept analysis in knowledge processing: A survey on applications 2013 Expert Systems with Applications   article DOIURL  
Abstract: Abstract This is the second part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this second part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 which applied FCA-based methods for knowledge discovery and ontology engineering in various application domains. These domains include software mining, web analytics, medicine, biology and chemistry data.
BibTeX:
@article{poelmans2013formal2,
  author = {Poelmans, Jonas and Ignatov, Dmitry I. and Kuznetsov, Sergei O. and Dedene, Guido},
  title = {Formal concept analysis in knowledge processing: A survey on applications },
  journal = {Expert Systems with Applications },
  year = {2013},
  volume = {40},
  number = {16},
  pages = {6538 - 6560},
  url = {http://www.sciencedirect.com/science/article/pii/S0957417413002959},
  doi = {http://dx.doi.org/10.1016/j.eswa.2013.05.009}
}
Poelmans, J., Kuznetsov, S. O., Ignatov, D. I. & Dedene, G. Formal Concept Analysis in knowledge processing: A survey on models and techniques 2013 Expert Systems with Applications   article DOIURL  
Abstract: Abstract This is the first part of a large survey paper in which we analyze recent literature on Formal Concept Analysis (FCA) and some closely related disciplines using FCA. We collected 1072 papers published between 2003 and 2011 mentioning terms related to Formal Concept Analysis in the title, abstract and keywords. We developed a knowledge browsing environment to support our literature analysis process. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. In this first part, we zoom in on and give an extensive overview of the papers published between 2003 and 2011 on developing FCA-based methods for knowledge processing. We also give an overview of the literature on FCA extensions such as pattern structures, logical concept analysis, relational concept analysis, power context families, fuzzy FCA, rough FCA, temporal and triadic concept analysis and discuss scalability issues.
BibTeX:
@article{poelmans2013formal1,
  author = {Poelmans, Jonas and Kuznetsov, Sergei O. and Ignatov, Dmitry I. and Dedene, Guido},
  title = {Formal Concept Analysis in knowledge processing: A survey on models and techniques },
  journal = {Expert Systems with Applications },
  year = {2013},
  volume = {40},
  number = {16},
  pages = {6601 - 6623},
  url = {http://www.sciencedirect.com/science/article/pii/S0957417413002935},
  doi = {http://dx.doi.org/10.1016/j.eswa.2013.05.007}
}
Konstan, J. & Riedl, J. Recommender systems: from algorithms to user experience 2012 User Modeling and User-Adapted Interaction   article DOIURL  
Abstract: Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.
BibTeX:
@article{konstan2012recommender,
  author = {Konstan, JosephA. and Riedl, John},
  title = {Recommender systems: from algorithms to user experience},
  journal = {User Modeling and User-Adapted Interaction},
  publisher = {Springer Netherlands},
  year = {2012},
  volume = {22},
  number = {1-2},
  pages = {101-123},
  url = {http://dx.doi.org/10.1007/s11257-011-9112-x},
  doi = {http://dx.doi.org/10.1007/s11257-011-9112-x}
}
leaong, S. A survey of recommender systems for scientific papers 2012   other  
BibTeX:
@other{noauthororeditor,
  author = {leaong, Sheryl},
  title = {A survey of recommender systems for scientific papers},
  year = {2012}
}
Strohmaier, M., Körner, C. & Kern, R. Understanding why users tag: A survey of tagging motivation literature and results from an empirical study 2012 Web Semantics: Science, Services and Agents on the World Wide Web   article DOIURL  
Abstract: While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.
BibTeX:
@article{strohmaier2012understanding,
  author = {Strohmaier, Markus and Körner, Christian and Kern, Roman},
  title = {Understanding why users tag: A survey of tagging motivation literature and results from an empirical study},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year = {2012},
  volume = {17},
  number = {0},
  pages = {1 - 11},
  url = {http://www.sciencedirect.com/science/article/pii/S1570826812000820},
  doi = {http://dx.doi.org/10.1016/j.websem.2012.09.003}
}
Fortunato, S. Community detection in graphs 2010 Physics Reports   article DOIURL  
Abstract: The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e.g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
BibTeX:
@article{fortunato2010community,
  author = {Fortunato, Santo},
  title = {Community detection in graphs },
  journal = {Physics Reports },
  year = {2010},
  volume = {486},
  number = {3–5},
  pages = {75 - 174},
  url = {http://www.sciencedirect.com/science/article/pii/S0370157309002841},
  doi = {http://dx.doi.org/10.1016/j.physrep.2009.11.002}
}
Poelmans, J., Elzinga, P., Viaene, S. & Dedene, G. Formal Concept Analysis in Knowledge Discovery: A Survey 2010 Conceptual Structures: From Information to Intelligence   incollection DOIURL  
Abstract: In this paper, we analyze the literature on Formal Concept Analysis (FCA) using FCA. We collected 702 papers published between 2003-2009 mentioning Formal Concept Analysis in the abstract. We developed a knowledge browsing environment to support our literature analysis process. The pdf-files containing the papers were converted to plain text and indexed by Lucene using a thesaurus containing terms related to FCA research. We use the visualization capabilities of FCA to explore the literature, to discover and conceptually represent the main research topics in the FCA community. As a case study, we zoom in on the 140 papers on using FCA in knowledge discovery and data mining and give an extensive overview of the contents of this literature.
BibTeX:
@incollection{springerlink:10.1007/978-3-642-14197-3_15,
  author = {Poelmans, Jonas and Elzinga, Paul and Viaene, Stijn and Dedene, Guido},
  title = {Formal Concept Analysis in Knowledge Discovery: A Survey},
  booktitle = {Conceptual Structures: From Information to Intelligence},
  publisher = {Springer},
  year = {2010},
  volume = {6208},
  pages = {139-153},
  url = {http://dx.doi.org/10.1007/978-3-642-14197-3_15},
  doi = {http://dx.doi.org/10.1007/978-3-642-14197-3_15}
}
Trant, J. Studying Social Tagging and Folksonomy: A Review and Framework 2009 Journal of Digital Information   article URL  
Abstract: This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks).
BibTeX:
@article{trant2009studying,
  author = {Trant, Jennifer},
  title = {Studying Social Tagging and Folksonomy: A Review and Framework},
  journal = {Journal of Digital Information},
  year = {2009},
  volume = {10},
  number = {1},
  url = {https://journals.tdl.org/jodi/index.php/jodi/article/view/269}
}
Ames, M. & Naaman, M. Why We Tag: Motivations for Annotation in Mobile and Online Media 2007 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems   inproceedings DOIURL  
Abstract: Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture. A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.
BibTeX:
@inproceedings{ames2007motivations,
  author = {Ames, Morgan and Naaman, Mor},
  title = {Why We Tag: Motivations for Annotation in Mobile and Online Media},
  booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
  publisher = {ACM},
  year = {2007},
  pages = {971--980},
  url = {http://doi.acm.org/10.1145/1240624.1240772},
  doi = {http://dx.doi.org/10.1145/1240624.1240772}
}
Adomavicius, G. & Tuzhilin, A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions 2005 IEEE Transactions on Knowledge and Data Engineering   article DOI  
Abstract: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.
BibTeX:
@article{10.1109/TKDE.2005.99,
  author = {Adomavicius, Gediminas and Tuzhilin, Alexander},
  title = {Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  publisher = {IEEE Computer Society},
  year = {2005},
  volume = {17},
  number = {6},
  pages = {734--749},
  doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2005.99}
}
Lakhal, L. & Stumme, G. Efficient Mining of Association Rules Based on Formal Concept Analysis 2005 Formal Concept Analysis   incollection DOIURL  
Abstract: Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to
BibTeX:
@incollection{lakhal2005efficient,
  author = {Lakhal, Lotfi and Stumme, Gerd},
  title = {Efficient Mining of Association Rules Based on Formal Concept Analysis},
  booktitle = {Formal Concept Analysis},
  publisher = {Springer Berlin Heidelberg},
  year = {2005},
  volume = {3626},
  pages = {180-195},
  url = {http://dx.doi.org/10.1007/11528784_10},
  doi = {http://dx.doi.org/10.1007/11528784_10}
}
Tilley, T., Cole, R., Becker, P. & Eklund, P. Formal Concept Analysis 2005   incollection URL  
Abstract: Formal Concept Analysis (FCA) has typically been applied in the field of software engineering to support software maintenance and object-oriented class identification tasks. This paper presents a broader overview by describing and classifying academic papers that report the application of FCA to software engineering. The papers are classified using a framework based on the activities defined in the ISO12207 Software Engineering standard. Two alternate classification schemes based on the programming language under analysis and target application size are also discussed. In addition, the authors work to support agile methods and formal specification via FCA is introduced.
BibTeX:
@incollection{tilley2005formal,
  author = {Tilley, Thomas and Cole, Richard and Becker, Peter and Eklund, Peter},
  title = {Formal Concept Analysis},
  publisher = {Springer-Verlag},
  year = {2005},
  pages = {250--271},
  url = {http://dl.acm.org/citation.cfm?id=2137739.2137755}
}

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