Publications

2017

  • S. Andrews, T. Day, K. Domdouzis, L. Hirsch, R. Lefticaru, and C. Orphanides, “Analyzing Crowd-Sourced Information and Social Media for Crisis Management.” Springer, 2017, pp. 77-96.
    [Bibtex]
    @INBOOK{ANDREWSETAL2017,
      author = {Simon Andrews and Tony Day and Konstantinos Domdouzis and Laurence Hirsch and Raluca Lefticaru and Constantinos Orphanides},
      pages = {77-96},
      title = {Analyzing Crowd-Sourced Information and Social Media for Crisis Management},
      publisher = {Springer},
      year = {2017},
      series = {Transactions on Computational Science and Computational Intelligence},
      abstract = {The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the ATHENA project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering, and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State-of-the-art techniques such as formal concept analysis and machine learning are used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment, and assignment of priority. We ground many of these techniques using results obtained from a set of tweets which emerged from the Colorado wildfires of 2012 in order to demonstrate the applicability of our work to real crisis scenarios.}
    }
  • C. Orphanides, B. Akhgar, and P. S. Bayerl, “Discovering Knowledge in Online Drug Transactions Using Conceptual Graphs and Formal Concept Analysis,” in 2016 Proceedings of the 7th European Intelligence and Security Informatics Conference (EISIC), 2017, pp. 100-103.
    [Bibtex]
    @INPROCEEDINGS{ORPHANIDESETAL2017,
      author = {Constantinos Orphanides and Babak Akhgar and Petra Saskia Bayerl},
      title = {Discovering Knowledge in Online Drug Transactions Using Conceptual Graphs and Formal Concept Analysis},
      booktitle = {2016 Proceedings of the 7th European Intelligence and Security Informatics Conference (EISIC)},
      year = {2017},
      publisher = {IEEE},  
      pages = {100-103},
      abstract = {In this short paper, we describe a conceptual approach in which Conceptual Graphs (CGs) and Formal Concept Analysis (FCA) are employed towards knowledge discovery in online drug transactions. The transactions are acquired by performing Named-Entity Recognition (NER) on documents crawled from online public sources such as Twitter and Instagram, and are structured based on a CG ontology created to model such transactions. The drug transactions are then visualized using FCA as the knowledge discovery method.}
    }

2015

  • H. C. Nwagwu and C. Orphanides, “Visual Analysis of a Large and Noisy Dataset,” International Journal of Conceptual Structures and Smart Applications, vol. 3, iss. 2, pp. 12-24, 2015.
    [Bibtex]
    @ARTICLE{NWAGWUORPHANIDES2015,
      author = {Honour Chika Nwagwu and Constantinos Orphanides},
      title = {Visual Analysis of a Large and Noisy Dataset},
      journal = {International Journal of Conceptual Structures and Smart Applications},
      year = {2015},
      volume = {3},
      pages = {12-24},
      number = {2},
      abstract = {Visual analysis has witnessed a growing acceptance as a method of scientific inquiry in the research community. It is used in qualitative and mixed research methods. Even so, visual data analysis is likely to produce biased results when used in analysing a large and noisy dataset. This can be evident when  a data analyst is not able to holistically explore, all the values associated with the objects of interest in a dataset. Consequently, the data analyst may assess inconsistent data as inconsistent when contradiction associated with the data is not visualiused. This work identified incomplete analysis as a challenge in the visual data analysis of a large and noisy dataset. It considers Formal Concept Analysis (FCA) tools and techniques and prescribes the mining and visualisation of Incomplete or Inconsistent Data (IID) when dealing with a large and noisy dataset. It presents an automated approach for transforming IID from a noisy context whose abjects are associated with mutually exclusive many-valued attributes, to a formal context.}
    }

2013

  • C. Melo, C. Orphanides, K. McLeod, M. Aufaure, S. Andrews, and A. Burger, “A Conceptual Approach to Gene Expression Analysis Enhanced by Visual Analytics,” in Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC 2013), 2013, pp. 1314-1319.
    [Bibtex]
    @INPROCEEDINGS{MELOETAL2013,
      author = {Cassio Melo and Constantinos Orphanides and Kenneth McLeod and Marie-Aude Aufaure and Simon Andrews and Albert Burger},
      title = {A Conceptual Approach to Gene Expression Analysis Enhanced by Visual Analytics},  
      booktitle = {Proceedings of the 28th Annual ACM Symposium on Applied Computing (SAC 2013)},
      pages = {1314-1319},
      publisher = {ACM New York, NY, USA},
      note = {ISBN 978-1-4503-1656-9},
      year = {2013},  
      abstract = {The analysis of gene expression data is a complex task for biologists wishing to understand the role of genes in the formation of diseases such as cancer. Biologists need greater support when trying to discover, and comprehend, new relationships within their data. In this paper, we describe an approach to the analysis of gene expression data where overlapping groupings are generated by Formal Concept Analysis and interactively analyzed in a tool called CUBIST. The CUBIST workflow involves querying a semantic database and converting the result into a formal context, which can be simplified to make it manageable, before it is visualized as a concept lattice and associated charts.}
    }
  • S. Andrews and C. Orphanides, “Discovering Knowledge in Data Using Formal Concept Analysis,” International Journal of Distributed Systems and Technologies, vol. 4, iss. 2, pp. 31-50, 2013.
    [Bibtex]
    @ARTICLE{ANDREWSORPHANIDES2013,
      author = {Simon Andrews and Constantinos Orphanides},
      title = {Discovering Knowledge in Data Using Formal Concept Analysis},
      journal = {International Journal of Distributed Systems and Technologies},
      year = {2013},
      volume = {4},
      pages = {31-50},
      number = {2},
      abstract = {Formal Concept Analysis (FCA) has been successfully applied to data in a number of problem domains. However, its use has tended to be on an ad hoc, bespoke basis, relying on FCA experts working closely with domain experts and requiring the production of specialised FCA software for the data analysis. The availability of generalised tools and techniques, that might allow FCA to be applied to data more widely, is limited. Two important issues provide barriers: raw data is not normally in a form suitable for FCA and requires undergoing a process of transformation to make it suitable, and even when converted into a suitable form for FCA, real data sets tend to produce a large number of results that can be difficult to manage and interpret. This article describes how some open-source tools and techniques have been developed and used to address these issues and make FCA more widely available and applicable. Three examples of real data sets, and real problems related to them, are used to illustrate the application of the tools and techniques and demonstrate how FCA can be used as a semantic technology to discover knowledge. Furthermore, it is shown how these tools and techniques enable FCA to deliver a visual and intuitive means of mining large data sets for association and implication rules that complements the semantic analysis. In fact, it transpires that FCA reveals hidden meaning in data that can then be examined in more detail using an FCA approach to traditional data mining methods.}
    }
  • [PDF] C. Orphanides and G. Georgiou, “FCAWarehouse, a Prototype Online Data Repository for FCA,” in Proceedings of the 3rd CUBIST (Combining and Uniting Business Intelligence with Semantic Technologies) Workshop, in conjunction with the 11th International Conference on Formal Concept Analysis (ICFCA) 2013, Dresden, Germany, 2013.
    [Bibtex]
    @INPROCEEDINGS{ORPHANIDESGEORGIOU2013,
      author = {Constantinos Orphanides and George Georgiou},
      title = {FCAWarehouse, a Prototype Online Data Repository for FCA},
      booktitle = {Proceedings of the 3rd CUBIST (Combining and Uniting Business Intelligence with Semantic Technologies) Workshop, in conjunction with the 11th International Conference on Formal Concept Analysis (ICFCA) 2013, Dresden, Germany},
      year = {2013},
      %volume = {753},
      %publisher = {CEUR Workshop Proceedings},
      %note = {ISSN 1613-0073},
      abstract = {This paper presents FCAWarehouse, a prototype online data repository for FCA. The paper explains the motivation behind the development of FCAWarehouse and the features available, such as the ability to donate datasets and their respective formal contexts, the ability to generate artificial formal contexts on-the-fly, and how these features are also available through a set of web-services. The paper concludes by suggesting future work in order to enhance it's usability.}
    }

2012

  • S. Andrews and C. Orphanides, “Knowledge Discovery Through Creating Formal Contexts,” International Journal of Space-Based and Situated Computing, vol. 2, iss. 2, pp. 123-138, 2012.
    [Bibtex]
    @ARTICLE{ANDREWSORPHANIDES2012,
      author = {Simon Andrews and Constantinos Orphanides},
      title = {Knowledge Discovery Through Creating Formal Contexts},
      journal = {International Journal of Space-Based and Situated Computing},
      year = {2012},
      volume = {2},
      pages = {123-138},
      number = {2},
      abstract = {Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called Formal Concept Analysis (FCA). This paper describes a tool called FcaBedrock that converts data into formal contexts for FCA. The paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Examples are given of how formal contexts can be created using FcaBedrock and then analysed for knowledge discovery, using real datasets. Creating formal contexts using FcaBedrock is shown to be straightforward and versatile. Large data sets are easily converted into a standard FCA format.}
    }

2011

  • S. Andrews, C. Orphanides, and S. Polovina, “Visualising Computational Intelligence through Converting Data into Formal Concepts.” Springer, 2011, vol. 352, pp. 139-165.
    [Bibtex]
    @INBOOK{ANDREWSETAL2011,
      chapter = {6},
      pages = {139-165},
      title = {Visualising Computational Intelligence through Converting Data into Formal Concepts},
      publisher = {Springer},
      year = {2011},
      author = {Simon Andrews and Constantinos Orphanides and Simon Polovina},
      volume = {352},
      series = {Next Generation Data Technologies for Collective Computational Intelligence, Studies in Computational Intelligence},
      abstract = {Formal Concept Analysis (FCA) is an emerging data technology that complements collective intelligence such as that identified in the Semantic Web, by visualising the hidden meaning in disparate and distributed data. The chapter demonstrates the discovery of these novel semantics through a set of FCA open source software tools, FcaBedrock and InClose, that were developed by the authors. These tools add computational intelligence by converting data into a Boolean form called a Formal Context, prepare this data for analysis by creating focused and manageable sub-contexts and then analyse the prepared data using a visualisation called a Concept Lattice. The Formal Concepts thus visualised highlight how data itself contains meaning, and how FCA tools thereby extract data’s inherent semantics. The chapter describes how this will be further developed in a project called “Combining and Uniting Business Intelligence with Semantic Technologies” (CUBIST), to provide in-data-warehouse visual analytics for Resource Description Framework (RDF)-based triple stores.}
    }
  • [PDF] C. Orphanides, “Exploring the Applicability of Formal Concept Analysis on Market Intelligence Data,” in Proceedings of the 1st CUBIST (Combining and Uniting Business Intelligence with Semantic Technologies) Workshop, in conjunction with the 19th ICCS [www.iccs.info], Derby, United Kingdom, 2011.
    [Bibtex]
    @INPROCEEDINGS{ORPHANIDES2011,
      author = {Constantinos Orphanides},
      title = {Exploring the Applicability of Formal Concept Analysis on Market Intelligence Data},
      booktitle = {Proceedings of the 1st CUBIST (Combining and Uniting Business Intelligence   with Semantic Technologies) Workshop, in conjunction with the 19th ICCS [www.iccs.info], Derby, United Kingdom},
      year = {2011},
      volume = {753},
      publisher = {CEUR Workshop Proceedings},
      note = {ISSN 1613-0073},
      abstract = {This paper examines and identifies issues associated with the applicability of FCA on sample data provided by a CUBIST use-case partner. The paper explains the various steps related to the transformation of these data to formal contexts, such as preprocessing, cleansing and simplification, as well as preprocessing and limitation issues, by using two FCA tools currently being developed in CUBIST, FcaBedrock and InClose. The paper demonstrates what is achievable to date, using the above-mentioned tools and what issues need to be considered to achieve more meaningful and intuitive FCA analyses. The paper concludes by suggesting and explaining techniques and features that should be implemented in later iterations of these tools, to deal with the identified barriers. This work has been carried out as a part of the European CUBIST FP7 Project: http://www.cubist-project.eu}
    }

2010

  • [PDF] S. Andrews and C. Orphanides, “FcaBedrock, a Formal Context Creator,” in Conceptual Structures: From Information to Intelligence, Proceedings of the 18th International Conference on Conceptual Structures (ICCS) 2010. LNAI 6208, 2010, pp. 181-184.
    [Bibtex]
    @INPROCEEDINGS{ANDREWSORPHANIDES2010A,
      author = {Simon Andrews and Constantinos Orphanides},
      title = {FcaBedrock, a Formal Context Creator},
      booktitle = {Conceptual Structures: From Information to Intelligence, Proceedings of the 18th International Conference on Conceptual Structures (ICCS) 2010. LNAI 6208},
      year = {2010},
      number = {6208},
      series = {LNAI},
      pages = {181-184},
      publisher = {Springer},
      abstract = {FcaBedrock employs user-guided automation to convert c.s.v. data sets into Burmeister .cxt and FIMI .dat context files for FCA.}
    }
  • [PDF] S. Andrews and C. Orphanides, “Analysis of Large Data Sets using Formal Concept Lattices,” in Proceedings of the 7th International Conference on Concept Lattices and Their Applications (CLA) 2010, 2010, pp. 104-115.
    [Bibtex]
    @INPROCEEDINGS{ANDREWSORPHANIDES2010B,
      author = {Simon Andrews and Constantinos Orphanides},
      title = {Analysis of Large Data Sets using Formal Concept Lattices},
      booktitle = {Proceedings of the 7th International Conference on Concept Lattices and Their Applications (CLA) 2010},
      year = {2010},
      pages = {104-115},
      publisher = {University of Seville},
      note = {ISBN 978-84614-4027-6},
      abstract = {Formal Concept Analysis (FCA) is an emerging data technology that has applications in the visual analysis of large-scale data. However, data sets are often too large (or contain too many Formal Concepts) for the resulting Concept Lattice to be readable. This paper complements existing work in this area by describing two methods by which useful and manageable Lattices can be derived from large data sets. This is achieved though the use of a set of freely available FCA tools: the Context creator FcaBedrock and the Concept miner In-Close, that were developed by the authors, and the Lattice builder ConExp. In the first method, a sub-Context is produced from a data set, giving rise to a readable Lattice that focuses on attributes of interest. In the second method, a Context is mined for ‘large’ Concepts which are then used to re-write the original Context, thus reducing ‘noise’ in the Context and giving rise to a readable Lattice that lucidly portrays a conceptual overview of the large set of data it is derived from. A three year European Framework 7 project called CUBIST will develop this work to provide FCA-based visual analytics for data warehouses.}
    }
  • [PDF] S. Andrews and C. Orphanides, “Knowledge Discovery through creating Formal Contexts,” in Proceedings of the 2nd International Conference on Intelligent Networking and Collaborative Systems (INCoS) 2010, 2010, pp. 455-460.
    [Bibtex]
    @INPROCEEDINGS{ANDREWSORPHANIDES2010C,
      author = {Simon Andrews and Constantinos Orphanides},
      title = {Knowledge Discovery through creating Formal Contexts},
      booktitle = {Proceedings of the 2nd International Conference on Intelligent Networking and Collaborative Systems (INCoS) 2010},
      year = {2010},
      pages = {455-460},
      publisher = {IEEE Computer Society},
      note = {978-0-7695-4278-2},
      abstract = {Knowledge discovery is important for systems that have computational intelligence in helping them learn and adapt to changing environments. By representing, in a formal way, the context in which an intelligent system operates, it is possible to discover knowledge through an emerging data technology called Formal Concept Analysis (FCA). This paper describes a tool called FcaBedrock that converts data into Formal Contexts for FCA. The paper describes how, through a process of guided automation, data preparation techniques such as attribute exclusion and value restriction allow data to be interpreted to meet the requirements of the analysis. Creating Formal Contexts using FcaBedrock is shown to be straightforward and versatile. Large data sets are easily converted into a standard FCA format.}
    }
  • [PDF] S. Andrews, C. Orphanides, and S. Polovina, “Visualising Computational Intelligence through converting Data into Formal Concepts,” in Proceedings of the 5th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3GPCIC) 2010, 2010, pp. 302-307.
    [Bibtex]
    @INPROCEEDINGS{ANDREWSETAL2010,
      author = {Simon Andrews and Constantinos Orphanides and Simon Polovina},
      title = {Visualising Computational Intelligence through converting Data into Formal Concepts},
      booktitle = {Proceedings of the 5th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3GPCIC) 2010},
      year = {2010},
      pages = {302-307},
      publisher = {IEEE Computer Society},
      note = {ISBN 978-0-7695-4237-9/10},
      abstract = {Formal Concept Analysis (FCA) is an emerging data echnology that complements collective intelligence such as that identified in the Semantic Web by visualising the hidden meaning in disparate and distributed data. The paper demonstrates the discovery of these novel semantics through a set of FCA open source software tools FcaBedrock and In-Close that were developed by the authors. These tools add computational intelligence by converting data into a Boolean form called a Formal Context, prepare this data for analysis by creating focused and noise-free sub-Contexts and then analyse the prepared data using a visualisation called a Concept Lattice. The Formal Concepts thus visualised highlight how data itself contains meaning, and how FCA tools thereby extract data's inherent semantics. The paper describes how this will be further developed in a project called CUBIST, to provide in-data-warehouse visual analytics for RDF-based triple stores.}
    }
Read more:
How to change WordPress’s default email address and site name

While messing around on a website built in WordPress, I noticed that whenever your WordPress installation emails you, the email...

Close