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Publications of year 2021
Articles in journal or book chapters
  1. Shridhar B. Dandin and Mireille Ducassé. ComVisMD-Compact 2D Visualization of Multidimensional Data: Experimenting with Two Different Datasets. In H. Sharma et al., editor, Intelligent Learning for Computer Vision, volume 61 of Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore Pte Ltd, 2021. [WWW] Keyword(s): Tabular data, Visual representation design, Data analysis, Reasoning, Problem solving, Decision making, Data clustering, Aggregation.
    @incollection{dandin2021,
    TITLE = {{ComVisMD-Compact 2D Visualization of Multidimensional Data: Experimenting with Two Different Datasets}},
    AUTHOR = {Dandin, Shridhar B. and Ducassé, Mireille},
    URL = {https://hal.archives-ouvertes.fr/hal-03131685},
    BOOKTITLE = {{Intelligent Learning for Computer Vision}},
    EDITOR = {H. Sharma et al.},
    PUBLISHER = {{Springer Nature Singapore Pte Ltd}},
    SERIES = {Lecture Notes on Data Engineering and Communications Technologies},
    VOLUME = {61},
    YEAR = {2021},
    KEYWORDS = {Tabular data ; Visual representation design ; Data analysis ; Reasoning ; Problem solving ; Decision making ; Data clustering ; Aggregation},
    HAL_ID = {hal-03131685},
    HAL_VERSION = {v1},
    
    }
    


  2. Sébastien Ferré. Conceptual Navigation in Large Knowledge Graphs. In Rokia Missaoui, Leonard Kwuida, and Talel Abdessalem, editors, Complex Data Analysis with Formal Concept Analysis. Springer, 2021. Note: To appear. Keyword(s): knowledge graph, formal concept analysis, Graph-FCA, conceptual navigation.
    Abstract:
    A growing part of Big Data is made of knowledge graphs. Major knowledge graphs such as Wikidata, DBpedia or the Google Knowledge Graph count millions of entities and billions of semantic links. A major challenge is to enable their exploration and querying by end-users. The SPARQL query language is powerful but provides no support for exploration by end-users. Question answering is user-friendly but is limited in expressivity and reliability. Navigation in concept lattices supports exploration but is limited in expressivity and scalability. % In this paper, we introduce a new exploration and querying paradigm, Abstract Conceptual Navigation (ACN), that merges querying and navigation in order to reconcile expressivity, usability, and scalability. ACN is founded on Formal Concept Analysis (FCA) by defining the navigation space as a concept lattice. We then instantiate the ACN paradigm to knowledge graphs (Graph-ACN) by relying on Graph-FCA, an extension of FCA to knowledge graphs. We continue by detailing how Graph-ACN can be efficiently implemented on top of SPARQL endpoints, and how its expressivity can be increased in a modular way. Finally, we present a concrete implementation available online, Sparklis, and a few application cases on large knowledge graphs.

    @InCollection{Fer2021cda_fca,
    author = {Sébastien Ferré},
    title = {Conceptual Navigation in Large Knowledge Graphs},
    booktitle = {Complex Data Analysis with Formal Concept Analysis},
    OPTcrossref = {},
    OPTkey = {},
    publisher = {Springer},
    year = {2021},
    editor = {Rokia Missaoui and Leonard Kwuida and Talel Abdessalem},
    OPTvolume = {},
    OPTnumber = {},
    OPTseries = {},
    OPTtype = {},
    OPTchapter = {},
    OPTpages = {17--44},
    OPTedition = {},
    OPTmonth = {},
    OPTaddress = {},
    note = {To appear},
    OPTannote = {},
    keywords = {knowledge graph, formal concept analysis, Graph-FCA, conceptual navigation},
    abstract = {A growing part of Big Data is made of knowledge graphs. Major knowledge graphs such as Wikidata, DBpedia or the Google Knowledge Graph count millions of entities and billions of semantic links. A major challenge is to enable their exploration and querying by end-users. The SPARQL query language is powerful but provides no support for exploration by end-users. Question answering is user-friendly but is limited in expressivity and reliability. Navigation in concept lattices supports exploration but is limited in expressivity and scalability. % In this paper, we introduce a new exploration and querying paradigm, Abstract Conceptual Navigation (ACN), that merges querying and navigation in order to reconcile expressivity, usability, and scalability. ACN is founded on Formal Concept Analysis (FCA) by defining the navigation space as a concept lattice. We then instantiate the ACN paradigm to knowledge graphs (Graph-ACN) by relying on Graph-FCA, an extension of FCA to knowledge graphs. We continue by detailing how Graph-ACN can be efficiently implemented on top of SPARQL endpoints, and how its expressivity can be increased in a modular way. Finally, we present a concrete implementation available online, Sparklis, and a few application cases on large knowledge graphs.},
    
    }
    



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Last modified: Thu Apr 8 17:20:28 2021
Author: ferre.


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