% This data is distributed under the terms of the Open Data Commons Attribution License (ODC-By) v1.0 - See more at: http://opendatacommons.org/licenses/by/1-0/
% All issues of Volume 5
% Volume 5, Issue 1, 2018(In Progress)
    @Article{OJSW_2018v5i1n01_Kostov,
        title     = {Count Distinct Semantic Queries over Multiple Linked Datasets},
        author    = {Bogdan Kostov and
                     Petr Kremen},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2018},
        volume    = {5},
        number    = {1},
        pages     = {1--11},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201712245426},
        urn       = {urn:nbn:de:101:1-201712245426},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In this paper, we revise count distinct queries and their semantics over datasets with incomplete knowledge, which is a typical case for the linked data integration scenario where datasets are viewed as ontologies. We focus on counting individuals present in the signature of the ontology. Specifically, we investigate the Certain Epistemic Count (CEC) and the Possible Epistemic Count (PEC) interval based semantics. In the case of CEC semantics, we propose an algorithm for its evaluation and we prove its correctness under a practical constraint of the queried ontology. We conduct and report experiments with the implementation of the proposed algorithm. We also prove decidability of the PEC semantics.}
    }
    @Article{OJSW_2018v5i1n02_Cbabchoub,
        title     = {FICLONE: Improving DBpedia Spotlight Using Named Entity Recognition and Collective Disambiguation},
        author    = {Mohamed Chabchoub and
                     Michel Gagnon and
                     Amal Zouaq},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2018},
        volume    = {5},
        number    = {1},
        pages     = {12--28},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018080519301478077663},
        urn       = {urn:nbn:de:101:1-2018080519301478077663},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In this paper we present FICLONE, which aims to improve the performance of DBpedia Spotlight, not only for the task of semantic annotation (SA), but also for the sub-task of named entity disambiguation (NED). To achieve this aim, first we enhance the spotting phase by combining a named entity recognition system (Stanford NER ) with the results of DBpedia Spotlight. Second, we improve the disambiguation phase by using coreference resolution and exploiting a lexicon that associates a list of potential entities of Wikipedia to surface forms. Finally, to select the correct entity among the candidates found for one mention, FICLONE relies on collective disambiguation, an approach that has proved successful in many other annotators, and that takes into consideration the other mentions in the text. Our experiments show that FICLONE not only substantially improves the performance of DBpedia Spotlight for the NED sub-task but also generally outperforms other state-of-the-art systems. For the SA sub-task, FICLONE also outperforms DBpedia Spotlight against the dataset provided by the DBpedia Spotlight team.}
    }