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    @Article{OJWT_2018v5i1n05_Blazquez,
        title     = {Consuming Web Data in a Guiding App for Public Bus Users},
        author    = {Miguel \'{A}ngel Garrido Bl\'{a}zquez and
                     Paloma C\'{a}ceres and
                     Bel\'{e}n Vela and
                     Carlos E. Cuesta and
                     Jos\'{e} Mar\'{i}a Cavero Barca and
                     Almudena Sierra-Alonso},
        journal   = {Open Journal of Web Technologies (OJWT)},
        issn      = {2199-188X},
        year      = {2018},
        volume    = {5},
        number    = {1},
        pages     = {31--43},
        note      = {Special Issue: Proceedings of the International Workshop on Web Data Processing \& Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence (KI) in Berlin, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018093019302970779034},
        urn       = {urn:nbn:de:101:1-2018093019302970779034},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The complexity of urban public bus networks in big cities makes their use very difficult. This paper presents Notify.me, a set of pervasive services for mobility that employs open data from the public bus network in Madrid. Our solution provides both a guiding service to assist users travelling by bus and a notifying service (visual, acoustical and sensorial) that informs them when a relevant point on their route has been reached (transfer or destination). Notify.me needs a starting point, which can be the user's current location, a destination and the preferences regarding the best route for the user. Notify.me requests a route from the Madrid public bus company via SOAP Web services. The back-end responds with the calculated route, the user's route, which includes the bus lines, the transfers and the pedestrian routes needed to reach the destination. Finally, an empirical evaluation of the experiences of users who employed Notify.me is presented.}
    }
    @Article{OJWT_2020v7i1n01_Caceres,
        title     = {Adding Semantics to Enrich Public Transport and Accessibility Data from the Web},
        author    = {Paloma C\'{a}ceres and
                     Almudena Sierra-Alonso and
                     Bel\'{e}n Vela and
                     Jos\'{e} Mar\'{i}a Cavero and
                     Miguel \'{A}ngel Garrido and
                     Carlos E. Cuesta},
        journal   = {Open Journal of Web Technologies (OJWT)},
        issn      = {2199-188X},
        year      = {2020},
        volume    = {7},
        number    = {1},
        pages     = {1--18},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020011918333806107393},
        urn       = {urn:nbn:de:101:1-2020011918333806107393},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Web technologies and open data practices have now begun to promote new issues and services addressed to both final and specialized users. The smart cities initiative has also introduced new trends and ideas to offer to the public, one of which is the challenge of a more inclusive society that will provide the same opportunities for all. One of the major areas that could benefit from these new initiatives is public transport by, for example, providing open and accessible datasets, which include information by and about people with special needs. In this sense, the Google Transit Feed Specification (GTFS) defines a format to describe public transportation and associated geographic information. It includes details regarding accessibility and what people with special needs might require to get around using public transport. We are, however, of the opinion that this specification has a low granularity and is not sufficient, since it only takes into account only mobility needs. As suggestions for improvement, we propose to enrich GTFS data by combining public transport data from multiple Web sources with semantic metadata techniques. Those data are stored in a public semantic dataset. To define this dataset, we propose a systematic method to extract data from different sources and integrate them. This method is applied to obtain data about the metro system from the website of Metro Madrid and GTFS. Relevant SPARQL queries and two applications are developed to evaluate the usefulness of the dataset obtained.}
    }