BibTex:

    @Article{OJSW_2021v8i1n01_Groppe,
        title     = {Generating Sound from the Processing in Semantic Web Databases},
        author    = {Sven Groppe and
                     Rico Klinckenberg and
                     Benjamin Warnke},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2021},
        volume    = {8},
        number    = {1},
        pages     = {1--27},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2022011618330544843704},
        urn       = {urn:nbn:de:101:1-2022011618330544843704},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Databases process a lot of intermediate steps generating many intermediate results during data processing for answering queries. It is not easy to understand these complex tasks and algorithms for students, developers and all those interested in databases. For this purpose, an additional medium is sonification, which maps data to auditory dimensions and offers a new audible experience to their listeners. Hence, we propose a sonification of query processing paired with a corresponding visualization both integrated in a web application. In a demonstration of our approach and in an extensive user evaluation we show that listeners increase their understanding of the operators' functionality and sonification supports easy remembering of requirements like merge joins work on sorted input. Furthermore, new ways of analyzing query processing are possible with our proposed sonification approach.}
    }

BibTex:

    @Article{OJSW_2020v7i1n01_Holubova,
        title     = {NextGen Multi-Model Databases in Semantic Big Data Architectures},
        author    = {Irena Holubova and
                     Stefanie Scherzinger},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2020},
        volume    = {7},
        number    = {1},
        pages     = {1--16},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020011918332157719390},
        urn       = {urn:nbn:de:101:1-2020011918332157719390},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {When semantic big data is managed in commercial settings, with time, the need may arise to integrate and interlink records from various data sources. In this vision paper, we discuss the potential of a new generation of multi-model database systems as data backends in such settings. Discussing a specific example scenario, we show how this family of database systems allows for agile and flexible schema management. We also identify open research challenges in generating sound triple-views from data stored in interlinked models, as a basis for SPARQL querying. We then conclude with a general overview of multi-model data management systems, to provide a wider scope of the problem domain.}
    }

BibTex:

    @Article{OJSW_2020v7i1n02_Cure,
        title     = {On Distributed SPARQL Query Processing Using Triangles of RDF Triples},
        author    = {Hubert Naacke and
                     Olivier Cur\'{e}},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2020},
        volume    = {7},
        number    = {1},
        pages     = {17--32},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020112218333311672109},
        urn       = {urn:nbn:de:101:1-2020112218333311672109},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Knowledge Graphs are providing valuable functionalities, such as data integration and reasoning, to an increasing number of applications in all kinds of companies. These applications partly depend on the efficiency of a Knowledge Graph management system which is often based on the RDF data model and queried with SPARQL. In this context, query performance is preponderant and relies on an optimizer that usually makes an intensive usage of a large set of indexes. Generally, these indexes correspond to different re-orderings of the subject, predicate and object of a triple pattern. In this work, we present a novel approach that considers indexes formed by a frequently encountered basic graph pattern: triangle of triples. We propose dedicated data structures to store these triangles, provide distributed algorithms to discover and materialize them, including inferred triangles, and detail query optimization techniques, including a data partitioning approach for bias data. We provide an implementation that runs on top of Apache Spark and experiment on two real-world RDF data sets. This evaluation emphasizes the performance boost (up to 40x on query processing) that one can obtain by using our approach when facing triangles of triples.}
    }

BibTex:

    @Article{OJSW_2020v7i1n03_Erekhinskaya,
        title     = {Ten Ways of Leveraging Ontologies for Rapid Natural Language Processing Customization for Multiple Use Cases in Disjoint Domains},
        author    = {Tatiana Erekhinskaya and
                     Marta Tatu and
                     Mithun Balakrishna and
                     Sujal Patel and
                     Dmitry Strebkov and
                     Dan Moldovan},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2020},
        volume    = {7},
        number    = {1},
        pages     = {33--51},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2020112218332779310329},
        urn       = {urn:nbn:de:101:1-2020112218332779310329},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {With the ever-growing adoption of AI technologies by large enterprises, purely data-driven approaches have dominated the field in the recent years. For a single use case, a development process looks simple: agreeing on an annotation schema, labeling the data, and training the models. As the number of use cases and their complexity increases, the development teams face issues with collective governance of the models, scalability and reusablity of data and models. These issues are widely addressed on the engineering side, but not so much on the knowledge side. Ontologies have been a well-researched approach for capturing knowledge and can be used to augment a data-driven methodology. In this paper, we discuss 10 ways of leveraging ontologies for Natural Language Processing (NLP) and its applications. We use ontologies for rapid customization of a NLP pipeline, ontologyrelated standards to power a rule engine and provide standard output format. We also discuss various use cases for medical, enterprise, financial, legal, and security domains, centered around three NLP-based applications: semantic search, question answering and natural language querying.}
    }

BibTex:

    @Article{OJSW_2019v6i1n01_Sutton,
        title     = {Integrity Proofs for RDF Graphs},
        author    = {Andrew Sutton and
                     Reza Samavi},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2019},
        volume    = {6},
        number    = {1},
        pages     = {1--18},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018102818300947746192},
        urn       = {urn:nbn:de:101:1-2018102818300947746192},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Representing open datasets with the RDF model is becoming increasingly popular. An important aspect of this data model is that it can utilize the methods of computing cryptographic hashes to verify the integrity of RDF graphs. In this paper, we first develop a number of metrics to compare the state-of-the-art integrity proof methods and then present two new approaches to generate an integrity proof of RDF datasets: (i) semantic-based and (ii) structure-based. The semantic-based approach leverages timestamps (or other inherent notions of ordering) as an indexing key to construct a sorted Merkle tree variation, where timestamps are semantically extractable from the dataset. The structure-based approach utilizes the redundant structure of large RDF datasets to compress the dataset statements prior to generating a variation of a Merkle tree. We provide a theoretical analysis and an experimental evaluation of our two proposed methods. Compared to the Merkle and sorted Merkle tree, the semantic-based approach achieves faster querying performance for large datasets. The structure-based approach is well suited when RDF datasets contain large amounts of semantic redundancies. We also evaluate our methods' resistance to adversarial threats.}
    }

BibTex:

    @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.}
    }

BibTex:

    @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.}
    }

BibTex:

    @Article{OJSW_2017v4i1n01_Font,
        title     = {Assessing and Improving Domain Knowledge Representation in DBpedia},
        author    = {Ludovic Font and
                     Amal Zouaq and
                     Michel Gagnon},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2017},
        volume    = {4},
        number    = {1},
        pages     = {1--19},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194949},
        urn       = {urn:nbn:de:101:1-201705194949},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {With the development of knowledge graphs and the billions of triples generated on the Linked Data cloud, it is paramount to ensure the quality of data. In this work, we focus on one of the central hubs of the Linked Data cloud, DBpedia. In particular, we assess the quality of DBpedia for domain knowledge representation. Our results show that DBpedia has still much room for improvement in this regard, especially for the description of concepts and their linkage with the DBpedia ontology. Based on this analysis, we leverage open relation extraction and the information already available on DBpedia to partly correct the issue, by providing novel relations extracted from Wikipedia abstracts and discovering entity types using the dbo:type predicate. Our results show that open relation extraction can indeed help enrich domain knowledge representation in DBpedia.}
    }

BibTex:

    @Article{OJSW_2017v4i1n02_Kejriwal,
        title     = {Scalable Generation of Type Embeddings Using the ABox},
        author    = {Mayank Kejriwal and
                     Pedro Szekely},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2017},
        volume    = {4},
        number    = {1},
        pages     = {20--34},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2017100112160},
        urn       = {urn:nbn:de:101:1-2017100112160},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Structured knowledge bases gain their expressive power from both the ABox and TBox. While the ABox is rich in data, the TBox contains the ontological assertions that are often necessary for logical inference. The crucial links between the ABox and the TBox are served by is-a statements (formally a part of the ABox) that connect instances to types, also referred to as classes or concepts. Latent space embedding algorithms, such as RDF2Vec and TransE, have been used to great effect to model instances in the ABox. Such algorithms work well on large-scale knowledge bases like DBpedia and Geonames, as they are robust to noise and are low-dimensional and real-valued. In this paper, we investigate a supervised algorithm for deriving type embeddings in the same latent space as a given set of entity embeddings. We show that our algorithm generalizes to hundreds of types, and via incremental execution, achieves near-linear scaling on graphs with millions of instances and facts. We also present a theoretical foundation for our proposed model, and the means of validating the model. The empirical utility of the embeddings is illustrated on five partitions of the English DBpedia ABox. We use visualization and clustering to show that our embeddings are in good agreement with the manually curated TBox. We also use the embeddings to perform a soft clustering on 4 million DBpedia instances in terms of the 415 types explicitly participating in is-a relationships in the DBpedia ABox. Lastly, we present a set of results obtained by using the embeddings to recommend types for untyped instances. Our method is shown to outperform another feature-agnostic baseline while achieving 15x speedup without any growth in memory usage.}
    }

BibTex:

    @Article{OJSW_2017v4i1n03_Pandey,
        title     = {A Semantic Safety Check System for Emergency Management},
        author    = {Yogesh Pandey and
                     Srividya K. Bansal},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2017},
        volume    = {4},
        number    = {1},
        pages     = {35--50},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201711266890},
        urn       = {urn:nbn:de:101:1-201711266890},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {There has been an exponential growth and availability of both structured and unstructured data that can be leveraged to provide better emergency management in case of natural disasters and humanitarian crises. This paper is an extension of a semantics-based web application for safety check, which uses of semantic web technologies to extract different kinds of relevant data about a natural disaster and alerts its users. The goal of this work is to design and develop a knowledge intensive application that identifies those people that may have been affected due to natural disasters or man-made disasters at any geographical location and notify them with safety instructions. This involves extraction of data from various sources for emergency alerts, weather alerts, and contacts data. The extracted data is integrated using a semantic data model and transformed into semantic data. Semantic reasoning is done through rules and queries. This system is built using front-end web development technologies and at the back-end using semantic web technologies such as RDF, OWL, SPARQL, Apache Jena, TDB, and Apache Fuseki server. We present the details of the overall approach, process of data collection and transformation and the system built. This extended version includes a detailed discussion of the semantic reasoning module, research challenges in building this software system, related work in this area, and future research directions including the incorporation of geospatial components and standards.}
    }

BibTex:

    @Article{OJSW_2016v3i1n01_Peixoto,
        title     = {Hierarchical Multi-Label Classification Using Web Reasoning for Large Datasets},
        author    = {Rafael Peixoto and
                     Thomas Hassan and
                     Christophe Cruz and
                     Aur\'{e}lie Bertaux and
                     Nuno Silva},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2016},
        volume    = {3},
        number    = {1},
        pages     = {1--15},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194907},
        urn       = {urn:nbn:de:101:1-201705194907},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Extracting valuable data among large volumes of data is one of the main challenges in Big Data. In this paper, a Hierarchical Multi-Label Classification process called Semantic HMC is presented. This process aims to extract valuable data from very large data sources, by automatically learning a label hierarchy and classifying data items.The Semantic HMC process is composed of five scalable steps, namely Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct automatically a label hierarchy from statistical analysis of data. This paper focuses on the last two steps which perform item classification according to the label hierarchy. The process is implemented as a scalable and distributed application, and deployed on a Big Data platform. A quality evaluation is described, which compares the approach with multi-label classification algorithms from the state of the art dedicated to the same goal. The Semantic HMC approach outperforms state of the art approaches in some areas.}
    }

BibTex:

    @Article{OJSW_2016v3i1n02_Tatu,
        title     = {A Semantic Question Answering Framework for Large Data Sets},
        author    = {Marta Tatu and
                     Mithun Balakrishna and
                     Steven Werner and
                     Tatiana Erekhinskaya and
                     Dan Moldovan},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2016},
        volume    = {3},
        number    = {1},
        pages     = {16--31},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194921},
        urn       = {urn:nbn:de:101:1-201705194921},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Traditionally, the task of answering natural language questions has involved a keyword-based document retrieval step, followed by in-depth processing of candidate answer documents and paragraphs. This post-processing uses semantics to various degrees. In this article, we describe a purely semantic question answering (QA) framework for large document collections. Our high-precision approach transforms the semantic knowledge extracted from natural language texts into a language-agnostic RDF representation and indexes it into a scalable triplestore. In order to facilitate easy access to the information stored in the RDF semantic index, a user's natural language questions are translated into SPARQL queries that return precise answers back to the user. The robustness of this framework is ensured by the natural language reasoning performed on the RDF store, by the query relaxation procedures, and the answer ranking techniques. The improvements in performance over a regular free text search index-based question answering engine prove that QA systems can benefit greatly from the addition and consumption of deep semantic information.}
    }

BibTex:

    @Article{OJSW_2016v3i1n03_Smid,
        title     = {OnGIS: Semantic Query Broker for Heterogeneous Geospatial Data Sources},
        author    = {Marek Smid and
                     Petr Kremen},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2016},
        volume    = {3},
        number    = {1},
        pages     = {32--50},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194936},
        urn       = {urn:nbn:de:101:1-201705194936},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Querying geospatial data from multiple heterogeneous sources backed by different management technologies poses an interesting problem in the data integration and in the subsequent result interpretation. This paper proposes broker techniques for answering a user's complex spatial query: finding relevant data sources (from a catalogue of data sources) capable of answering the query, eventually splitting the query and finding relevant data sources for the query parts, when no single source suffices. For the purpose, we describe each source with a set of prototypical queries that are algorithmically arranged into a lattice, which makes searching efficient. The proposed algorithms leverage GeoSPARQL query containment enhanced with OWL 2 QL semantics. A prototype is implemented in a system called OnGIS.}
    }

BibTex:

    @Article{OJSW_2015v2i1n01e_Groppe,
        title     = {Semantic and Web: The Semantic Part},
        author    = {Sven Groppe and
                     Paulo Rupino da Cunha},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {1--3},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194864},
        urn       = {urn:nbn:de:101:1-201705194864},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The Web is everywhere in daily life. Business is not possible any more without the fast communication through the web. The knowledge of the humans is reflected in the information accessible in the web. New challenges occur with the flood of information and electronic possibilities for the human being. The current World Wide Web enables an easy, instant access to a vast amount of online information. However, the content in the Web is typically for human consumption, and is not tailored to be machine-processed. The Semantic Web, which is intended to establish a machine-understandable web, thereby offers a promising and potential solution to mining and analyzing web content. The Semantic Web is currently changing from an emergent trend to a technology used in complex real-world applications. This part of the special issue "Semantic and Web" especially investigates how semantic technologies can help the human being to open the new possibilities of the web. The papers, which contribute more to Web technologies, are published in Open Journal of Web Technologies (OJWT).}
    }

BibTex:

    @Article{OJSW_2015v2i1n02_Kachfah,
        title     = {BEAUFORD: A Benchmark for Evaluation of Formalisation of Definitions in OWL},
        author    = {Cheikh Kacfah Emani and
                     Catarina Ferreira Da Silva and
                     Bruno Fi\'{e}s and
                     Parisa Ghodous},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {4--15},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194879},
        urn       = {urn:nbn:de:101:1-201705194879},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {In this paper we present BEAUFORD, a benchmark for methods which aim to provide formal expressions of concepts using the natural language (NL) definition of these concepts. Adding formal expressions of concepts to a given ontology allows reasoners to infer more useful pieces of information or to detect inconsistencies in this given ontology. To the best of our knowledge, BEAUFORD is the first benchmark to tackle this ontology enrichment problem. BEAUFORD allows the breaking down of a given formalisation approach by identifying its key features. In addition, BEAUFORD provides strong mechanisms to evaluate efficiently an approach even in case of ambiguity which is a major challenge in formalisation of NL resources. Indeed, BEAUFORD takes into account the fact that a given NL phrase can be formalised in many ways. Hence, it proposes a suitable specification to represent these multiple formalisations. Taking advantage of this specification, BEAUFORD redefines classical precision and recall and introduces other metrics to take into account the fact that there is not only one unique way to formalise a definition. Finally, BEAUFORD comprises a well-suited dataset to concretely judge of the efficiency of methods of formalisation. Using BEAUFORD, current approaches of formalisation of definitions can be compared accurately using a suitable gold standard.}
    }

BibTex:

    @Article{OJSW_2015v2i1n03_Blasko,
        title     = {Ontology Evolution Using Ontology Templates},
        author    = {Miroslav Blasko and
                     Petr Kremen and
                     Zdenek Kouba},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {16--29},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194898},
        urn       = {urn:nbn:de:101:1-201705194898},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Evolving ontologies by domain experts is difficult and typically cannot be performed without the assistance of an ontology engineer. This process takes long time and often recurrent modeling errors have to be resolved. This paper proposes a technique for creating controlled ontology evolution scenarios that ensure consistency of the possible ontology evolution and give guarrantees to the domain expert that his/her updates do not cause inconsistency. We introduce ontology templates that formalize the notion of controlled evolution and define ontology template consistency checking service together with a consistency checking algorithm. We prove correctness and demonstate the practical use of the techniques in two scenarios.}
    }

BibTex:

    @Article{OJSW_2015v2i1n04_Groppe,
        title     = {Distributed Join Approaches for W3C-Conform SPARQL Endpoints},
        author    = {Sven Groppe and
                     Dennis Heinrich and
                     Stefan Werner},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2015},
        volume    = {2},
        number    = {1},
        pages     = {30--52},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194910},
        urn       = {urn:nbn:de:101:1-201705194910},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Currently many SPARQL endpoints are freely available and accessible without any costs to users: Everyone can submit SPARQL queries to SPARQL endpoints via a standardized protocol, where the queries are processed on the datasets of the SPARQL endpoints and the query results are sent back to the user in a standardized format. As these distributed execution environments for semantic big data (as intersection of semantic data and big data) are freely accessible, the Semantic Web is an ideal playground for big data research. However, when utilizing these distributed execution environments, questions about the performance arise. Especially when several datasets (locally and those residing in SPARQL endpoints) need to be combined, distributed joins need to be computed. In this work we give an overview of the various possibilities of distributed join processing in SPARQL endpoints, which follow the SPARQL specification and hence are "W3C conform". We also introduce new distributed join approaches as variants of the Bitvector-Join and combination of the Semi- and Bitvector-Join. Finally we compare all the existing and newly proposed distributed join approaches for W3C conform SPARQL endpoints in an extensive experimental evaluation.}
    }

BibTex:

    @Article{OJSW-v1i2n01_Li,
        title     = {Developing Knowledge Models of Social Media: A Case Study on LinkedIn},
        author    = {Jinwu Li and
                     Vincent Wade and
                     Melike Sah},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {1--24},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194841},
        urn       = {urn:nbn:de:101:1-201705194841},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {User Generated Content (UGC) exchanged via large Social Network is considered a very important knowledge source about all aspects of the social engagements (e.g. interests, events, personal information, personal preferences, social experience, skills etc.). However this data is inherently unstructured or semi-structured. In this paper, we describe the results of a case study on LinkedIn Ireland public profiles. The study investigated how the available knowledge could be harvested from LinkedIn in a novel way by developing and applying a reusable knowledge model using linked open data vocabularies and semantic web. In addition, the paper discusses the crawling and data normalisation strategies that we developed, so that high quality metadata could be extracted from the LinkedIn public profiles. Apart from the search engine in LinkedIn.com itself, there are no well known publicly available endpoints that allow users to query knowledge concerning the interests of individuals on LinkedIn. In particular, we present a system that extracts and converts information from raw web pages of LinkedIn public profiles into a machine-readable, interoperable format using data mining and Semantic Web technologies. The outcomes of our research can be summarized as follows: (1) A reusable knowledge model which can represent LinkedIn public users and company profiles using linked data vocabularies and structured data, (2) a public SPARQL endpoint to access structured data about Irish industry and public profiles, (3) a scalable data crawling strategy and mashup based data normalisation approach. The proposed data mining and knowledge representation proposed in this paper are evaluated in four ways: (1) We evaluate metadata quality using automated techniques, such as data completeness and data linkage. (2) Data accuracy is evaluated via user studies. In particular, accuracy is evaluated by comparison of manually entered metadata fields and the metadata which was automatically extracted. (3) User perceived metadata quality is measured by asking users to rate the automatically extracted metadata in user studies. (4) Finally, the paper discusses how the extracted metadata suits for a user interface design. Overall, the evaluations show that the extracted metadata is of high quality and meets the requirements of a data visualisation user interface.}
    }

BibTex:

    @Article{OJSW-v1i2n02_Groppe,
        title     = {P-LUPOSDATE: Using Precomputed Bloom Filters to Speed Up SPARQL Processing in the Cloud},
        author    = {Sven Groppe and
                     Thomas Kiencke and
                     Stefan Werner and
                     Dennis Heinrich and
                     Marc Stelzner and
                     Le Gruenwald},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2014},
        volume    = {1},
        number    = {2},
        pages     = {25--55},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194858},
        urn       = {urn:nbn:de:101:1-201705194858},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Increasingly data on the Web is stored in the form of Semantic Web data. Because of today's information overload, it becomes very important to store and query these big datasets in a scalable way and hence in a distributed fashion. Cloud Computing offers such a distributed environment with dynamic reallocation of computing and storing resources based on needs. In this work we introduce a scalable distributed Semantic Web database in the Cloud. In order to reduce the number of (unnecessary) intermediate results early, we apply bloom filters. Instead of computing bloom filters, a time-consuming task during query processing as it has been done traditionally, we precompute the bloom filters as much as possible and store them in the indices besides the data. The experimental results with data sets up to 1 billion triples show that our approach speeds up query processing significantly and sometimes even reduces the processing time to less than half.}
    }

BibTex:

    @Article{OJSW-v1i1n02_Garcia,
        title     = {MapReduce-based Solutions for Scalable SPARQL Querying},
        author    = {Jos\'{e} M. Gim\'{e}nez-Garcia and
                     Javier D. Fern\'{a}ndez and
                     Miguel A. Mart\'{i}nez-Prieto},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2014},
        volume    = {1},
        number    = {1},
        pages     = {1--18},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194824},
        urn       = {urn:nbn:de:101:1-201705194824},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and rconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results.}
    }

BibTex:

    @Article{OJSW-v1i1n03_Delgado,
        title     = {BioSStore: A Client Interface for a Repository of Semantically Annotated Bioinformatics Web Services},
        author    = {Ismael Navas-Delgado and
                     Jos\'{e} F. Aldana-Montes},
        journal   = {Open Journal of Semantic Web (OJSW)},
        issn      = {2199-336X},
        year      = {2014},
        volume    = {1},
        number    = {1},
        pages     = {19--29},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-201705194836},
        urn       = {urn:nbn:de:101:1-201705194836},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {Bioinformatics has shown itself to be a domain in which Web services are being used extensively. In this domain, simple but real services are being developed. Thus, there are huge repositories of real services available (for example BioMOBY main repository includes more than 1500 services). Besides, bioinformatics repositories usually have active communities using and working on improvements. However, these kinds of repositories do not exploit the full potential of Web services (and SOA, Service Oriented Applications, in general). On the other hand, sophisticated technologies have been proposed to improve SOA, including the annotation on Web services to explicitly describe them. However, these approaches are lacking in repositories with real services. In the work presented here, we address the drawbacks present in bioinformatics services and try to improve the current semantic model by introducing the use of the W3C standard Semantic Annotations for WSDL and XML Schema (SAWSDL) and related proposals (WSMO Lite). This paper focuses on a user interface that takes advantage of a repository of semantically annotated bioinformatics Web services. In this way, we exploit semantics for the discovery of Web services, showing how the use of semantics will improve the user searches. The BioSStore is available at http://biosstore.khaos.uma.es. This portal will contain also future developments of this proposal.}
    }