Showing papers in "Journal of Web Semantics in 2016"

TL;DR: The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

TL;DR: The results substantiate the claim that lightweight interfaces can lower the cost for knowledge publishers compared to more expressive endpoints, while enabling applications to query the publishers' data with the necessary reliability.

TL;DR: This paper presents an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques, and shows how approaching information from news in an event-centric manner can increase the user's understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts.

TL;DR: The DL-Learner framework is described, which supports supervised machine learning using OWL and RDF for background knowledge representation and includes several algorithm implementations, usage examples and has applications building on top of the framework.

TL;DR: This paper identifies well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establishes the computational complexity of answering such queries.

TL;DR: The Graph of Things is a unified integrated and live view for heterogeneous IoT data sources using Linked Data, backed by a scalable and elastic software stack to deal with billions of records of historical and static datasets in conjunction with millions of triples being fetched and enriched to connect to GoT per hour in real time.

TL;DR: In this paper, the authors present an approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source.

TL;DR: YAM++ is described, an ontology matching tool aimed at solving issues of scalability, efficiency, and configuration tuning, and was one of the best ontological matching systems in terms of F -measure.

TL;DR: This paper proposes and evaluates new methods to find the most relevant entity type based on collection statistics and on the knowledge graph structure interconnecting entities and types and shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user.

TL;DR: CroMatcher is presented, an ontology matching system, introducing several novelties to the automated weight calculation process, and a new, iterative method for producing one-to-one final alignment of ontology structures, which is a significant enhancement of similar non-iterative methods proposed in the literature.

TL;DR: The QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP, and a series of steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion.

TL;DR: A new XML Semantic Disambiguation Framework titled XSDF is provided, designed to address each of the above limitations, taking as input: an XML document, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts extracted from a reference machine-readable semantic network.

TL;DR: The principles of benchmark design for instance matching systems are introduced, the dimensions and characteristics of an instance matching benchmark are discussed, and a comprehensive overview of existing benchmarks, as well as benchmark generators are provided.

TL;DR: A novel technique for type inference that extracts types from the free text description of the entity combining lexico-syntactic pattern analysis with supervised classification is introduced.

TL;DR: This work proposes an efficient linking method that uses a random walk strategy to combine a precision-oriented and a recall-oriented classifier in such a way that a high precision is maintained, while recall is elevated to the maximum possible level without affecting precision.

TL;DR: A general method for constructing sar-graphs using a language- and relation-independent, distantly supervised approach which relies solely on the availability of a lexical semantic resource, providing sense information for words, as well as a knowledge base containing seed relation instances.

TL;DR: A detailed theoretical investigation of query approximation, query relaxation, and their combination is undertaken, and for the first time these two disparate notions are combined into a single ‘flex’ operation that simultaneously applies both approximation and relaxation to a query conjunct, providing even greater flexibility for users.

TL;DR: This work builds on the success of existing statistical machine translation approaches, and investigates the impact of different domain adaptation techniques on the task of translating ontology labels, showing that these techniques have a generally positive effect on the quality of translation of the ontology and that, in combination, they provide a significant improvement in quality.

TL;DR: A novel personalized concept-based search mechanism for the Web of Data based on results categorization, combining novel categorization and personalization techniques, and using categorization for providing personalization.

TL;DR: Estoratory Spatio-Temporal Analysis tool was developed to address some of the Linked statistical Data management issues, such as crossing the statistical and the geographical dimensions, producing statistical maps, visualizing different measures, and comparing statistical indicators of different regions through time.

TL;DR: The definition and the computation of Least Common Subsumers (LCSs) in RDF are proposed, and a polynomial-time characterization using a form of graph composition is found that remarkably distinguishes LCSs from Entailment in R DF, which is an NP-complete graph matching problem.

TL;DR: This paper proposes a new approach that employs a decrease-and-conquer strategy for fast SPARQL query processing that can outperform its competitors designed to support named graph queries on RDF quads for a variety of queries.

TL;DR: This paper compares the effect on usability of two important paradigms for ontology-based query interfaces: form-based and graph-based interfaces and observes positive results introducing faceted search and dynamic term suggestion in semantic search interfaces.

TL;DR: It is shown that LDQL is strictly more expressive than the query formalisms that have been proposed previously for Linked Data on the Web, and allows LDQL to define queries for which a complete execution is not computationally feasible over the Web.

TL;DR: This paper proposes a Rule-based Agent-oriented Framework (RbAF) to support the WsSWF execution by combining rule-based knowledge representation with agent technology and describes workflows by messaging reaction rules, which go beyond global Event-Condition-Action rules and support performing complex actions locally within certain contexts.

TL;DR: This paper presents a locking model, which is a variant of multigranularity locking protocol (MGL), to address isolation between transactions that manipulate RDF data and assess the performance of the proposed locking model through model simulation.

TL;DR: This work presents a mobile query service that supports on-the-fly and integrated querying of semantic data, originating from a largely unused portion of the Semantic Web, comprising online RDF files and semantics embedded in annotated webpages.

TL;DR: The versatile PatOMat framework for pattern-based ontology transformation, together with the GUIPOT Protege plugin as its front-end, can be used to fulfill this task, and two different use cases are presented.