CIDER-CL is a system to perform monolingual and cross-lingual ontology alignment. To that end, several semantic measures are used, such as SoftTFIDF and Cross-Lingual Explicit Semantic Analysis. Artificial neural networks are used to combine elementary similarity computations. CIDER-CL can operate in two modes:
LabelTranslator is a plug-in developed in the NeOn project. This plug-in is implemented for the ontology development tool NeOn Toolkit and its functionality is the localization of terms, classes and properties, of an OWL ontology creating a linguistic repository model, called LIR.
The Lexical Model for ONtologies (lemon) is a model collaboratively developed in the Monnet project and conceived to be a standard for sharing lexical information on the Semantic Web. lemon draws heavily from earlier work of the Monnet members, in particular from LexInfo, LIR and LMF models. lemon aims to be: concise, descriptive (not prescriptive), modular, and RDF-native. More information at http://lemon-model.net/
Recently we have seen a large increase in the amount of geospatial data that is being published using RDF and Linked Data principles. Efforts such as the W3C Geo XG, and most recently the GeoSPARQL initiative are providing the necessary vocabularies to publish this kind of information on the Web of Data. map4rdf is a mapping and faceted browsing tool for exploring and visualizing RDF datasets enhanced with geometrical Information.
The morph suite of technologies (together with their corresponding algorithms), is focused on applying a range of query rewriting techniques over heterogeneous federated data sources, through the use of mappings expressed in the W3C R2RML language.
morph-LDP is an extension of morph-RDB that works with our Linked Data Platform implementation . morph-LDP exposes relational data as read/write Linked Data for LDP-aware applications, whilst allowing legacy applications to continue using their relational databases.
morph-streams is an ontology-based data access system that allows evaluating SPARQL-Stream queries over a range of data streaming systems, which are mapped using the W3C R2RML language. More specifically, the current version of morph-streams provides wrappers for:
OOPS! is a web-based tool, independent of any ontology development environment, for detecting potential pitfalls that could lead to modelling errors. This tool is intended to help ontology developers during the ontology validation activity, which can be divided into diagnosis and repair. Currently, OOPS! provides mechanisms to automatically detect a number of pitfalls, thus helps developers in the diagnosis activity.
Sitemap4rdf is a command-line tool that generates sitemap.xml files for Linked Data sites that have a SPARQL endpoint. Sitemap4rdf queries the endpoint to retrieve a list of all URLs, and generates the sitemap.xml, which then must be uploaded to the site.
So far, Linked Data principles and practices are being adopted by an increasing number of data providers, getting as result a global data space on the Web containing hundreds of LOD datasets. In this context it is important to promote the reuse and linkage of datasets, and to this end, it is necessary to know the structure of datasets. One step forward for knowing in depth the structure of a given dataset is to explore the vocabulary used in the dataset, and how the dataset is actually using such vocabulary.
geometry2rdf is a library for generating RDF files for geometrical information (which could be available in GML or WKT). The GML and WKT is manipulated with GeoTools. The current version of the library works with Oracle geospatial databases and relies on Jena.
Kyrie is a query rewriting system that uses an ontology to rewrite a Datalog query into an different Datalog query that captures the knowledge of the ontology. The rewritten query obtains extensionally from a data source the results (certain answers) that are extensionally or intensionally implied by the original query.
LDP4j is an open source Java-based framework for the development of read-write Linked Data applications based on the W3C Linked Data Platform 1.0 (LDP) specification. LDP4j provides the components required by clients and servers for handling their LDP-based communication, hiding the complexity of the protocol details from application developers and letting them focus on implementing their application-specific business logic.
The LIR is a linguistic proprietary model expected to be published and used with domain ontologies. In itself, it has been implemented as an ontology in OWL. The LIR covers a subset of lexical and terminological description elements that account for the linguistic realization of a domain ontology in different natural languages. Thus, its main purpose is to associate multilingual information to ontologies with the aim of contributing to the Ontology Localization Activity.
morph-GFT is an extension of morph-RDB that works with Google Fusion Table (GFT) tables. These tables can be described using R2RML Mappings and enables users to query them using SPARQL. In morph-GFT, SPARQL queries posed by users are translated into SQL-like queries that are supported by the GFT API.
The OGSA-DAI RDF resource is an extension to OGSA-DAI which adds a new data resource to the framework. The RDF resource extends the existing data resources (relational and XML databases, indexed files) with a resource that provides access to RDF datasets. The access is done either by connecting directly to the database containing the RDF triples (via JenaSDB) or connecting to a SPARQL endpoint at the RDF dataset side. In the figure below it is shown the extension to OGSA-DAI with the new data resource (plus a new resource in development that accesses a RDB2RDF processor).
Sem4Tags is a multilingual tool that given an input tag and a context generates as a result a DBpedia resource representing the tag meaning. The current version is able to process tags in English and Spanish.
This system implements the SPARQL 1.1 Federated Query extension, by extending the OGSA-DAI/DQP system. The optimisation of these federated queries is done in SPARQL-DQP by identifying well-designed patterns in queries, as described in  and . Using such patterns it is possible to apply rewriting rules to federated SPARQL queries and improve the execution time of the queries.