An Online Tool for Inspecting Datasets in the Linked Data Cloud (LOUPE)
Loupe is a tool that helps you to inspect a dataset to understand which vocabularies (classes, and properties) are used including statistics and frequent triple patterns. Starting to from the high-level statistics, Loupe allows you to zoom into details down to the corresponding triples. The tool has been developed by the Ontology Engineering Group and presented at ISWC2015.


System for Heterogeneous mobilE Requests by Leveraging Ontological and Contextual Knowledge (SHERLOCK)
Nowadays people are exposed to huge amounts of information that are generated continuously. However, current mobile applications, Web pages, and Location-Based Services (LBSs) are designed for specific scenarios and goals. We propose the system SHERLOCK, which searches and shares up-to-date knowledge from nearby devices to relieve the user from knowing and managing such knowledge directly. Besides, the system guides the user in the process of selecting the service that best fits his/her needs in the given context.


Android Goes Semantic!
The massive spread of mobile computing in our daily lives has attracted a huge community of mobile apps developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, knowledge decoupling, etc.) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of inferring logical consequences from the knowledge considered. However, using semantic APIs and reasoners on current Android-based devices is not problem-free and, currently, there are no remarkable efforts to enable mobile devices with semantic reasoning capabilities. In our work, we analyze whether the most popular current available DL reasoners can be used on Android-based devices. We evaluate the efforts needed to port them to the Android platform, taking into account its limitations, and present some tests to show the performance of these reasoners on current smartphones/tablets.


Query Gen
QueryGen is our approach to perform semantic keyword-based search on heterogeneous information systems. It is the result of putting together different semantic techniques developed in our research group.


GENeric Information Extraction Framework (GENIE)
GENIE is an architectural proposal that implements a set of components which objective is to provide tools to make easier information extraction with easily accessible formats using Machine Learning, IA Technics, Natural Language Processing tools and Semantic Methods.