In this work, we consider that a close collaboration between the research fields of Natural Language Processing and Knowledge Representation becomes essential to fulfill the vision of the Semantic Web. This will permit to retrieve information from vast amount of textual documents present on the Web and to represent these extractions in an amenable manner for querying and reasoning purposes. In such a context, uncertain, incomplete and ambiguous information must be handled properly. In the following, we present a solution that enables to qualify and quantify the uncertainty of extracted information from linguistic treatment.

Fadhela Kerdjoudj - GeolSemantics, France
Olivier Curé - University of Paris-Est Marne-la-vallée, France

The complexity of software systems is growing and the computation of several software quality metrics is challenging. Therefore, being able to use the already estimated quality metrics to predict their evolution is a crucial task. In this paper we outline our idea to use Linked Open Data to enrich the information available for such prediction. We report our experience so far, and we outline the preliminary results obtained.

Davide Ceolin - VU University Amsterdam, The Netherlands
Till Döohmen - Software Improvement Group, The Netherlands
Joost Visser - Software Improvement Group, The Netherlands

We study the problem of exchanging probabilistic data between ontology-based probabilistic databases. The probabilities of the probabilistic source databases are compactly encoded via Boolean formulas with the variables adhering to the dependencies imposed by a Bayesian network, which are closely related to the management of provenance. For the ontologies and the ontology mappings, we consider different kinds of existential rules from the Datalog+/-- family. We provide a complete picture of the computational complexity of the problem of deciding whether there exists a probabilistic (universal) solution for a given probabilistic source database relative to a (probabilistic) ontological data exchange problem. We also analyze the complexity of answering UCQs (unions of conjunctive queries) in this framework.

Thomas Lukasiewicz - University of Oxford, UK
Maria Vanina Martinez - Univ. Nacional del Sur and CONICET, Argentina
Livia Predoiu - Otto-von-Guericke Universität Magdeburg, Germany
Gerardo I. Simari - Univ. Nacional del Sur and CONICET, Argentina

Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of predicting missing linksin large knowledge graphs, so to discover new facts about the world. Recently, representation learning models that embed entities and predicates in continuous vector spaces achieved new state-of-the-art results on this problem. A major limitation in these models is that the training process, which consists in learning the optimal entity and predicate embeddings for a given knowledge graph, can be very computationally expensive: it may even require days of computations for large knowledge graphs. In this work, by leveraging adaptive learning rates, we propose a principled method for reducing the training time by an order of magnitude, while learning more accurate link prediction models. Furthermore, we employ the proposed training method for evaluating a set of novel and scalable models. Our evaluations show significant improvements over state-of-the-art link prediction methods on the WordNet and Freebase datasets.

Pasquale Minervini - University of Bari, Italy
Claudia d'Amato - University of Bari, Italy
Nicola Fanizzi - University of Bari, Italy
Floriana Esposito - University of Bari, Italy

Probabilistic OWL (PR-OWL) improves the Web Ontology Language (OWL) with the ability to treat uncertainty using Multi-Entity Bayesian Networks (MEBN). PR-OWL 2 presents a better integration with OWL and its underlying logic, allowing the creation of ontologies with probabilistic and deterministic parts. PR-OWL 2, however, has scalability problems since it is built upon OWL 2 DL, a version of OWL based on description logic SROIQ(D), which is known to have high complexity. To address this issue, this paper proposes PR-OWL 2 RL, a scalable version of PR-OWL with OWL 2 RL profile and triplestores (databases based on RDF triples). OWL 2 RL allows reasoning in polynomial time for the main reasoning tasks. The paper also presents First-Order expressions accepted by this new language and analyze its expressive power, making a comparison with the previous language and showing in which kinds of problems each version is more suitable.

Laécio Lima Dos Santos - University of Brasilia, Brazil
Rommel Novaes Carvalho - Brazil's Office of the Comptroller General, Brazil
Marcelo Ladeira - University of Brasilia, Brazil
Li Weigang - University of Brasilia, Brazil
Gilson Libório de Oliveira Mendes - Brazil's Office of the Comptroller General, Brazil

Fuzzy ontologies allow the representation of imprecise structured knowledge, typical in many real-world application domains. A key factor in the practical success of fuzzy ontologies is the availability of highly optimized reasoners. This short paper discusses a novel optimization technique: a reduction of the size of the optimization problems obtained during the inference by the fuzzy ontology reasoner fuzzyDL.

Fernando Bobillo - University of Zaragoza, Spain Umberto Straccia - ISTI-CNR, Italy