Search

Semantic Web technologies and other open standards have the potential of allowing current open datasets and knowledge bases to become more interlinked and interoperable, providing interfaces for end-users to access data via powerful high-level query languages such as SPARQL. As a consequence, it becomes ever more important for these data to be efficiently and easily searchable by non-expert audiences. The challenge of increasing the usability of these database systems, therefore, becomes central. In this thesis, we approach the problem of increasing the usability of querying graph data, in the form of RDF knowledge bases, from different perspectives. We study the learning - more specifically, the reverse engineering - of SPARQL queries, including its theoretical and practical aspects. Along the way we find that a key limitation of these approaches is the completeness of the data, and therefore turn to learning - in this case, knowledge base completion - of RDF data. Specifically, we begin by studying the definability problem for first-order logic, providing exact complexity bounds. We next provide a theoretical study of reverse engineering in the SPARQL context, formalising variants of the reverse engineering problem and giving bounds on their complexity. We develop algorithms for reverse engineering and perform experimental analyses, showing that they scale well. Additionally, we implement and present a proof-of-concept user application which demonstrates how reverse engineering is capable of guiding users who are unfamiliar with both the dataset and with SPARQL to desired queries and result sets based on a query by example paradigm. Finally, to address the issue of the completeness of data, we propose a scalable and ontology-aware graph embedding model which allows for fact inference in RDF datasets, providing a data learning approach that is complementary to query learning.

Nowadays, an increasing amount of media platforms provide the users with opportunities for sharing their opinions about products, companies or people. In order to support users accessing opinion-based information, and to support engineers building systems that require opinion-aware reasoning, intelligent opinion-aware tools and techniques are needed. This thesis contributes methods and technology for opinion-aware information management from two different perspectives, namely document summarisation and knowledge representation. Document summarisation has been widely investigated as a mean to reduce information overload. This thesis focuses on statistical models for summarisation, with a particular attention to divergence-based models, within the context of opinions. Firstly, topic-based document summarisation is addressed, contributing a study on divergence-based document to summary similarity and the definition of a novel algorithm for summarisation based on sentence removal. Secondly, summarisation models are tailored to opinion-oriented content and shown to be useful also when exploited for different tasks such as sentiment classification. Thirdly, summarisation models are applied to knowledge-oriented data, in order to tackle tasks such as entity summarisation. The comprehensive task addressed is the knowledge-based opinion-aware summarisation of content (free text, facts). This thesis also contributes a broad discussion on knowledge representation of opinions. A thorough study on how to model opinions using traditional techniques, such as Entity-Relationship (ER) modelling, underlines that a high-level, opinion-aware layer of conceptual modelling is useful since it hides away implementation details. A conceptual and logical knowledge representation methodology for modelling opinions is hence proposed, with the purpose of guiding engineers towards the use of best practices during the development of sentiment analysis applications. Specifically, an extension of the traditional ER modelling and the definition of an automatic mapping procedure, to translate opinion-aware components of the conceptual model into a relational model, help achieving a clear separation between conceptual and logical modelling. The mapping procedure yields an automatic and replicable methodology to design applications which require opinion-aware reasoning.