João Carlos Pereira da Silva

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This paper describes Treo, a natural language query mechanism for Linked Data which focuses on the provision of a precise and scalable semantic matching approach between natural language queries and distributed heterogeneous Linked Datasets. Treo’s semantic matching approach combines three key elements: entity search, a Wikipedia-based semantic relatedness(More)
Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked Data on the Web, should be able to search and query data spread over potentially large numbers of heterogeneous, complex and distributed datasets. Ideally, a query mechanism for Linked Data should abstract users from the representation(More)
Linked Data brings the promise of incorporating a new dimension to the Web where the availability of Web-scale data can determine a paradigmatic transformation of the Web and its applications. However, together with its opportunities, Linked Data brings inherent challenges in the way users and applications consume the available data. Users consuming Linked(More)
Tasks such as question answering and semantic search are dependent on the ability of querying & reasoning over large-scale commonsense knowledge bases (KBs). However, dealing with commonsense data demands coping with problems such as the increase in schema complexity, semantic inconsistency, incompleteness and scalability. This paper proposes a selective(More)
The integration of a small fraction of the information present in the Web of Documents to the Linked Data Web can provide a significant shift on the amount of information available to data consumers. However, information extracted from text does not easily fit into the usually highly normalized structure of ontology-based datasets. While the representation(More)
This demo presents Graphia, an information extraction pipeline targeting an RDF representation of unstructured data in the form of structured discourse graphs (SDGs). It combines natural language processing and information extraction techniques with the use of linked open data resources and semantic web technologies to enable discourse representation as a(More)
Most information extraction approaches available today have either focused on the extraction of simple relations or in scenarios where data extracted from texts should be normalized into a database schema or ontology. Some relevant information present in natural language texts, however, can be irregular, highly contextualized, with complex semantic(More)
Distributional semantics focuses on the automatic construction of a semantic model based on the statistical distribution of colocated words in large-scale texts. Deductive reasoning is a fundamental component for semantic understanding. Despite the generality and expressivity of logical models, from an applied perspective, deductive reasoners are dependent(More)
The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic or vocabulary-independent, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the(More)
Natural language descriptors used for categorizations are present from folksonomies to ontologies. While some descriptors are composed of simple expressions, other descriptors have complex compositional patterns (e.g. ‘French Senators Of The Second Empire’, ‘Churches Destroyed In The Great Fire Of London And Not Rebuilt’). As conceptual models get more(More)