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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 scal-able 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 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, in-completeness and scalability. This paper proposes a selective(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)
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)
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)
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 composi-tional 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)
Distributional semantics focuses on the automatic construction of a semantic model based on the statistical distribution of co-located words in large-scale texts. Deductive reasoning is a fundamental component for semantic understanding. Despite the generality and ex-pressivity of logical models, from an applied perspective, deductive rea-soners are(More)
Linked Data promises an unprecedented availability of data on the Web. However, this vision comes together with the associated challenges of querying highly heterogeneous and distributed data. In order to query Linked Data on the Web today, end-users need to be aware of which datasets potentially contain the data and the data model behind these datasets.(More)