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The increasing size and availability of web data make data quality a core challenge in many applications. Principles of data quality are recognized as essential to ensure that data fit for their intended use in operations, decision-making, and planning. However, with the rise of the Semantic Web, new data quality issues appear and require deeper consideration. In this paper, we propose to extend the...
In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the definition of new features to be derived from the data model. These problems can be cast as regression problems so that suitable solutions can be devised...
An increasing amount of data are published and consumed on the Web according to the Linked Data paradigm. In such scenario, understanding if the data consumed are up-to-date is crucial. Outdated data are usually considered inappropriate for many crucial tasks, such as make the consumer confident that answers returned to a query are still valid at the time the query is formulated. In this paper we...
The paper tackles the problem of mining linked open data. The inherent lack of knowledge caused by the open-world assumption made on the semantic of the data model determines an abundance of data of uncertain classification. We present a semi-supervised machine learning approach. Specifically a self-training strategy is adopted which iteratively uses labeled instances to predict a label also for unlabeled...
The Internet of Things refers to extending the Internet to physical entities of interest (EoI) to humans (e.g. a table, a room or another human being) sensed as a set of properties that can be observed, measured, accessed or triggered by devices such as actuators, sensors or other smart components. In this vision, the IoT foresees novel types of applications dynamically finding the associations between...
The Semantic Web of Things is a novel paradigm combining the Semantic Web and the Internet of Things, aiming to associate semantic annotations to real-world objects, locations and events. This paper presents a general framework for the Semantic Web of Things, based on an evolution of classic Knowledge Base models, also providing architectural solutions for information storage, communication and processing.
This paper describes an approach for improving semantic queries by utilizing Universal Words (UWs) and a graph database. Concept Description Language (CDL) is used for representing the semantic data, and Neo4j graph database is used as the storage back-end. Cypher graph query language is used as the basis for implementing the semantic queries. For improving the queries, query expansion is performed...
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