Ubiquitous Computing is moving the interaction away from the human-computer paradigm and towards the creation of smart environments that users and things, from the IoT perspective, interact with. User modeling and adaptation is consistently present having the human user as a constant but pervasive interaction introduces the need for context incorporation towards context-aware smart environments. The current article discusses both aspects of the user modeling and adaptation as well as context awareness and incorporation into the smart home domain. Users are modeled as fuzzy personas and these models are semantically related. Context information is collected via sensors and corresponds to various aspects of the pervasive interaction such as temperature and humidity, but also smart city sensors and services. This context information enhances the smart home environment via the incorporation of user defined home rules. Semantic Web technologies support the knowledge representation of this ecosystem while the overall architecture has been experimentally verified using input from the SmartSantander smart city and applying it to the SandS smart home within FIRE and FIWARE frameworks.
Usually, documents are given in textual form, accompanied by a set of terminological classifications (metadata), based on vocabularies of domain ontologies. This paper presents a novel method for advancing the above classification, by extracting more properties of the analyzed documents. We first extract additional roles from the textual part and together with roles extracted from the ontology statements, we construct an extended document vector representation. We then introduce a pruning algorithm that, for a given document collection, merges concepts of the ontology to produce classes with a sufficient number of corresponding instances. We then classify the documents to ontology classes using the Stanford linear Classifier. Finally, we propose an algorithm that assigns additional concept labels to documents, using the output of the classifier. Our system is evaluated in a set of real data and ontological descriptions and its performance is measured in terms of various accuracy and specificity measures indicates that the proposed approach for documents classification produces correct labels for the majority of items.
We describe a system that performs semantic Question Answering based on the combination of classic Information Retrieval methods with semantic ones. First, we use a search engine to gather web pages and then apply a noun phrase extractor to extract all the candidate answer entities from them. Candidate entities are ranked using a linear combination of two IR measures to pick the most relevant ones. For each one of the top ranked candidate entities we find the corresponding Wikipedia page. We then propose a novel way to exploit Semantic Information contained in the structure of Wikipedia. A vector is built for every entity from Wikipedia category names by splitting and lemmatizing the words that form them. These vectors maintain Semantic Information in the sense that we are given the ability to measure semantic closeness between the entities. Based on this, we apply an intelligent clustering method to the candidate entities and show that candidate entities in the biggest cluster are the most semantically related to the ideal answers to the query. Results on the topics of the TREC 2009 Related Entity Finding task dataset show promising performance.
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