Thesis deals with measurement of positioning in marketing, specifically the method of semantic differential. The main objective of this work is to verify how it changes the semantic profile of selected wine samples tasted by young wine consumers in changing contextual conditions of this experiment. The experiment consists of two separate measurements, during which there is a change in the contextual conditions. The theoretical part is description of his positioning and measurement, which continuously follows the method of semantic differential, where he presented Seman-tick differential as a tool by which we can measure and record the positioning of his value by creating semantic profiles. The practical part explains the connection and answer research questions determined through experiment, where it was shown that the change in the contextual conditions tasted wine samples young wine consumers, changes the semantic profile of the wine samples.

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AIM AND PURPOSE: The aim of this research project is to investigate within-language and cross-language semantic facilitation and interference effects in English-Spanish bilingual persons with aphasia and neurotypical adults. The purpose of the project described in this protocol is to gain insight into how languages are initiated in bilingual speakers who present with aphasia, specifically when presented with semantically related stimuli. METHODS: To achieve this aim, participants wore an eye-tracking device and were presented with an image and four word choices. They were asked to match the picture to the most correct word. The word choices included the correct target word, semantically related words in English or Spanish, and at least two semantically unrelated words. The exact distribution of word type was dependent on the experimental condition. Their trial duration, dwell time per area of interest, and total fixation count per area of interest was collected for each trial and analyzed using mixed linear effects models. RESULTS: The results of this study showed that bilinguals with aphasia (BWA) spent significantly more time on trials that included a semantically related word in Spanish, compared to semantically unrelated words in either language or semantically related words in English. This pattern was not seen in neurologically healthy control participants. We also showed that across group all participants spent more time on the target word compared to semantically related words, however, BWA demonstrated increased fixation measures in trials that included a Spanish semantically related word. This pattern was not seen in neurologically healthy control participants. These results demonstrate increased semantic interference in BWA when compared to neurologically healthy control participants. CONCLUSION: Spanish-English BWA may be more susceptible to cross-language semantic interference compared to neurologically healthy bilingual individuals. However, both BWA and neurologically healthy individuals may experience within language semantic interference.

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The web of data has continued to expand thanks to the principles of Linked Data outlined by Tim Berners-Lee, increasing its impact on the semantic web both in its depth and range of data sources. Meanwhile traditional web applications and technologies, with a strong focus on user interaction, such as blogs, wikis, folksonomies-based systems, and content management systems have become an integral part of the World Wide Web. However the semantic web has not yet managed to fully harness these technologies, resulting in a lack of linked data coming from user-generated content. The high level aim of this thesis is to answer the question of whether semantic web applications can be configured to use existing technologies that encourage usergenerated content on the Web. This thesis proposes an approach to reusing user-generated content from folksonomybased systems in semantic web applications, allowing these applications to be configured to make use of the structure and associated reasoning power of the semantic web, but while being able to reuse the vast amount of data already existing in these folksonomy-based systems. It proposes two new methods of semantic web application development: (i) a reusable infrastructure for building semantic mashup applications that can be configured to make use of the proposed approach; and (ii) a approach to configuring traditional web content management systems (CMS) to maintain repositories of Linked Data. The proposed approach allows semantic web applications to make use of tagged resources, while also addressing some limitations of the folksonomy approach by using ontology reasoning to exploit the structured information held in domain ontologies. The reusable infrastructure provides a set of components to allow semantic web applications to be configured to reuse content from folksonomy-based systems, while also allowing the users of these systems to contribute to the semantic web indirectly via the proposed approach. The proposed Linked Data CMS approach provides a configurable tools for semantic web application developers to develop an entire website based on linked data, while allowing ordinary web users to contribute directly to the semantic web using familiar CMS tools. The approaches proposed in this thesis make use of lightweight ontology reasoning, which is both efficient and scalable, to provide a basis for the development of practical semantic web applications. The research presented in this thesis shows how the semantic web can reuse both folksonomies and content management systems from Web 2.0 to help narrow the gap between these two key areas of the web.

Natural language understanding is a sub-field of natural language processing, which builds automated systems to understand natural language. It is such an ambitious task that it sometimes is referred to as an AI-complete problem, implying that its difficulty is equivalent to solving the central artificial intelligence problem -- making computers as intelligent as people. Despite its complexity, natural language understanding continues to be a fundamental problem in natural language processing in terms of its theoretical and empirical importance. In recent years, startling progress has been made at different levels of natural language processing tasks, which provides great opportunity for deeper natural language understanding. In this thesis, we focus on the task of semantic parsing, which maps a natural language sentence into a complete, formal meaning representation in a meaning representation language. We present two novel state-of-the-art learned syntax-based semantic parsers using statistical syntactic parsing techniques, motivated by the following two reasons. First, the syntax-based semantic parsing is theoretically well-founded in computational semantics. Second, adopting a syntax-based approach allows us to directly leverage the enormous progress made in statistical syntactic parsing. The first semantic parser, Scissor, adopts an integrated syntactic-semantic parsing approach, in which a statistical syntactic parser is augmented with semantic parameters to produce a semantically-augmented parse tree (SAPT). This integrated approach allows both syntactic and semantic information to be available during parsing time to obtain an accurate combined syntactic-semantic analysis. The performance of Scissor is further improved by using discriminative reranking for incorporating non-local features. The second semantic parser, SynSem, exploits an existing syntactic parser to produce disambiguated parse trees that drive the compositional semantic interpretation. This pipeline approach allows semantic parsing to conveniently leverage the most recent progress in statistical syntactic parsing. We report experimental results on two real applications: an interpreter for coaching instructions in robotic soccer and a natural-language database interface, showing that the improvement of Scissor and SynSem over other systems is mainly on long sentences, where the knowledge of syntax given in the form of annotated SAPTs or syntactic parses from an existing parser helps semantic composition. SynSem also significantly improves results with limited training data, and is shown to be robust to syntactic errors. / text

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This thesis is motivated by the need for autonomous agents on the Semantic Web to be able to learn The Semantic Web is an effort for extending the existing Web with machine understandable information, thus enabling intelligent agents to understand the content of web-pages and help users carrying out tasks online. For such autonomous personal agents working on a world wide Semantic Web we make two observations. Firstly, every user is different and the Semantic Web will never cater for them all - - therefore, it is crucial for an agent to be able to learn from the user and the world around it to provide a personalised view of the web. Secondly, due to the immense amounts of information available on the world wide Semantic Web an agent cannot read and process all available data. We argue that to deal with the information overload a goal-directed approach is needed; an agent must be able to reason about the external world, the internal state and the actions available and only carry out the actions that help activate the current goal. In the first part of this thesis we explore the application of two machine learning techniques to Semantic Web data. Firstly, we investigate the classification of Semantic Web resources, we discuss the issues of mapping Semantic Web format to an input representation suitable for a selection of well-known algorithms, and outline the requirements for these algorithms to work well in a Semantic Web context. Secondly, we consider the clustering of Semantic Web resources. Here we focus on the definition of the similarity between two resources, and how we can determine what part of a large Semantic Web graph is relevant to a single resource. In the second part of the thesis we describe our goal-directed learning agent Smeagol. We present explicit definitions of the classification and clustering techniques devised in the first part of the thesis, allowing Smeagol to use a planning approach to create plans of actions that may fulfil a given top-level goal. We also investigate different ways that Smeagol can dynamically replan when steps within the initial plan fail and show that Smeagol can offer plausible learned answers to a given query, even when no explicit correct answer exists.

The massive use of Internet-based connectivity of devices such as smartphones and sensors has led to the emergence of Internet of Things(IoT). Healthcare is one of the areas that IoT-based applications deployment is becoming more successful. However, the deployment of IoT in healthcare faces one major challenge, the selection of IoT devices by stakeholders (for example, patients, caregivers, health professionals and other government agencies) given an amount of available IoT devices based on a disease(for ex-ample, Asthma) or various healthcare scenarios (for example, disease management, prevention and rehabilitation). Since healthcare stakeholders currently do not have enough knowledge about IoT, the IoT devices selection process has to proceed in a way that it allows users to have more detailed information about IoT devices for example, Quality of Service (QoS) parameters, cost, availability(manufacturer), device placement and associated disease. To address this challenge, this thesis work proposes, develops and validates a novel Semantic sEnsor sElection system(iSEE) for healthcare. This thesis also develops iSEE system prototype and Smart Healthcare Ontology(SHO). A Java application is built to allow users for querying our developed SHO in an efficient way.The iSEE system is evaluated based on query response time and the result-set for the queries. Further, we evaluate SHO using Competency Questions(CQs). The conducted evaluations show that our iSEE system can be used efficiently to support stakeholders within the healthcare domain.