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Although the concept of user experience includes two key aspects, experience of meaning (usability) and experience of emotion (affect), the empirical work that measures both the usability and affective aspects of user experience is currently limited. This is particularly important considering that affect could significantly influence a user's perception of usability. This paper uses image schemas to quantitatively and systematically evaluate both these aspects. It proposes a method for evaluating user experience that is based on using image schemas, sentiment analysis, and computational semantics. The aim is to link the sentiments expressed by users during their interactions with a product to the specific image schemas used in the designs. The method involves semantic and sentiment analysis of the verbal responses of the users to identify (i) task-related words linked to the task for which a certain image schema has been used and (ii) affect-related words associated with the image schema employed in the interaction. The main contribution is in linking image schemas with interaction and affect. The originality of the method is twofold. First, it uses a domain-specific ontology of image schemas specifically developed for the needs of this study. Second, it employs a novel ontology-based algorithm that extracts the image schemas employed by the user to complete a specific task and identifies and links the sentiments expressed by the user with the specific image schemas used in the task. The proposed method is evaluated using a case study involving 40 participants who completed a set task with two different products. The results show that the method successfully links the users' experiences to the specific image schemas employed to complete the task. This method facilitates significant improvements in product design practices and usability studies in particular, as it allows qualitative and quantitative evaluation of designs by identifying specific image schemas and product design features that have been positively or negatively received by the users. This allows user experience to be assessed in a systematic way, which leads to a better understanding of the value associated with particular design features. [ABSTRACT FROM AUTHOR]

Trademarks are signs of high reputational value. Thus, they require protection. This paper studies conceptual similarities between trademarks, which occurs when two or more trademarks evoke identical or analogous semantic content. This paper advances the state-of-the-art by proposing a computational approach based on semantics that can be used to compare trademarks for conceptual similarity. A trademark retrieval algorithm is developed that employs natural language processing techniques and an external knowledge source in the form of a lexical ontology. The search and indexing technique developed uses similarity distance, which is derived using Tversky’s theory of similarity. The proposed retrieval algorithm is validated using two resources: a trademark database of 1400 disputed cases and a database of 378 943 company names. The accuracy of the algorithm is estimated using measures from two different domains: the R -precision score, which is commonly used in information retrieval and human judgment/collective human opinion, which is used in human–machine systems. [ABSTRACT FROM PUBLISHER]

The Internet is used by more than two billion customers around the world and is expected to serve as a global platform for interconnecting cyber-physical objects that form the Internet of Things (IoT). Within the next decade, traffic demands are expected to increase a thousand-fold. This challenge can be addressed by introducing and expanding heterogeneous wireless technologies, which provide higher network capacity, wider coverage and higher quality of service (QoS). However, the heterogeneity and complexity of these networks are a major challenge for traditional control and management systems. Therefore, there is a need for self-manageable and self-configurable networks that support the data produced by the different IoT devices and provide opportunities for data analytics. In this work, a cognitive network framework is proposed, in which the network protocol stack is integrated with a semantic system. The proposed framework provides the bases for building smart networks that observe data from different layers in the network protocol stack and represents it in a hierarchical structure in a knowledge base. The framework employs an ontology that provides an abstraction model for the different heterogeneous wireless devices. The ontology determines the relationships between technology-dependent parameters in the network protocol stack and enables, through the use of inferences, the utilization of the observed data from the network. The use of a cognitive network framework with the network protocol stack allows adding ontologies to describe the data, a solution which could solve the problem of analysing, searching or visualising data. [ABSTRACT FROM AUTHOR]

This research addresses the semantic and knowledge gap problem in information retrieval by proposing an ontology-based semantic feature-matching approach, which uses natural language processing, named entity recognition and user-oriented ontologies. The approach comprises four steps: (i) user-oriented ontology building; (ii) semantic feature extraction for identifying information objects; (iii) semantic feature selection using user-oriented ontologies to enhance the semantic representation of the information objects, and (iv) measuring the similarity between the information objects using their enhanced semantic representations. The experiment conducted explores the retrieval performance of the proposed approach and shows that it consistently outperforms its corresponding term-based approach by demonstrating improved precision, recall and F-score. [ABSTRACT FROM AUTHOR]

The aim of document clustering is to produce coherent clusters of similar documents. Clustering algorithms rely on text normalisation techniques to represent and cluster documents. Although most document clustering algorithms perform well in specific knowledge domains, processing cross-domain document repositories is still a challenge. This paper attempts to address this challenge. It investigates the performance of the sk-means clustering algorithm across domains, by comparing the cluster coherence produced with semantic-based and traditional (TF-IDF-based) document representations. The evaluation is conducted on 20 different generic sub-domains of a thousand documents, each randomly selected from the Reuters21578 corpus. The experimental results obtained from the evaluation demonstrate improved coherence of clusters produced by using a semantically enhanced text stemmer (SETS), when compared to the text normalisation obtained with the Porter stemmer. In addition, semantic-based text normalisation is shown to be resistant to noise, which is often introduced in the index aggregation stage, a stage that acquires features to represent documents. [ABSTRACT FROM AUTHOR]

Abstract: One of the challenges in image retrieval is dealing with concepts which have no visual appearance in the images or are not used as keywords in their annotations. To address this problem, this paper proposes an unsupervised concept-based image indexing technique which uses a lexical ontology to extract semantic signatures called ‘semantic chromosomes’ from image annotations. A semantic chromosome is an information structure, which carries the semantic information of an image; it is the semantic signature of an image in a collection expressed through a set of semantic DNA (SDNA), each of them representing a concept. Central to the concept-based indexing technique discussed is the concept disambiguation algorithm developed, which identifies the most relevant ‘semantic DNA’ (SDNA) by measuring the semantic importance of each word/phrase in the annotation. The concept disambiguation algorithm is evaluated using crowdsourcing. The experiments show that the algorithm has better accuracy (79.4%) than the accuracy demonstrated by other unsupervised algorithms (73%) in the 2007 Semeval competition. It is also comparable with the accuracy achieved in the same competition by the supervised algorithms (82–83%) which contrary to the approach proposed in this paper have to be trained with large corpora. The approach is currently applied to the automated generation of mood boards used as an inspirational tool in concept design. [Copyright &y& Elsevier]

Abstract: This research is motivated by the realisation that semantic technology can be used to develop computational tools in support of designers’ creativity by focusing on the inspirational stage of design. The paper describes a semantic-based image retrieval tool developed for the needs of concept cars designers from two renowned European companies. It is created to help them find and interpret sources of inspiration. The core innovation of the tool is its ability to provide a degree of diversity, ambiguity and uncertainty in the information gathering and idea generation process. The tool is based on the assumption that there is a semantic link between the images in a web page and the text around them. Furthermore, it uses the idea that the more frequently a term occurs in a document and the fewer documents it occurs in, the more representative this term is of that document. The new contribution is linking the most meaningful words in a document with ontological concepts, and then finding the most powerful set of concepts representing that document and consequently the images in it. This is based on the observation that monosemic words (with a single meaning) are more domain-oriented than polysemic ones (that have multiple meanings), and provide a greater amount of domain information. The tool tags images by first processing all significant words in the text around them, extracting all keywords and key phrases in it, ranking them according to their significance, and linking them to ontological concepts. It generates a set of concept numbers for each text, which is then used to retrieve information in a process called semantic expansion, where a keyword query is also processed semantically. The proposed approach is illustrated with examples using the tool developed for the needs of Stile Bertone and Fiat, Italy, two of the industrial partners in the TRENDS project sponsored by the European Community. [Copyright &y& Elsevier]