Enhancing visibility in EPCIS governing agri-food supply chains via linked pedigrees

Data integration for the purposes of tracking, tracing and transparency are important challenges in the agri-food supply chain. The Electronic Product Code Information Services (EPCIS) is an event-oriented GS1 standard that aims to enable tracking and tracing of products through the sharing of event-based datasets that encapsulate the Electronic Product Code (EPC). In this paper, the authors propose a framework that utilises events and EPCs in the generation of "linked pedigrees" - linked datasets that enable the sharing of traceability information about products as they move along the supply chain. The authors exploit two ontology based information models, EEM and CBVVocab within a distributed and decentralised framework that consumes real time EPCIS events as linked data to generate the linked pedigrees. The authors exemplify the usage of linked pedigrees within the fresh fruit and vegetables supply chain in the agri-food sector.

Adapting sentiment lexicons using contextual semantics for sentiment analysis of twitter

Sentiment lexicons for sentiment analysis offer a simple, yet effective way to obtain the prior sentiment information of opinionated words in texts. However, words’ sentiment orientations and strengths often change throughout various contexts in which the words appear. In this paper, we propose a lexicon adaptation approach that uses the contextual semantics of words to capture their contexts in tweet messages and update their prior sentiment orientations and/or strengths accordingly. We evaluate our approach on one state-of-the-art sentiment lexicon using three different Twitter datasets. Results show that the sentiment lexicons adapted by our approach outperform the original lexicon in accuracy and F-measure in two datasets, but give similar accuracy and slightly lower F-measure in one dataset.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.

Stretching the life of Twitter classifiers with time-stamped semantic graphs

Social media has become an effective channel for communicating both trends and public opinion on current events. However the automatic topic classification of social media content pose various challenges. Topic classification is a common technique used for automatically capturing themes that emerge from social media streams. However, such techniques are sensitive to the evolution of topics when new event-dependent vocabularies start to emerge (e.g., Crimea becoming relevant to War Conflict during the Ukraine crisis in 2014). Therefore, traditional supervised classification methods which rely on labelled data could rapidly become outdated. In this paper we propose a novel transfer learning approach to address the classification task of new data when the only available labelled data belong to a previous epoch. This approach relies on the incorporation of knowledge from DBpedia graphs. Our findings show promising results in understanding how features age, and how semantic features can support the evolution of topic classifiers.

SentiCircles for contextual and conceptual semantic sentiment analysis of Twitter

Lexicon-based approaches to Twitter sentiment analysis are gaining much popularity due to their simplicity, domain independence, and relatively good performance. These approaches rely on sentiment lexicons, where a collection of words are marked with fixed sentiment polarities. However, words' sentiment orientation (positive, neural, negative) and/or sentiment strengths could change depending on context and targeted entities. In this paper we present SentiCircle; a novel lexicon-based approach that takes into account the contextual and conceptual semantics of words when calculating their sentiment orientation and strength in Twitter. We evaluate our approach on three Twitter datasets using three different sentiment lexicons. Results show that our approach significantly outperforms two lexicon baselines. Results are competitive but inconclusive when comparing to state-of-art SentiStrength, and vary from one dataset to another. SentiCircle outperforms SentiStrength in accuracy on average, but falls marginally behind in F-measure. © 2014 Springer International Publishing.

Semantic representation for comparison of performance measurement indicators

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Preface

The Semantic Web has come a long way since its inception in 2001, especially in terms of technical development and research progress. However, adoption by non- technical practitioners is still an ongoing process, and in some areas this process is just now starting. Emergency response is an area where reliability and timeliness of information and technologies is of essence. Therefore it is quite natural that more widespread adoption in this area has not been seen until now, when Semantic Web technologies are mature enough to support the high requirements of the application area. Nevertheless, to leverage the full potential of Semantic Web research results for this application area, there is need for an arena where practitioners and researchers can meet and exchange ideas and results. Our intention is for this workshop, and hopefully coming workshops in the same series, to be such an arena for discussion. The Extended Semantic Web Conference (ESWC - formerly the European Semantic Web conference) is one of the major research conferences in the Semantic Web field, whereas this is a suitable location for this workshop in order to discuss the application of Semantic Web technology to our specific area of applications. Hence, we chose to arrange our first SMILE workshop at ESWC 2013. However, this workshop does not focus solely on semantic technologies for emergency response, but rather Semantic Web technologies in combination with technologies and principles for what is sometimes called the "social web". Social media has already been used successfully in many cases, as a tool for supporting emergency response. The aim of this workshop is therefore to take this to the next level and answer questions like: "how can we make sense of, and furthermore make use of, all the data that is produced by different kinds of social media platforms in an emergency situation?" For the first edition of this workshop the chairs collected the following main topics of interest: • Semantic Annotation for understanding the content and context of social media streams. • Integration of Social Media with Linked Data. • Interactive Interfaces and visual analytics methodologies for managing multiple large-scale, dynamic, evolving datasets. • Stream reasoning and event detection. • Social Data Mining. • Collaborative tools and services for Citizens, Organisations, Communities. • Privacy, ethics, trustworthiness and legal issues in the Social Semantic Web. • Use case analysis, with specific interest for use cases that involve the application of Social Media and Linked Data methodologies in real-life scenarios. All of these, applied in the context of: • Crisis and Disaster Management • Emergency Response • Security and Citizen Journalism The workshop received 6 high-quality paper submissions and based on a thorough review process, thanks to our program committee, the decision was made to accept four of these papers for the workshop (67% acceptance rate). These four papers can be found later in this proceedings volume. Three out of four of these papers particularly discuss the integration and analysis of social media data, using Semantic Web technologies, e.g. for detecting complex events in social media streams, for visualizing and analysing sentiments with respect to certain topics in social media, or for detecting small-scale incidents entirely through the use of social media information. Finally, the fourth paper presents an architecture for using Semantic Web technologies in resource management during a disaster. Additionally, the workshop featured an invited keynote speech by Dr. Tomi Kauppinen from Aalto university. Dr. Kauppinen shared experiences from his work on applying Semantic Web technologies to application fields such as geoinformatics and scientific research, i.e. so-called Linked Science, but also recent ideas and applications in the emergency response field. His input was also highly valuable for the roadmapping discussion, which was held at the end of the workshop. A separate summary of the roadmapping session can be found at the end of these proceedings. Finally, we would like to thank our invited speaker Dr. Tomi Kauppinen, all our program committee members, as well as the workshop chair of ESWC2013, Johanna Völker (University of Mannheim), for helping us to make this first SMILE workshop a highly interesting and successful event!

Majestic Gamification:a case study in the adoption of a service innovation

Purpose: A case study is presented concerning a gamified awards system designed to encourage software users to explore a suite of tools, and to share their expertise level in profile pages. Majestic is a high-tech business based in the West Midlands (UK) w hich offers a Link Intelligence database using a Software as a Service (SaaS) business model. Customers leverage the database for tasks including Search Engine Optimisation (SEO) by using a suite of web-based tools. Getting to know all the tools and how they can be deployed to good effect represents a considerable learning challenge, and Majestic were aware that. Design/methodology/approach: We present the development of Majestic Awards as a case study highlighting the most important design decisions. Then we reflect on the development process as an example of innovation adoption, thereby identifying resources and cu ltura l factors which were critical in ensuring the success of the project. Findings: The gamified awards system makes learning the tools an enjoyable, explorative experience. Success factors included identifying a clear business goal, the process/ project f it, senior management buy in, and identifying the knowledge and resources to resolve t echnical issues. Originality/value: Prior to gamification of the system, only the most expert users regu larly utilized all the tools. The user base is now more knowl edgable about the system and some users choose to use the system to publicize their expertise.

Web designers, social e-value creation and e-business planning:understanding resistance from conflicting demands

This chapter investigates the conflicting demands faced by web designers in the development of social e-atmospherics that aim to encourage e-value creation, thus strengthening and prolonging market planning strategies. While recent studies have shown that significant shifts are occurring concerning the importance of users’ generated content by way of social e-communication tools (e.g. blogs), these trends are also creating expectations that social and cultural cues ought to become a greater part of e-atmospherics and e-business strategies. Yet, there is growing evidence that organizations are resisting such efforts, fearing that they will lose control of their e-marketing strategy. This chapter contributes to the theory and literature on online cross-cultural understanding and the impact website designers (meso-level) can have on improving the sustainability of e-business planning, departing from recent studies that focus mainly on firms’ e-business plans (macro-level) or final consumers (micro-level). A second contribution is made with respect to online behavior regarding the advancement of technologies that facilitate the development and shaping of new social e-atmospherics that affect users’ behavior and long term e-business strategies through the avoidance of traditional, formal decision making processes and marketing strategy mechanisms implemented by firms. These issues have been highlighted in the literature on the co-production and co-creation of value, which few organizations have thus far integrated in their strategic and pragmatic e-business plans. Drawing upon fifteen online interviews with web designers in the USA, as key non-institutional actors at the meso-level who are developing what future websites will be like, this chapter analyzes ways in which identifying points of resistance and conflicting demands can lead to engagement with the debate over the online co-creation of value and more sustainable future e-business planning. A number of points of resistance to the inclusion of more e-social atmospherics are identified, and the implications for web designers’ roles and web design planning are discussed along with the limitations of the study and potential future research for e-business studies.

Visualisation and browsing of image databases

In this chapter we provide a comprehensive overview of the emerging field of visualising and browsing image databases. We start with a brief introduction to content-based image retrieval and the traditional query-by-example search paradigm that many retrieval systems employ. We specify the problems associated with this type of interface, such as users not being able to formulate a query due to not having a target image or concept in mind. The idea of browsing systems is then introduced as a means to combat these issues, harnessing the cognitive power of the human mind in order to speed up image retrieval.We detail common methods in which the often high-dimensional feature data extracted from images can be used to visualise image databases in an intuitive way. Systems using dimensionality reduction techniques, such as multi-dimensional scaling, are reviewed along with those that cluster images using either divisive or agglomerative techniques as well as graph-based visualisations. While visualisation of an image collection is useful for providing an overview of the contained images, it forms only part of an image database navigation system. We therefore also present various methods provided by these systems to allow for interactive browsing of these datasets. A further area we explore are user studies of systems and visualisations where we look at the different evaluations undertaken in order to test usability and compare systems, and highlight the key findings from these studies. We conclude the chapter with several recommendations for future work in this area. © 2011 Springer-Verlag Berlin Heidelberg.

Semantic enabled sensor network design

Wireless Sensor Network (WSN) systems have become more and more popular in our modern life. They have been widely used in many areas, such as smart homes/buildings, context-aware devices, military applications, etc. Despite the increasing usage, there is a lack of formal description and automated verification for WSN system design. In this paper, we present an approach to support the rigorous verification of WSN modeling using the Semantic Web technology We use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) to define a meta-ontology for the modeling of WSN systems. Furthermore, we apply ontology reasoners to perform automated verification on customized WSN models and their instances. We demonstrate and evaluate our approach through a Light Control System (LCS) as the case study.

DFIG machine design for maximizing power output based on surrogate optimization algorithm

This paper presents a surrogate-model-based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine's previous operational performance, the DFIG's stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization-based surrogate optimization techniques are used in conjunction with the finite element method to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Representing clinical documents to support automatic retrieval of evidence from the Cochrane Library

The overall aim of our research is to develop a clinical information retrieval system that retrieves systematic reviews and underlying clinical studies from the Cochrane Library to support physician decision making. We believe that in order to accomplish this goal we need to develop a mechanism for effectively representing documents that will be retrieved by the application. Therefore, as a first step in developing the retrieval application we have developed a methodology that semi-automatically generates high quality indices and applies them as descriptors to documents from The Cochrane Library. In this paper we present a description and implementation of the automatic indexing methodology and an evaluation that demonstrates that enhanced document representation results in the retrieval of relevant documents for clinical queries. We argue that the evaluation of information retrieval applications should also include an evaluation of the quality of the representation of documents that may be retrieved. ©2010 IEEE.