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The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems.

The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (named entity recognition). The semantic extraction rules are constructed based on defined language models and the developed ontology.

The proposed method is implemented as a part of the sport news semantic annotations-generating prototype BKAnnotation. This component is a part of the sport integration system based on Web Semantics BKSport. The semantic annotations generated are used for improving features of news searching – sorting – association. The experiments on the news data from SkySport (2014) channel showed positive results. The precisions achieved in both cases, with and without integration of the pronoun recognition method, are both over 80 per cent. In particular, the latter helps increase the recall value to around 10 per cent.

This is one of the initial proposals in automatic creation of semantic data about news, football news in particular and sport news in general. The combination of ontology, knowledge base and patterns of language model allows detection of not only entities with corresponding types but also semantic triples. At the same time, the authors propose a pronoun recognition method using extraction rules to improve the relation recognition process.

The design of tests is a very important step in the software development process because it allows us to match the users’ expectations with the finished product. Considered as a cumbersome activity, efforts have been made to automatize and alleviate the burden of test generation, but it is still a largely neglected step. The study aims to propose taking advantage of existing requirement artifacts, like scenarios that describe the dynamic of the domain in a very early stage of software development, to obtain tests from them.

In particular, the approach proposed complement the scenarios that are textually described with a glossary, the language extended lexicon. Thus, a set of rules to derive tests from scenarios is also proposed. The tests are then described using the task/method model.

The main findings of this study consist of an extension of a previously presented set of rules. And, a tool based on a media wiki platform that makes possible to record scenarios and the language extended lexicon and implement the rules to obtain the tests.

The main originality of this study is the glossary which complements scenarios, the semantic support to obtain tests and the tool to automatize the approach.

This chapter explores the role of language in the relationship between diversity and team performance. Specifically, we consider how a linguistic approach to social categorization may be used to study the social psychological mechanisms that underlie diversity effects. Using the results of a study examining the effects of gender, ethnicity and tenure on language abstraction, we consider the potential implications for team processes and effectiveness. In addition, we propose a revised team input-process-output model that highlights the potential effects of language on team processes. We conclude by suggesting directions for future research linking diversity, linguistic categorization, and team effectiveness.

The expression of conceptual syntagmatic relationships in document retrieval systems holds out hope for both increased discrimination (generally) and increased recall (in certain contexts). The inclusion of such relationships in retrieval systems requires both a structured inventory of relationships and some means of expressing them; this article examines the latter. To be fully effective, the expression of conceptual syntagmatic relationships must comply with criteria of systematicity, complexity, efficiency and naturalness. Unfortunately, the complex interaction of natural language means of expressing these relationships (lexicalisation, word order, function words and morphosyntactic cases) causes them to fail the systematicity criterion. Most document retrieval system means of expressing conceptual syntagmatic relationships (as exemplified by various term co‐occurrence techniques, links and role indicators) fail to comply with this and other of the criteria. Only gestalt structures simultaneously representing relationships, participants and roles (for example, frames) conform fully to the criterial checklist.

This chapter evaluates the extent to which sustainable principles have been included on the destination recovery plans implemented by British Destination Management Organisations (DMOs) in response to COVID-19 and their subsequent quarantines during the period 2020–2021. The aim of the chapter is to explore if this pause in activities created by the COVID-19 crisis was used by UK DMOs as an opportunity to develop sustainable destination management plans, or alternatively, led them to prioritise financial income as the key driver in their recovery. The chapter also identifies the goals, motivations, performance indicators and strategies applied by those DMOs which developed post-COVID tourism destination recovery plans, with particular focus on those which have decided to include sustainability elements in their plans. The chapter concludes by developing a set of principles that other DMOs could apply when intending to develop sustainable management plans for their destinations in response to future major operational disruptions.

The purpose of this paper is to explore the current literature on data governance in scientific and practice-oriented publications, and to provide a comparative analysis of the activities reported for data governance. Data have become a key organisational asset and data governance both a necessary and critical activity.

A comprehensive literature review is conducted in order to identify the published material that reflects the current state of knowledge. A systematic procedure was followed that identified 61 publications that explicitly mention data governance activities. Open coding techniques were applied to conduct content analysis, resulting in the identification of 591 concepts. A critical analysis also identified gaps in the literature.

The analysis identified 120 data governance activities which are understood as: “action” plus “area of governance” plus “decision domain” (e.g. define data policies for data quality). The authors define and present a data governance activities model based on the analysis. The analysis also shows a higher volume of data governance activities reported by practice-oriented publications that are associated with the “implement” and “monitor” actions of the areas of governance across the decision domains compared with scientific publications, whereas The authors found that the scientific publications focus more on defining activities. The results contribute to identifying research gaps and concerns on which ongoing and future research efforts can be focused.

This paper is of interest to both academics and practitioners, as it helps them understand the activities associated with a data governance programme. Current literature fails to provide a comprehensive understanding of the data governance activities that are required when considering a data governance programme. Therefore, the proposed model for data governance activities can be used to give insights into these activities.

To the knowledge of the authors, this study is the first to explicitly consider data governance activities from both an academic and practice-oriented perspective.

This paper aims to present an object detection methodology to categorize 3D object models in an efficient manner. The authors propose a dynamically generated hierarchical architecture to compute very fast objects’ 3D pose for mobile service robots to grasp them.

The methodology used in this study is based on a dynamic pyramid search and fast template representation, metadata and context-free grammars. In the experiments, the authors use an omnidirectional KUKA mobile manipulator equipped with an RGBD camera, to localize objects requested by humans. The proposed architecture is based on efficient object detection and visual servoing. In the experiments, the robot successfully finds 3D poses. The present proposal is not restricted to specific robots or objects and can grow as much as needed.

The authors present the dynamic categorization using context-free grammars and 3D object detection, and through several experiments, the authors perform a proof of concept. The authors obtained promising results, showing that their methods can scale to more complex scenes and they can be used in future applications in real-world scenarios where mobile robot are needed in areas such as service robots or industry in general.

The experiments were carried out using a mobile KUKA youBot. Scalability and more robust algorithms will improve the present proposal. In the first stage, the authors carried out an experimental validation.

The current proposal describes a scalable architecture, where more agents can be added or reprogrammed to handle more complicated tasks.

The main contribution of this study resides in the dynamic categorization scheme for fast detection of 3D objects, and the issues and experiments carried out to test the viability of the methods. Usually, state-of-the-art treats categories as rigid and make static queries to datasets. In the present approach, there are no fixed categories and they are created and combined on the fly to speed up detection.

The management of autonomous, heterogeneous network resources and services provides new challenges which libraries are now addressing. This paper outlines an approach based on the construction of broker services which mediate access to resources. It outlines a framework – the MODELS Information Architecture – for thinking about the components of broker services and their logical arrangement. It describes several development projects and services which show how brokers are developing. It uses examples drawn from the serials environment to describe some of the issues. Technologists understand that they must build more stable and unobtrusive media. They must establish more coherent contexts into which the technology may disappear.

This article proposes a strategy to make the testing step easier, generating user acceptance tests (UATs) in an automatic way from requirements artifacts.

This strategy is based on two modeling frameworks: scenarios and task/method paradigm. Scenarios are a requirement artifact used to describe business processes and requirements, and task/method paradigm is a modeling paradigm coming from the artificial intelligence field. The proposed strategy is composed of four steps. In the first step, scenarios are described through a semantic wiki website. Then scenarios are automatically translated into a task/method model (step two). In the third step, the task/method model obtained in step two is executed in order to produce and store all possible achievements of tasks and thus scenarios. The stored achievements are saved in a data structure called execution tree (ET). Finally, from this ET (step four), the UATs are generated.

The feasibility of this strategy is shown through a case study coming from the agriculture production systems field.

Generally, test design approaches deal with a small number of variables describing one specific situation where a decision table or workflow is used to design tests. The proposed approach can deal with many variables because the authors rely on scenarios that can be composed in order to obtain a tree with all the testing paths that can arise from their description.

The purpose of this article it to investigate the design, and to implement the set‐up of the national union catalogue for those serials which can be accessed at the research libraries of the UK, the aim being to: enable researchers to locate serials held; to assist upgrade of library OPACs; and to provide a component within the UK digital library.

This article builds upon the RSLP‐funded and JISC‐funded scoping and feasibility studies. It makes best use of extant data in OPACs on serials holdings information, by a mixture of collaborative action by contributing libraries, ISSN‐IC and CONSER; and makes best use of extant software (Aleph 500) and national data services (EDINA).

Today, knowing who has which journals online has become as important as knowing who has which journals on‐shelf. Knowledge of both is critical for a researcher in her quest to discover, locate and access that key article. Knowledge of both is also critical for the library profession, as help staff and as curators of quality bibliographic records for serials in their OPACs. SUNCAT has over four million records, describing the “print and electronic holdings” of over 20 of the largest UK libraries – thought to cover 90 per cent or more of the journal titles held across the UK as a whole. The extension of coverage to up to a further 50 research libraries is in progress. So too is development activity to meet the challenge of the digital, investigating how SUNCAT can offer an important link in the access/supply chain, as the focus shifts from document supply to document access.

This article is of significance to researchers and librarians in the UK; there is wider significance for the international serials community.