Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

Knowledge and organisational success:developing a scale of knowledge framework

The aim of this exploratory research is to understand further how organisations can evaluate their activities, which generate knowledge creation, to meet changing stakeholder expectations. A Scale of Knowledge (SoK) Framework is proposed which links knowledge management and organisational activities to changing stakeholder expectations. The framework was informed by the knowledge management literature, as well as empirical work conducted via a single case study of a multi-site hospital organisation in Saudi Arabia. Eight in-depth semi-structured interviews were conducted with managers from across the organisation regarding current and future stakeholder expectations, organisational strategy/activities and knowledge management. Data were analysed using thematic analysis and a hierarchical value map technique to identify activities that can produce further knowledge and consequently impact on how stakeholder expectations are met. The SoK Framework developed may be useful to practitioners as an analytical aid to determine if current organisational activities produce organisational knowledge which helps them meet (increasingly higher levels of) stakeholder expectations. The limitations of the research and avenues for future development of the proposed framework are discussed

The consolidation process of the EU regulatory framework on nanotechnologies: within and beyond the EU case-by-case approach

The field of nanotechnologies has been the subject of a process of wide-ranging regulation, which covers two different trends. From the 2000s the European Commission and Parliament agreed on a type of adaptive, experimental and flexible approach, which had its apex with the Commission code of conduct on responsible nano-research developed through a set of consultations. In 2009 this initial agreement subsequently broke down and the EU started to develop a set of regulatory initiatives of a sectoral nature in several fields (cosmetics, food, biocides). Thus, the current arrangement of governance in the field of nanotechnologies appears to be a hybrid, which mixes forms belonging to the new governance method (consultations, self-regulation, agency, comitology committees, networking), working like a lung in the framework of EU policy, with more traditional tools belonging to the classic governance method (regulations, directives). This model of governance based on a case-by-case approach runs the risk of lacking coherence since it is exposed to sudden changes of direction when risks emerge and it has a weak anticipatory dimension due to both its excessive dependency on data collection and its insufficient use of upstream criteria, such as human rights, which should be used earlier, to allow anticipated intervention with a less intense use of hard law solutions

Automatically detecting open academic review praise and criticism

This is an accepted manuscript of an article published by Emerald in Online Information Review on 15 June 2020. The accepted version of the publication may differ from the final published version, accessible at https://doi.org/10.1108/OIR-11-2019-0347.Purpose: Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research’s open peer review publishing platform. Design/methodology/approach: PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings. Findings: PeerJudge can predict F1000Research judgements from negative evaluations in reviewers’ comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision. Originality/value: PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments

Of course we share! Testing Assumptions about Social Tagging Systems

Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior

Summary Proceedings of a Wind Shear Workshop

A number of recent program results and current issues were addressed: the data collection phase of the highly successful Joint Airport Weather Study (JAWS) Project and the NASA-B5f7B Gust Gradient Program, the use of these data for flight crew training through educational programs (e.g., films) and with manned flight training simulators, methods for post-accident determination of wind conditions from flight data recorders, the microburst wind shear phenomenon which was positively measured and described the ring vortex as a possible generating mechanism, the optimum flight procedure for use during an unexpected wind shear encounter, evaluation of the low-level wind shear alert system (LLWSAS), and assessment of the demonstrated and viable application of Doppler radar as an operational wind shear warning and detection system