title: Use of Starch Granules Enriched with Carvacrol for the Lesser Mealworm, Alphitobius diaperinus Control in Chicken House: Effects on Insects and Poultry

text: The aim of this study was to investigate the effect of starch granules enriched with carvacrol and mixed with straw pellets (as poultry litter) on the mortality of larvae and adults of the lesser mealworm, Alphitobius diaperinus Panzer, a cosmopolitan pest inhabiting chicken houses in vast numbers worldwide. Additionally, the effect of starch granules on the growth parameters and survival of broiler chickens exposed to treated litter was examined. In this study, granules containing 3, 5, and 10% carvacrol was used. In a simulated chicken house bioassay, this material was mixed with pellets in three different proportions: 30/70%, 40/60%, and 50/50% (granules/pellets, respectively). On this medium, young larvae (approximately 10 days old), older larvae (last stage before pupa), and unsexed 7–10 days old adults of the lesser mealworm, with access to food, were colonized. Experiments were performed at 29°C in the dark. The study shows that poultry litter with the addition of starch granules enriched with 10% of carvacrol in the proportion of 40:60% (granules:pellets) appears to be the optimal medium applicable to broiler houses for A. diaperinus control. In this environment, all larvae and adults died within 3–4 days and the overall development of the experimental chickens was similar to that of the control. However, the feed conversion rate was slightly higher in the treated group (1.72) than in the control group (1.56). The average final body weight in the treated group was 100 g lower than that in the control group (the differences were not statistically significant).


Journal of Real-Time Image Processing: third issue of volume 17


title: Corporate Law Versus Social Autonomy: Law as Social Hazard

text: This article argues that corporate law has become the legal platform upon which is erected a social process impeding society’s capacity to lucidly reflect on its primary ends; in this sense, corporate law is in conflict with social autonomy. This process is described here as a social feedback loop, in the structural centre of which lies the corporation which imposes its own purpose as an irrational social end, i.e. irrespective of its potentially catastrophic social consequences. The article argues that resolving the conflict between corporate law and social autonomy is impossible, because it presupposes a change of social paradigm towards one where corporate law as business organisation law has no obvious fit. This questions the social legitimacy of corporate law, signifies its non-permanence and thus opens up the field for seeking radical alternatives in the future.


title: Construction and Characterization of a Novel Recombinant Attenuated and Replication-Deficient Candidate Human Adenovirus Type 3 Vaccine: “Adenovirus Vaccine Within an Adenovirus Vector”

text: Human adenoviruses (HAdVs) are highly contagious and result in large number of acute respiratory disease (ARD) cases with severe morbidity and mortality. Human adenovirus type 3 (HAdV-3) is the most common type that causes ARD outbreaks in Asia, Europe, and the Americas. However, there is currently no vaccine approved for its general use. The hexon protein contains the main neutralizing epitopes, provoking strong and lasting immunogenicity. In this study, a novel recombinant and attenuated adenovirus vaccine candidate against HAdV-3 was constructed based on a commercially-available replication-defective HAdV-5 gene therapy and vaccine vector. The entire HAdV-3 hexon gene was integrated into the E1 region of the vector by homologous recombination using a bacterial system. The resultant recombinants expressing the HAdV-3 hexon protein were rescued in AD293 cells, identified and characterized by RT-PCR, Western blots, indirect immunofluorescence, and electron microscopy. This potential vaccine candidate had a similar replicative efficacy as the wild-type HAdV-3 strain. However, and importantly, the vaccine strain had been rendered replication-defective and was incapable of replication in A549 cells after more than twenty-generation passages in AD293 cells. This represents a significant safety feature. The mice immunized both intranasally and intramuscularly by this vaccine candidate raised significant neutralizing antibodies against HAdV-3. Therefore, this recombinant, attenuated, and safe adenovirus vaccine is a promising HAdV-3 vaccine candidate. The strategy of using a clinically approved and replication-defective HAdV-5 vector provides a novel approach to develop universal adenovirus vaccine candidates against all the other types of adenoviruses causing ARDs and perhaps other adenovirus-associated diseases.


Editorial: Pathogen Genomics: Empowering Infectious Disease Surveillance and Outbreak Investigations


title: Dimensions and Contradictions of the European Integration: Deepening Versus Widening

text: The second chapter tackles the question of how the European Union as an imagined community developed through the process of enlargement and integration of European states and citizens. The new model of a transnational citizenship in terms of belonging refers not only to the citizens of the member countries and resident migrants. It becomes a desirable goal for many citizens of the aspirant countries. This reflection helps understand how the prospect of joining the EU affects the democratization of the post-socialist European states, with a focus on its normative and procedural dimensions in the accession process of the ‘Western Balkan’ countries. The overall aim of the analysis is to understand the making of today’s Europe and its growing complexity, which constantly requires new arguments to legitimate the EU’s founding principles.


Mehr Beachtung der PTBS bei Kindern und Jugendlichen


title: A concept analysis of children with complex health conditions: implications for research and practice

text: BACKGROUND: This concept analysis aimed to clarify the meaning of “children with complex health conditions” and endorse a definition to inform future research, policy, and practice. METHODS: Using Walker and Avant’s (2011)‘s approach, we refined the search strategy with input from our team, including family representatives. We reviewed the published and grey literature. We also interviewed 84 health, social, and educational stakeholders involved in the care of children with complex health conditions about their use/understanding of the concept. RESULTS: We provided model, borderline, related, and contrary cases for clarification purposes. We identified defining attributes that nuance the concept: (1) conditions and needs’ breadth; (2) uniqueness of each child/condition; (3) varying extent of severity over time; 4) developmental age; and (5) uniqueness of each family/context. Antecedents were chronic physical, mental, developmental, and/or behavioural condition(s). There were individual, family, and system consequences, including fragmented services. CONCLUSIONS: Building on previous definitions, we proposed an iteration that acknowledges the conditions’ changing trajectories as involving one or more chronic condition(s), regardless of type(s), whose trajectories can change over time, requiring services across sectors/settings, oftentimes resulting in a lower quality of life. A strength of this paper is the integration of the stakeholders’/family’s voices into the development of the definition.


Psychiatrische Aspekte einer Pandemie


Chemotherapie: Bewahrt Kälte vor Neuropathie?


Ventilator-associated pneumonia in the NICU: time to boost diagnostics?


Prone Positioning following Breast Reconstruction


L’utilisation de Smartphones pour le suivi du déconfinement du Covid-19 en France


docid: yozyowig

title: The use of Smartphones to monitor the deconfinement of Covid-19 in France


Should WALANT surgery be included in the training curriculum?


Ageism and intergenerational tensions in the COVID-19 period


Reprise du travail dans le bâtiment et les travaux publics


title: Revisiting Social Work with Older People in Chinese Contexts from a Community Development Lens: When East Meets West

text: Community development is an empowering and comprehensive method for social workers to address individual and societal challenges facing Chinese older people. This chapter explores the different meanings of community development in Chinese contexts, including communities in Mainland China and Chinese immigrant communities. When actualizing community development, social workers require theories to guide their actions, and this chapter proposes three interrelated theoretical bases: ecological system theory, empowerment theory, and anti-oppressive theory. Based on these theoretical bases, three practice directions are suggested to guide community development at different levels: “aging in place,” “age-friendly community,” and “gray power.” In particular, social workers need to adapt community development approaches to Chinese cultural contexts. Case examples are discussed to illustrate how to implement community development projects with older people in Chinese contexts and the roles of social workers in such projects.


title: Basic Income Advocacy in Canada: Multiple Streams, Experiments and the Road Ahead

text: Basic income has a long history in Canada, going back to the Great Depression. Its recognition has risen and fallen cyclically, several experiments have been mounted, and some programs, such as the Canada Child Benefit and the Old Age Security demogrant, have been identified as partial basic income schemes. However, a universal basic income for all members of the Canadian political community has never been visibly close to implementation. Against this background, this chapter examines basic income political advocacy from three vantage points. First, the multiple streams framework is used to assess linkages of basic income in the problem stream, its position in the policy stream and forces at play in the political stream. Next, the fate of experiments is described, along with approaches for increasing the likelihood that experiments will result in full-scale policy implementation if outcomes are sufficiently promising. Third, some advocacy strategies for the future are recommended.


title: The USA’s Modern Civil Rights Movement and Basic Income Guarantee

text: This chapter explores Dr. Martin Luther King Jr.’s (MLK) contribution to Basic Income Guarantee (BIG) and its association with the USA’s Modern Civil Rights Movement (MCRM). The exploration begins with a synopsis of the MCRM: its antecedents, a biographical sketch of MLK and a precis of his leadership. A discussion of MLK’s support of BIG and a brief survey of his long-term influence follow, ending with the conclusion. The goal is to identify and analyze the international thought-leader, Nobel Peace Laureate and civil rights strategist’s approach (justifications and strategies) to realizing BIG for practical and theoretical lessons that could inform future and current BIG activism.


title: Advocacy for a Universal Basic Income for the United States: My Story

text: At this moment in U.S. society, Universal Basic Income (UBI), an agreed upon amount of income distributed to the members of a society as a matter of right, is once again being given serious attention. The last time Universal Basic Income was part of a robust public discussion was in the 1960s and 1970s, when American politicians, businesspeople, and human rights activists all were talking about adequate income as a solution to poverty, automation, militarism, and racism. A plethora of writing and research exists on UBI and the effects it would have on people in the twenty-first century. While social workers are fundamentally concerned with the life cycle and systems that sustain human well-being, social workers and social work institutions remain largely absent from the fervent UBI discussion and activism that is taking place now in the United States. A social worker myself, I hope to explain here the professional and personal experiences that have driven me to commit myself to Universal Basic Income activism and strategy, and encourage the social work community and other committed people from all walks of life and all places to be part of the movement for UBI.


It’s not the mortality rate, stupid!


title: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection: let the virus be its own demise

text: There has been a collaborative global effort to construct novel therapeutic and prophylactic approaches to SARS-CoV-2 management. Although vaccine development is crucial, acute management of newly infected patients, especially those with severe acute respiratory distress syndrome, is a priority. Herein we describe the rationale and potential of repurposing a dual plasmid, Vigil (pbi-shRNA(furin)-GM-CSF), now in Phase III cancer trials, for the treatment of and, in certain circumstances, enhancement of the immune response to SARS-CoV-2.


title: Opportunity Costs Pacifism

text: If the resources used to wage wars could be spent elsewhere and save more lives, does this mean that wars are unjustified? This article considers this question, which has been largely overlooked by Just War Theorists and pacifists. It focuses on whether the opportunity costs of war lead to a form of pacifism, which it calls ‘Opportunity Costs Pacifism’. The article argues that Opportunity Costs Pacifism is, at the more ideal level, compelling. It suggests that the only plausible response to Opportunity Costs Pacifism applies in highly nonideal circumstances. This has major implications for Just War Theory and pacifism since it is only at the highly nonideal level that war can be justified.


docid: hferqfso

title: Editor’s Introduction to v40(1)


The Effect of a Virus on Adult Development


docid: pxfbmlnd

title: Parker Hannifin Corp, USA


FLSmidth & Co, Denmark


Company


Orders & Contracts


Alfa Laval AB, Sweden


Atlas Copco AB, Sweden


Pentair Ltd, UK/Ireland


title: Hamworthy Pumps sees revenues increase 60%

text: Hamworthy Pumps has grown its turnover by 60% and created 26 new jobs since it was acquired by the Scandinavian investment firm Solix Group from Wärtsilä Group in 2018.


The Gorman-Rupp Co, USA


title: Desmi hit by cyber-attack

text: Danish pump company Desmi A/S suffered a cyber-attack in the week before Easter, with the attackers requesting a ransom for data recovery.


Desmi A/S, Denmark


title: Approaches to Identify Fake News: A Systematic Literature Review

text: With the widespread dissemination of information via digital media platforms, it is of utmost importance for individuals and societies to be able to judge the credibility of it. Fake news is not a recent concept, but it is a commonly occurring phenomenon in current times. The consequence of fake news can range from being merely annoying to influencing and misleading societies or even nations. A variety of approaches exist to identify fake news. By conducting a systematic literature review, we identify the main approaches currently available to identify fake news and how these approaches can be applied in different situations. Some approaches are illustrated with a relevant example as well as the challenges and the appropriate context in which the specific approach can be used.


Fostering Diaologue for a Common Future: Asia-Europe Policy Forum (AEPF)


Informatik in Zeiten von Corona


title: Preface: Patterns and processes of meiofauna in freshwater ecosystems

text: Never heard of harpacticoids, ostracods, gastrotrichs or microturbellarians? This is no surprise, they are so tiny! Yet these taxa and many others more famous (nematodes, rotifers, or tardigrades) show complex behaviours and extraordinary physiologies that allow them to colonize inland waters worldwide. This exuberant fauna is better known as the meiofauna (or meiobenthos). Meiofaunal organisms have been fascinating study objects for zoologists since the seventeenth century and recent research has demonstrated their intermediate role in benthic food webs. This special issue highlights how meiofauna can help freshwater ecologists to describe and predict species distribution patterns, to assess production of biomass and trait functions relationships, as well as to examine the trophic links between microscopic and macroscopic worlds and to better understand species’ resilience to environmental extremes. Overall, meiofaunal organisms are bridging scales, and as such they deserve better integration to develop more comprehensive concepts and theories in ecology.


Acyclovir for ventilator-associated pneumonia refractory to antibiotics and with high viral herpes simplex load: we are not sure


title: Perfectionism and Perceived Control in Posttraumatic Stress Disorder Symptoms

text: In this study, we sought to examine associations between dimensions of trait perfectionism and posttraumatic stress disorder (PTSD) symptoms among a community sample of adults who experienced at least one traumatic event and determine whether the associations between trait perfectionism dimensions and PTSD symptomatology varied as a function of perceived control. A sample of 161 (57% women; M = 33.24 years, SD = 10.84 years) community adults who experienced at least one traumatic event in their lives completed self-report measures of trait perfectionism, perceived control, personality, and PTSD symptomatology. Results from multiple regression analyses indicated that socially prescribed perfectionism was associated with higher levels of PTSD symptomatology. However, this finding was qualified by a significant interaction with perceived control such that higher levels of socially prescribed perfectionism were associated with greater PTSD symptomatology only under conditions of low perceived control. Findings underscore the importance of individual differences in socially prescribed perfectionism and perceived control for PTSD symptomatology.


title: Naturally Derived Membrane Lipids Impact Nanoparticle-Based Messenger RNA Delivery

text: INTRODUCTION: Lipid based nanoparticles (LNPs) are clinically successful vectors for hepatic delivery of nucleic acids. These systems are being developed for non-hepatic delivery of mRNA for the treatment of diseases like cystic fibrosis or retinal degeneration as well as infectious diseases. Localized delivery to the lungs requires aerosolization. We hypothesized that structural lipids within LNPs would provide features of integrity which can be tuned for attributes required for efficient hepatic and non-hepatic gene delivery. Herein, we explored whether naturally occurring lipids that originate from the cell membrane of plants and microorganisms enhance mRNA-based gene transfection in vitro and in vivo and whether they assist in maintaining mRNA activity after nebulization. METHODS: We substituted DSPC, a structural lipid used in a conventional LNP formulation, to a series of naturally occurring membrane lipids. We measured the effect of these membrane lipids on size, encapsulation efficiency and their impact on transfection efficiency. We further characterized LNPs after nebulization and measured whether they retained their transfection efficiency. RESULTS: One plant-derived structural lipid, DGTS, led to a significant improvement in liver transfection of mRNA. DGTS LNPs had similar transfection ability when administered in the nasal cavity to conventional LNPs. In contrast, we found that DGTS LNPs had reduced transfection efficiency in cells pre-and post-nebulization while maintaining size and encapsulation similar to DSPC LNPs. CONCLUSIONS: We found that structural lipids provide differential mRNA-based activities in vitro and in vivo which also depend on the mode of administration. Understanding influence of structural lipids on nanoparticle morphology and structure can lead to engineering potent materials for mRNA-based gene therapy applications.


title: Development of Smart Healthcare Monitoring System in IoT Environment

text: Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient’s basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO(2) sensor. The error percentage of the developed scheme is within a certain limit (< 5%) for each case. The condition of the patients is conveyed via a portal to medical staff, where they can process and analyze the current situation of the patients. The developed prototype is well suited for healthcare monitoring that is proved by the effectiveness of the system.


Provando e Riprovando: Galilean science in times of virulence


title: Food Insecurity in Europe: A Gender Perspective

text: Food insecurity is the limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire foods in socially acceptable ways. The study presents a comparison of the principal determinants of individual food insecurity in Europe and other Continents, with particular regard to gender, since the literature clearly states the importance of women in the administration of food in the household. The study of gender related differences in food insecurity is particularly important in Europe, since women experience food insecurity at a larger extent than men, but with a variability related to the geographical distribution and with complex relationships with economic and social drivers. Using a large international sample of individual level data, that allows the analysis for developed Countries for the first time, and the first experiential measure of food insecurity comparable at the global level, the paper analyses the principal determinants of gender differences in food insecurity. In order to verify if women’s vulnerability in food insecurity is moderated by specific factors, the modelling approach allows gender to vary by education, poverty, place of residence. The results suggest that the driver that could most mitigate women disadvantage is education: people with a university degree present a lower probability of experiencing food insecurity, both for men and for women. On the contrary, familial characteristics, such as the number of children in the household, present a higher impact on women’s food insecurity than on men’s.


title: Planning and Animal Welfare Law: Considering Why Planning Authorities in the Republic of Ireland Should have Regard to Animal Welfare Laws as Part of the Planning Process

text: Animal welfare legislation routinely places obligations on owners of animals to take action to ensure that the welfare of the animal(s) is protected. Such legislation often requires animals to be sheltered by appropriate housing. Where facilities are built to provide animal housing it may be necessary to seek planning permission for the development, this is particularly an issue in relation to commercial facilities. The general approach of planning authorities in the Republic of Ireland is not to have regard to animal welfare matters at the planning stage. With reference to commercial dog breeding this paper considers the implications of the failure to have regard to animal welfare legislation during the planning process and the impact this can have on both animal welfare, but also the risks such an approach poses to the environment and public health.


title: Executive function training in very preterm children: a randomized controlled trial

text: Objective of the current study was to assess whether game-formatted executive function (EF) training, is effective in improving attention, EF and academic performance in very preterm and/or extremely low birthweight children aged 8–12 years. A multi-center, double-blind, placebo- and waitlist controlled randomized trial (NTR5365) in two academic hospitals in The Netherlands was performed. Eighty-five very preterm children with parent-rated attention problems on the Child Behavior Checklist were randomized to one of three treatment conditions: EF training, placebo training or waitlist condition. EF or placebo training was completed at home (6 weeks, 25 sessions of 30–45 min each). At baseline, 2 weeks after training or being on the waitlist, and five months after first follow-up visit, children underwent assessments of primary outcomes (parent and teacher ratings of attention) and secondary outcomes (parent and teacher ratings of daily-life EF, computerized EF tasks and academic performance). Linear mixed model analyses were performed for all outcome measures. There were no significant differences in improvement over time on parent- and teacher ratings of attention, parent- and teacher ratings of daily-life EF, computerized EF tasks, and academic performance (arithmetic and reading) between the EF training, placebo training and waitlist condition. In conclusion, game-formatted EF training does not improve attention, EF or academic performance in very preterm children with parent-rated attention problems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00787-020-01561-0) contains supplementary material, which is available to authorized users.


title: Physical Contact and Loneliness: Being Touched Reduces Perceptions of Loneliness

text: OBJECTIVE: This article addresses the connection between loneliness and physical contact. Evolutionary and psychological research has shown that touch is an important part of bond-building and emotion communication; loneliness is intimately related to these elements as well. In this paper, we ask whether physical contact reduces feelings of loneliness —which might derive from evolutionary ancient bonding mechanisms—despite a cultural context that is relatively non-tactile. METHOD: An experimental study (40 participants, 13 males) tested for observable effects of touch on loneliness scores in a low-contact culture to analyse whether they respond positively to that stimulus despite cultural training against it. RESULTS: Participants exposed to physical contact reported significantly lower neglect scores from their close relationships in a short loneliness scale, thus suggesting that there is an underlying mechanism that persists despite enculturation. The effects were particularly strong among single people, which could mean that lower loneliness among married people might be partly explained by the regular availability of physical contact. Participants in the experimental condition also showed a faster reduction in heart rate, interpreted as a sign of physiological wellbeing. CONCLUSIONS: These findings help to specify mechanisms within the evolutionary theoretical framework of loneliness that link internal feelings to environmental cues. This article aims at contributing to a more complex discussion on the interactions between emotions, cultural practices and psychological well-being.


docid: nyvaenxj

title: In This Issue/Research Watch/News-in-Brief


docid: adhzbaeq

title: Pandemic versus pandemonium: fighting on two fronts


docid: sbrxawij

title: Partial Domain Adaptation for Relation Extraction Based on Adversarial Learning

text: Relation extraction methods based on domain adaptation have begun to be extensively applied in specific domains to alleviate the pressure of insufficient annotated corpus, which enables learning by utilizing the training data set of a related domain. However, the negative transfer may occur during the adaptive process due to differences in data distribution between domains. Besides, it is difficult to achieve a fine-grained alignment of relation category without fully mining the multi-mode data structure. Furthermore, as a common application scenario, partial domain adaptation (PDA) refers to domain adaptive behavior when the relation class set of a specific domain is a subset of the related domain. In this case, some outliers belonging to the related domain will reduce the performance of the model. To solve these problems, a novel model based on a multi-adversarial module for partial domain adaptation (MAPDA) is proposed in this study. We design a weight mechanism to mitigate the impact of noise samples and outlier categories, and embed several adversarial networks to realize various category alignments between domains. Experimental results demonstrate that our proposed model significantly improves the state-of-the-art performance of relation extraction implemented in domain adaptation.


docid: keqsdceh

title: Modular Graphical Ontology Engineering Evaluated

text: Ontology engineering is traditionally a complex and time-consuming process, requiring an intimate knowledge of description logic and predicting non-local effects of different ontological commitments. Pattern-based modular ontology engineering, coupled with a graphical modeling paradigm, can help make ontology engineering accessible to modellers with limited ontology expertise. We have developed CoModIDE, the Comprehensive Modular Ontology IDE, to develop and explore such a modeling approach. In this paper we present an evaluation of the CoModIDE tool, with a set of 21 subjects carrying out some typical modeling tasks. Our findings indicate that using CoModIDE improves task completion rate and reduces task completion time, compared to using standard Protégé. Further, our subjects report higher System Usability Scale (SUS) evaluation scores for CoModIDE, than for Protégé. The subjects also report certain room for improvements in the CoModIDE tool – notably, these comments all concern comparatively shallow UI bugs or issues, rather than limitations inherent in the proposed modeling method itself. We deduce that our modeling approach is viable, and propose some consequences for ontology engineering tool development.


title: SASOBUS: Semi-automatic Sentiment Domain Ontology Building Using Synsets

text: In this paper, a semi-automatic approach for building a sentiment domain ontology is proposed. Differently than other methods, this research makes use of synsets in term extraction, concept formation, and concept subsumption. Using several state-of-the-art hybrid aspect-based sentiment analysis methods like Ont + CABASC and Ont + LCR-Rot-hop on a standard dataset, the accuracies obtained using the semi-automatically built ontology as compared to the manually built one, are slightly lower (from approximately 87% to 84%). However, the user time needed for building the ontology is reduced by more than half (from 7 h to 3 h), thus showing the usefulness of this work. This is particularly useful for domains for which sentiment ontologies are not yet available.


title: A Knowledge Graph for Industry 4.0

text: One of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality.


title: Embedding-Based Recommendations on Scholarly Knowledge Graphs

text: The increasing availability of scholarly metadata in the form of Knowledge Graphs (KG) offers opportunities for studying the structure of scholarly communication and evolution of science. Such KGs build the foundation for knowledge-driven tasks e.g., link discovery, prediction and entity classification which allow to provide recommendation services. Knowledge graph embedding (KGE) models have been investigated for such knowledge-driven tasks in different application domains. One of the applications of KGE models is to provide link predictions, which can also be viewed as a foundation for recommendation service, e.g. high confidence “co-author” links in a scholarly knowledge graph can be seen as suggested collaborations. In this paper, KGEs are reconciled with a specific loss function (Soft Margin) and examined with respect to their performance for co-authorship link prediction task on scholarly KGs. The results show a significant improvement in the accuracy of the experimented KGE models on the considered scholarly KGs using this specific loss. TransE with Soft Margin (TransE-SM) obtains a score of 79.5% Hits@10 for co-authorship link prediction task while the original TransE obtains 77.2%, on the same task. In terms of accuracy and Hits@10, TransE-SM also outperforms other state-of-the-art embedding models such as ComplEx, ConvE and RotatE in this setting. The predicted co-authorship links have been validated by evaluating profile of scholars.


title: Keyword Search over RDF Using Document-Centric Information Retrieval Systems

text: For ordinary users, the task of accessing knowledge graphs through structured query languages like SPARQL is rather demanding. As a result, various approaches exploit the simpler and widely used keyword-based search paradigm, either by translating keyword queries to structured queries, or by adopting classical information retrieval (IR) techniques. In this paper, we study and adapt Elasticsearch, an out-of-the-box document-centric IR system, for supporting keyword search over RDF datasets. Contrary to other works that mainly retrieve entities, we opt for retrieving triples, due to their expressiveness and informativeness. We specify the set of functional requirements and study the emerging questions related to the selection and weighting of the triple data to index, and the structuring and ranking of the retrieved results. Finally, we perform an extensive evaluation of the different factors that affect the IR performance for four different query types. The reported results are promising and offer useful insights on how different Elasticsearch configurations affect the retrieval effectiveness and efficiency.


title: ESBM: An Entity Summarization BenchMark

text: Entity summarization is the problem of computing an optimal compact summary for an entity by selecting a size-constrained subset of triples from RDF data. Entity summarization supports a multiplicity of applications and has led to fruitful research. However, there is a lack of evaluation efforts that cover the broad spectrum of existing systems. One reason is a lack of benchmarks for evaluation. Some benchmarks are no longer available, while others are small and have limitations. In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark. Using this largest available benchmark for evaluating general-purpose entity summarizers, we perform the most extensive experiment to date where 9 existing systems are compared. Considering that all of these systems are unsupervised, we also implement and evaluate a supervised learning based system for reference.


title: SAShA: Semantic-Aware Shilling Attacks on Recommender Systems Exploiting Knowledge Graphs

text: Recommender systems (RS) play a focal position in modern user-centric online services. Among them, collaborative filtering (CF) approaches have shown leading accuracy performance compared to content-based filtering (CBF) methods. Their success is due to an effective exploitation of similarities/correlations encoded in user interaction patterns, which is computed by considering common items users rated in the past. However, their strength is also their weakness. Indeed, a malicious agent can alter recommendations by adding fake user profiles into the platform thereby altering the actual similarity values in an engineered way. The spread of well-curated information available in knowledge graphs ([Formula: see text]) has opened the door to several new possibilities in compromising the security of a recommender system. In fact, [Formula: see text] are a wealthy source of information that can dramatically increase the attacker’s (and the defender’s) knowledge of the underlying system. In this paper, we introduce SAShA, a new attack strategy that leverages semantic features extracted from a knowledge graph in order to strengthen the efficacy of the attack to standard CF models. We performed an extensive experimental evaluation in order to investigate whether SAShA is more effective than baseline attacks against CF models by taking into account the impact of various semantic features. Experimental results on two real-world datasets show the usefulness of our strategy in favor of attacker’s capacity in attacking CF models.


title: Entity Summarization with User Feedback

text: Semantic Web applications have benefited from entity summarization techniques which compute a compact summary for an entity by selecting a set of key triples from underlying data. A wide variety of entity summarizers have been developed. However, the quality of summaries they generate are still not satisfying, and we lack mechanisms for improving computed summaries. To address this challenge, in this paper we present the first study of entity summarization with user feedback. We consider a cooperative environment where a user reads the current entity summary and provides feedback to help an entity summarizer compute an improved summary. Our approach represents this iterative process as a Markov decision process where the entity summarizer is modeled as a reinforcement learning agent. To exploit user feedback, we represent the interdependence of triples in the current summary and the user feedback by a novel deep neural network which is incorporated into the policy of the agent. Our approach outperforms five baseline methods in extensive experiments with both real users and simulated users.


title: Processing SPARQL Aggregate Queries with Web Preemption

text: Executing aggregate queries on the web of data allows to compute useful statistics ranging from the number of properties per class in a dataset to the average life of famous scientists per country. However, processing aggregate queries on public SPARQL endpoints is challenging, mainly due to quotas enforcement that prevents queries to deliver complete results. Existing distributed query engines allow to go beyond quota limitations, but their data transfer and execution times are clearly prohibitive when processing aggregate queries. Following the web preemption model, we define a new preemptable aggregation operator that allows to suspend and resume aggregate queries. Web preemption allows to continue query execution beyond quota limits and server-side aggregation drastically reduces data transfer and execution time of aggregate queries. Experimental results demonstrate that our approach outperforms existing approaches by orders of magnitude in terms of execution time and the amount of transferred data.


title: StreamPipes Connect: Semantics-Based Edge Adapters for the IIoT

text: Accessing continuous time series data from various machines and sensors is a crucial task to enable data-driven decision making in the Industrial Internet of Things (IIoT). However, connecting data from industrial machines to real-time analytics software is still technically complex and time-consuming due to the heterogeneity of protocols, formats and sensor types. To mitigate these challenges, we present StreamPipes Connect, targeted at domain experts to ingest, harmonize, and share time series data as part of our industry-proven open source IIoT analytics toolbox StreamPipes. Our main contributions are (i) a semantic adapter model including automated transformation rules for pre-processing, and (ii) a distributed architecture design to instantiate adapters at edge nodes where the data originates. The evaluation of a conducted user study shows that domain experts are capable of connecting new sources in less than a minute by using our system. The presented solution is publicly available as part of the open source software Apache StreamPipes.


title: Equivalent Rewritings on Path Views with Binding Patterns

text: A view with a binding pattern is a parameterized query on a database. Such views are used, e.g., to model Web services. To answer a query on such views, the views have to be orchestrated together in execution plans. We show how queries can be rewritten into equivalent execution plans, which are guaranteed to deliver the same results as the query on all databases. We provide a correct and complete algorithm to find these plans for path views and atomic queries. Finally, we show that our method can be used to answer queries on real-world Web services.


title: YAGO 4: A Reason-able Knowledge Base

text: YAGO is one of the large knowledge bases in the Linked Open Data cloud. In this resource paper, we present its latest version, YAGO 4, which reconciles the rigorous typing and constraints of schema.org with the rich instance data of Wikidata. The resulting resource contains 2 billion type-consistent triples for 64 Million entities, and has a consistent ontology that allows semantic reasoning with OWL 2 description logics.


title: VQuAnDa: Verbalization QUestion ANswering DAtaset

text: Question Answering (QA) systems over Knowledge Graphs (KGs) aim to provide a concise answer to a given natural language question. Despite the significant evolution of QA methods over the past years, there are still some core lines of work, which are lagging behind. This is especially true for methods and datasets that support the verbalization of answers in natural language. Specifically, to the best of our knowledge, none of the existing Question Answering datasets provide any verbalization data for the question-query pairs. Hence, we aim to fill this gap by providing the first QA dataset VQuAnDa that includes the verbalization of each answer. We base VQuAnDa on a commonly used large-scale QA dataset – LC-QuAD, in order to support compatibility and continuity of previous work. We complement the dataset with baseline scores for measuring future training and evaluation work, by using a set of standard sequence to sequence models and sharing the results of the experiments. This resource empowers researchers to train and evaluate a variety of models to generate answer verbalizations.


title: Applying Knowledge Graphs as Integrated Semantic Information Model for the Computerized Engineering of Building Automation Systems

text: During the life cycle of a smart building, an extensive amount of heterogeneous information is required to plan, construct, operate and maintain the building and its technical systems. Traditionally, there is an information gap between the different phases and stakeholders, leading to information being exchanged, processed and stored in a variety of mostly human-readable documents. This paper shows how a knowledge graph can be established as integrated information model that can provide the required information for all phases in a machine-interpretable way. The knowledge graph describes and connects all relevant information, which allows combining and applying it in a holistic way. This makes the knowledge graph a key enabler for a variety of advanced, computerized engineering tasks, ranging from the planning and design phases over the commissioning and the operation of a building. The computerized engineering of building automation systems (BAS) with an advanced software tool chain is presented as such a use case in more detail. The knowledge graph is based on standard semantic web technologies and builds on existing ontologies, such as the Brick and QUDT ontologies, with various novel extensions presented in this paper. Special attention is given to the rich semantic definition of the entities, such as the equipment and the typically thousands of datapoints in a BAS, which can be achieved as a combination of contextual modeling and semantic tagging.


title: Unsupervised Bootstrapping of Active Learning for Entity Resolution

text: Entity resolution is one of the central challenges when integrating data from large numbers of data sources. Active learning for entity resolution aims to learn high-quality matching models while minimizing the human labeling effort by selecting only the most informative record pairs for labeling. Most active learning methods proposed so far, start with an empty set of labeled record pairs and iteratively improve the prediction quality of a classification model by asking for new labels. The absence of adequate labeled data in the early active learning iterations leads to unstable models of low quality which is known as the cold start problem. In our work we solve the cold start problem using an unsupervised matching method to bootstrap active learning. We implement a thresholding heuristic that considers pre-calculated similarity scores and assigns matching labels with some degree of noise at no manual labeling cost. The noisy labels are used for initializing the active learning process and throughout the whole active learning cycle for model learning and query selection. We evaluate our pipeline with six datasets from three different entity resolution settings using active learning with a committee-based query strategy and show it successfully deals with the cold start problem. Comparing our method against two active learning baselines without bootstrapping, we show that it can additionally lead to overall improved learned models in terms of [Formula: see text] score and stability.


title: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion

text: Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in them. In this work, we examine the contribution of geometrical space to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging. We extend these models to the hyperbolic space so as to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model, dubbed HyperKG, can capture and show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models and effectively represent certain types of rules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-49461-2_12) contains supplementary material, which is available to authorized users.


docid: sekpmynq

title: Hybrid Reasoning Over Large Knowledge Bases Using On-The-Fly Knowledge Extraction

text: The success of logic-based methods for comparing entities heavily depends on the axioms that have been described for them in the Knowledge Base (KB). Due to the incompleteness of even large and well engineered KBs, such methods suffer from low recall when applied in real-world use cases. To address this, we designed a reasoning framework that combines logic-based subsumption with statistical methods for on-the-fly knowledge extraction. Statistical methods extract additional (missing) axioms for the compared entities with the goal of tackling the incompleteness of KBs and thus improving recall. Although this can be beneficial, it can also introduce noise (false positives or false negatives). Hence, our framework uses heuristics to assess whether knowledge extraction is likely to be advantageous and only activates the statistical components if this is the case. We instantiate our framework by combining lightweight logic-based reasoning implemented on top of existing triple-stores with an axiom extraction method that is based on the labels of concepts. Our work was motivated by industrial use cases over which we evaluate our instantiated framework, showing that it outperforms approaches that are only based on textual information. Besides the best combination of precision and recall, our implementation is also scalable and is currently used in an industrial production environment.


title: The Knowledge Graph Track at OAEI: Gold Standards, Baselines, and the Golden Hammer Bias

text: The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools. In 2018, we have started the Knowledge Graph track, whose goal is to evaluate the simultaneous matching of entities and schemas of large-scale knowledge graphs. In this paper, we discuss the design of the track and two different strategies of gold standard creation. We analyze results and experiences obtained in first editions of the track, and, by revealing a hidden task, we show that all tools submitted to the track (and probably also to other tracks) suffer from a bias which we name the golden hammer bias.


title: MetaLink: A Travel Guide to the LOD Cloud

text: Graph-based traversal is an important navigation paradigm for the Semantic Web, where datasets are interlinked to provide context. While following links may result in the discovery of complementary data sources and enriched query results, it is widely recognized that traversing the LOD Cloud indiscriminately results in low quality answers. Over the years, approaches have been published that help to determine whether links are trustworthy or not, based on certain criteria. While such approaches are often useful for specific datasets and/or in specific applications, they are not yet widely used in practice or at the scale of the entire LOD Cloud. This paper introduces a new resource called MetaLink. MetaLink is a dataset that contains metadata for a very large set of owl:sameAs links that are crawled from the LOD Cloud. MetaLink encodes a previously published error metric for each of these links. MetaLink is published in combination with LOD-a-lot, a dataset that is based on a large crawl of a subset of the LOD Cloud. By combining MetaLink and LOD-a-lot, applications are able to make informed decisions about whether or not to follow specific links on the LOD Cloud. This paper describes our approach for creating the MetaLink dataset. It describes the vocabulary that it uses and provides an overview of multiple real-world use cases in which the MetaLink dataset can solve non-trivial research and application challenges that were not addressed before.


title: Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach

text: Knowledge about the software used in scientific investigations is necessary for different reasons, including provenance of the results, measuring software impact to attribute developers, and bibliometric software citation analysis in general. Additionally, providing information about whether and how the software and the source code are available allows an assessment about the state and role of open source software in science in general. While such analyses can be done manually, large scale analyses require the application of automated methods of information extraction and linking. In this paper, we present SoftwareKG—a knowledge graph that contains information about software mentions from more than 51,000 scientific articles from the social sciences. A silver standard corpus, created by a distant and weak supervision approach, and a gold standard corpus, created by manual annotation, were used to train an LSTM based neural network to identify software mentions in scientific articles. The model achieves a recognition rate of .82 F-score in exact matches. As a result, we identified more than 133,000 software mentions. For entity disambiguation, we used the public domain knowledge base DBpedia. Furthermore, we linked the entities of the knowledge graph to other knowledge bases such as the Microsoft Academic Knowledge Graph, the Software Ontology, and Wikidata. Finally, we illustrate, how SoftwareKG can be used to assess the role of software in the social sciences.


title: SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

text: Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge.


docid: ddwzgqta

title: GEval: A Modular and Extensible Evaluation Framework for Graph Embedding Techniques

text: While RDF data are graph shaped by nature, most traditional Machine Learning (ML) algorithms expect data in a vector form. To transform graph elements to vectors, several graph embedding approaches have been proposed. Comparing these approaches is interesting for 1) developers of new embedding techniques to verify in which cases their proposal outperforms the state-of-art and 2) consumers of these techniques in choosing the best approach according to the task(s) the vectors will be used for. The comparison could be delayed (and made difficult) by the choice of tasks, the design of the evaluation, the selection of models, parameters, and needed datasets. We propose GEval, an evaluation framework to simplify the evaluation and the comparison of graph embedding techniques. The covered tasks range from ML tasks (Classification, Regression, Clustering), semantic tasks (entity relatedness, document similarity) to semantic analogies. However, GEval is designed to be (easily) extensible. In this article, we will describe the design and development of the proposed framework by detailing its overall structure, the already implemented tasks, and how to extend it. In conclusion, to demonstrate its operating approach, we consider the parameter tuning of the KGloVe algorithm as a use case.


title: Fast and Exact Rule Mining with AMIE 3

text: Given a knowledge base (KB), rule mining finds rules such as “If two people are married, then they live (most likely) in the same place”. Due to the exponential search space, rule mining approaches still have difficulties to scale to today’s large KBs. In this paper, we present AMIE 3, a system that employs a number of sophisticated pruning strategies and optimizations. This allows the system to mine rules on large KBs in a matter of minutes. Most importantly, we do not have to resort to approximations or sampling, but are able to compute the exact confidence and support of each rule. Our experiments on DBpedia, YAGO, and Wikidata show that AMIE 3 beats the state of the art by a factor of more than 15 in terms of runtime.


title: On Modeling the Physical World as a Collection of Things: The W3C Thing Description Ontology

text: This document presents the Thing Description ontology, an axiomatization of the W3C Thing Description model. It also introduces an alignment with the Semantic Sensor Network ontology and evaluates how this alignment contributes to semantic interoperability in the Web of Things.


docid: qfpfllay

title: Fostering Scientific Meta-analyses with Knowledge Graphs: A Case-Study

text: A meta-analysis is a Science of Science method widely used in the medical and social sciences to review, aggregate and quantitatively synthesise a body of studies that address the same research question. With the volume of research growing exponentially every year, conducting meta-analyses can be costly and inefficient, as a significant amount of time and human efforts needs to be spent in finding studies meeting research criteria, annotating them, and properly performing the statistical analyses to summarise the findings. In this work, we show these issues can be tackled with semantic representations and technologies, using a social science scenario as case-study. We show how the domain-specific content of research outputs can be represented and used to facilitate their search, analysis and synthesis. We present the very first representation of the domain of human cooperation, and the application we built on top of this to help experts in performing meta-analyses semi-automatically. Using few application scenarios, we show how our approach supports the various phases meta-analyses, and more in general contributes towards research replication and automated hypotheses generation.


title: Incremental Multi-source Entity Resolution for Knowledge Graph Completion

text: We present and evaluate new methods for incremental entity resolution as needed for the completion of knowledge graphs integrating data from multiple sources. Compared to previous approaches we aim at reducing the dependency on the order in which new sources and entities are added. For this purpose, we consider sets of new entities for an optimized assignment of them to entity clusters. We also propose the use of a light-weight approach to repair entity clusters in order to correct wrong clusters. The new approaches are integrated within the FAMER framework for parallel and scalable entity clustering. A detailed evaluation of the new approaches for real-world workloads shows their high effectiveness. In particular, the repair approach outperforms other incremental approaches and achieves the same quality than with batch-like entity resolution showing that its results are independent from the order in which new entities are added.


title: Piveau: A Large-Scale Open Data Management Platform Based on Semantic Web Technologies

text: The publication and (re)utilization of Open Data is still facing multiple barriers on technical, organizational and legal levels. This includes limitations in interfaces, search capabilities, provision of quality information and the lack of definite standards and implementation guidelines. Many Semantic Web specifications and technologies are specifically designed to address the publication of data on the web. In addition, many official publication bodies encourage and foster the development of Open Data standards based on Semantic Web principles. However, no existing solution for managing Open Data takes full advantage of these possibilities and benefits. In this paper, we present our solution “Piveau”, a fully-fledged Open Data management solution, based on Semantic Web technologies. It harnesses a variety of standards, like RDF, DCAT, DQV, and SKOS, to overcome the barriers in Open Data publication. The solution puts a strong focus on assuring data quality and scalability. We give a detailed description of the underlying, highly scalable, service-oriented architecture, how we integrated the aforementioned standards, and used a triplestore as our primary database. We have evaluated our work in a comprehensive feature comparison to established solutions and through a practical application in a production environment, the European Data Portal. Our solution is available as Open Source. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-49461-2_38) contains supplementary material, which is available to authorized users.


title: Astrea: Automatic Generation of SHACL Shapes from Ontologies

text: Knowledge Graphs (KGs) that publish RDF data modelled using ontologies in a wide range of domains have populated the Web. The SHACL language is a W3C recommendation that has been endowed to encode a set of either value or model data restrictions that aim at validating KG data, ensuring data quality. Developing shapes is a complex and time consuming task that is not feasible to achieve manually. This article presents two resources that aim at generating automatically SHACL shapes for a set of ontologies: (1) Astrea-KG, a KG that publishes a set of mappings that encode the equivalent conceptual restrictions among ontology constraint patterns and SHACL constraint patterns, and (2) Astrea, a tool that automatically generates SHACL shapes from a set of ontologies by executing the mappings from the Astrea-KG. These two resources are openly available at Zenodo, GitHub, and a web application. In contrast to other proposals, these resources cover a large number of SHACL restrictions producing both value and model data restrictions, whereas other proposals consider only a limited number of restrictions or focus only on value or model restrictions.


title: Entity Extraction from Wikipedia List Pages

text: When it comes to factual knowledge about a wide range of domains, Wikipedia is often the prime source of information on the web. DBpedia and YAGO, as large cross-domain knowledge graphs, encode a subset of that knowledge by creating an entity for each page in Wikipedia, and connecting them through edges. It is well known, however, that Wikipedia-based knowledge graphs are far from complete. Especially, as Wikipedia’s policies permit pages about subjects only if they have a certain popularity, such graphs tend to lack information about less well-known entities. Information about these entities is oftentimes available in the encyclopedia, but not represented as an individual page. In this paper, we present a two-phased approach for the extraction of entities from Wikipedia’s list pages, which have proven to serve as a valuable source of information. In the first phase, we build a large taxonomy from categories and list pages with DBpedia as a backbone. With distant supervision, we extract training data for the identification of new entities in list pages that we use in the second phase to train a classification model. With this approach we extract over 700k new entities and extend DBpedia with 7.5M new type statements and 3.8M new facts of high precision.


title: Estimating Characteristic Sets for RDF Dataset Profiles Based on Sampling

text: RDF dataset profiles provide a formal representation of a dataset’s characteristics (features). These profiles may cover various aspects of the data represented in the dataset as well as statistical descriptors of the data distribution. In this work, we focus on the characteristic sets profile feature summarizing the characteristic sets contained in an RDF graph. As this type of feature provides detailed information on both the structure and semantics of RDF graphs, they can be very beneficial in query optimization. However, in decentralized query processing, computing them is challenging as it is difficult and/or costly to access and process all datasets. To overcome this shortcoming, we propose the concept of a profile feature estimation. We present sampling methods and projection functions to generate estimations which aim to be as similar as possible to the original characteristic sets profile feature. In our evaluation, we investigate the feasibility of the proposed methods on four RDF graphs. Our results show that samples containing [Formula: see text] of the entities in the graph allow for good estimations and may be used by downstream tasks such as query plan optimization in decentralized querying.


title: Entity Linking and Lexico-Semantic Patterns for Ontology Learning

text: Ontology learning from a text written in natural language is a well-studied domain. However, the applicability of techniques for ontology learning from natural language texts is strongly dependent on the characteristics of the text corpus and the language used. In this paper, we present our work so far in entity linking and enhancing the ontology with extracted relations between concepts. We discuss the benefits of adequately designed lexico-semantic patterns in ontology learning. We propose a preliminary set of lexico-semantic patterns designed for the Czech language to learn new relations between concepts in the related domain ontology in a semi-supervised approach. We utilize data from the urban planning and development domain to evaluate the introduced technique. As a partial prototypical implementation of the stack, we present Annotace, a text annotation service that provides links between the ontology model and the textual documents in Czech.


title: Detecting Synonymous Properties by Shared Data-Driven Definitions

text: Knowledge graphs have become an essential source of entity-centric information for modern applications. Today’s KGs have reached a size of billions of RDF triples extracted from a variety of sources, including structured sources and text. While this definitely improves completeness, the inherent variety of sources leads to severe heterogeneity, negatively affecting data quality by introducing duplicate information. We present a novel technique for detecting synonymous properties in large knowledge graphs by mining interpretable definitions of properties using association rule mining. Relying on such shared definitions, our technique is able to mine even synonym rules that have only little support in the data. In particular, our extensive experiments on DBpedia and Wikidata show that our rule-based approach can outperform state-of-the-art knowledge graph embedding techniques, while offering good interpretability through shared logical rules.


title: Supporting Complex Decision Making by Semantic Technologies

text: Complex decisions require stakeholders to identify potential decision options and collaboratively select the optimal option. Identifying potential decision options and communicating them to stakeholders is a challenging task as it requires the translation of the decision option’s technical dimension to a stakeholder-compliant language which describes the impact of the decision (e.g., financial, political). Existing knowledge-driven decision support methods generate decision options by automatically processing available data and knowledge. Ontology-based methods emerged as a sub-field in the medical domain and provide concrete instructions for given medical problems. However, the research field lacks an evaluated practical approach to support the full cycle from data and knowledge assessment to the actual decision making. This work advances the field by: (i) a problem-driven ontology engineering method which (a) supports creating the necessary ontology model for the given problem domain and (b) harmonizes relevant data and knowledge sources for automatically identifying decision options by reasoners, and (ii) an approach which translates technical decision options into a language that is understood by relevant stakeholders. Expert evaluations and real-world deployments in three different domains demonstrate the added value of this method.


title: Handling Impossible Derivations During Stream Reasoning

text: With the rapid expansion of the Web and the advent of the Internet of Things, there is a growing need to design tools for intelligent analytics and decision making on streams of data. Logic-based frameworks like LARS allow the execution of complex reasoning on such streams, but it is paramount that the computation is completed in a timely manner before the stream expires. To reduce the runtime, we can extend the validity of inferred conclusions to the future to avoid repeated derivations, but this is not enough to avoid all sources of redundant computation. To further alleviate this problem, this paper introduces a new technique that infers the impossibility of certain derivations in the future and blocks the reasoner from performing computation that is doomed to fail anyway. An experimental analysis on microbenchmarks shows that our technique leads to a significant reduction of the reasoning runtime.


title: Building Linked Spatio-Temporal Data from Vectorized Historical Maps

text: Historical maps provide a rich source of information for researchers in the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their changes over time, such as the changes in the transportation networks and the decline of wetlands. It can be labor-intensive for a scientist to analyze changes across space and time in such maps, even after they have been digitized and converted to a vector format. In this paper, we present an unsupervised approach that converts vector data of geographic features extracted from multiple historical maps into linked spatio-temporal data. The resulting graphs can be easily queried and visualized to understand the changes in specific regions over time. We evaluate our technique on railroad network data extracted from USGS historical topographic maps for several regions over multiple map sheets and demonstrate how the automatically constructed linked geospatial data enables effective querying of the changes over different time periods.


title: QAnswer KG: Designing a Portable Question Answering System over RDF Data

text: While RDF was designed to make data easily readable by machines, it does not make data easily usable by end-users. Question Answering (QA) over Knowledge Graphs (KGs) is seen as the technology which is able to bridge this gap. It aims to build systems which are capable of extracting the answer to a user’s natural language question from an RDF dataset. In recent years, many approaches were proposed which tackle the problem of QA over KGs. Despite such efforts, it is hard and cumbersome to create a Question Answering system on top of a new RDF dataset. The main open challenge remains portability, i.e., the possibility to apply a QA algorithm easily on new and previously untested RDF datasets. In this publication, we address the problem of portability by presenting an architecture for a portable QA system. We present a novel approach called QAnswer KG, which allows the construction of on-demand QA systems over new RDF datasets. Hence, our approach addresses non-expert users in QA domain. In this paper, we provide the details of QA system generation process. We show that it is possible to build a QA system over any RDF dataset while requiring minimal investments in terms of training. We run experiments using 3 different datasets. To the best of our knowledge, we are the first to design a process for non-expert users. We enable such users to efficiently create an on-demand, scalable, multilingual, QA system on top of any RDF dataset.


title: SchemaTree: Maximum-Likelihood Property Recommendation for Wikidata

text: Wikidata is a free and open knowledge base which can be read and edited by both humans and machines. It acts as a central storage for the structured data of several Wikimedia projects. To improve the process of manually inserting new facts, the Wikidata platform features an association rule-based tool to recommend additional suitable properties. In this work, we introduce a novel approach to provide such recommendations based on frequentist inference. We introduce a trie-based method that can efficiently learn and represent property set probabilities in RDF graphs. We extend the method by adding type information to improve recommendation precision and introduce backoff strategies which further increase the performance of the initial approach for entities with rare property combinations. We investigate how the captured structure can be employed for property recommendation, analogously to the Wikidata PropertySuggester. We evaluate our approach on the full Wikidata dataset and compare its performance to the state-of-the-art Wikidata PropertySuggester, outperforming it in all evaluated metrics. Notably we could reduce the average rank of the first relevant recommendation by 71%.


title: A Simple Method for Inducing Class Taxonomies in Knowledge Graphs

text: The rise of knowledge graphs as a medium for storing and organizing large amounts of data has spurred research interest in automated methods for reasoning with and extracting information from this representation of data. One area which seems to receive less attention is that of inducing a class taxonomy from such graphs. Ontologies, which provide the axiomatic foundation on which knowledge graphs are built, are often governed by a set of class subsumption axioms. These class subsumptions form a class taxonomy which hierarchically organizes the type classes present in the knowledge graph. Manually creating and curating these class taxonomies oftentimes requires expert knowledge and is time costly, especially in large-scale knowledge graphs. Thus, methods capable of inducing the class taxonomy from the knowledge graph data automatically are an appealing solution to the problem. In this paper, we propose a simple method for inducing class taxonomies from knowledge graphs that is scalable to large datasets. Our method borrows ideas from tag hierarchy induction methods, relying on class frequencies and co-occurrences, such that it requires no information outside the knowledge graph’s triple representation. We demonstrate the use of our method on three real-world datasets and compare our results with existing tag hierarchy induction methods. We show that our proposed method outperforms existing tag hierarchy induction methods, although both perform well when applied to knowledge graphs.


Playing with Creativity Across the Lifespan: a Conversation with Dr. Sandra Russ


title: Minimally invasive surfactant therapy versus InSurE in preterm neonates of 28 to 34 weeks with respiratory distress syndrome on non-invasive positive pressure ventilation—a randomized controlled trial

text: Preterm neonates with respiratory distress syndrome (RDS) are commonly treated with surfactant by intubate surfactant extubate (InSurE) technique. Mode of surfactant administration has evolved towards less invasive technique in the last few years. We randomised 58 preterm infants of 28–34 weeks of gestation with RDS within 6 h of birth to receive surfactant by InSurE or minimally invasive surfactant therapy (MIST). Non-invasive positive pressure ventilation (NIPPV) was used as primary respiratory support. The main objective was to compare the need of invasive mechanical ventilation (IMV) in first 72 h of life and secondarily hemodynamically significant patent ductus arteriosus (hsPDA), intraventricular haemorrhage (IVH) (> grade 2), bronchopulmonary dysplasia (BPD) and composite outcome of BPD/mortality. We did not find any difference in need of IMV in first 72 h between MIST and InSurE (relative risk with MIST, 0.62; 95% confidence interval, 0.22 to 1.32). No difference was observed in terms of hs PDA, IVH (> grade 2), BPD and composite outcome of BPD/mortality. Conclusion: There is no difference between MIST and InSurE in preterm neonates with RDS with NIPPV as a primary mode of respiratory support. Larger multicentre studies are needed to further explore differences in treatment failure and other secondary outcomes. Trial registration: www.ctri.nic.in id CTRI/2019/03/017992, registration date March 8, 2019.


docid: uvptbxuh

title: Focus on better care and ethics: Are medical ethics lagging behind the development of new medical technologies?


title: Modernity, Catastrophe, and Realism in the Postcolonial Indian Novel

text: British colonialism in South Asia was marked by modernisation programmes of agriculture and industry. These programmes resulted in a long-term agrarian and food crisis which then led to a number of catastrophic events in the long twentieth century. The present book is about three such events from India’s late-colonial and postcolonial periods—the 1943–1944 Bengal famine, the 1967–1972 Naxalbari movement, and the 1975–1977 national emergency. It argues that these events are all linked with the historical crisis and yet distinct in their nature and orientation. The chapter contends that such a condition of relation through difference can be better understood via a reading of form and mode of the novels of these events. It shows that writers have predominantly used the realist form to represent the long-term historical crisis, while they have employed a range of modes—from social realist, critical realist to metafictional, urban fantastic, gothic, and others (many of which are conventionally understood as anti-realist or non-realist). These modes have shaped their realisms into a deeply heterogeneous and dynamic form, and, in so doing, composed the aesthetic framework of catastrophe-prone, crisis-ridden condition of life and living in postcolonial India, which is called here as “catastrophic realism.” The chapter situates the link between crisis and event by drawing upon the works of Fredric Jameson, Veena Das, Shahid Amin, and Louis Althusser. Its take on catastrophic realism is mediated through a thorough reading of realism, form, and mode in the European and postcolonial/Indian contexts, where Sa’adat Hasan Manto’s pre- and post-partition stories are offered as examples.


title: Using Historical Scientific Controversies to Promote Undergraduates’ Argumentation

text: The Covid-19 pandemic is the reason why humanity is paying more attention to the importance of regular and rigorous handwashing. Interestingly, in the nineteenth century, regular and rigorous handwashing was a key (and controversial) solution proposed by the Hungarian obstetrician Ignaz Philipp Semmelweis to cut drastically cases of puerperal fever. The purpose of this study was to provide evidence that the case of Semmelweis and puerperal fever—a crucial historical scientific controversy—can be used as a springboard to promote university student argumentation. Our study was inspired by the fact that the Organization for Economic and Cooperative Development (OECD) stressed that more efforts and resources should be invested in promoting argumentation as an essential component for scientifically literate citizens in twenty-first century societies. However, nowadays, argument and debate are virtually absent from university science education. The data was derived from 124 undergraduates’ (64 females and 60 males, 15–30 years old) written responses and audio and video recordings in a university biology course in Colombia. The findings show that the articulation of this historical controversy with decision-making, small-group debate, and whole-class debate activities can be useful for promoting undergraduates’ argumentation. This study contributes to the development of a research-based university science education that can inform the design of an argumentation curriculum for higher education.


title: Circulating fibrocytes traffic to the lung in murine acute lung injury and predict outcomes in human acute respiratory distress syndrome: a pilot study

text: BACKGROUND: Fibrosis is an integral component of the pathogenesis of acute lung injury and is associated with poor outcomes in patients with acute respiratory distress syndrome (ARDS). Fibrocytes are bone marrow-derived cells that traffic to injured tissues and contribute to fibrosis; hence their concentration in the peripheral blood has the potential to serve as a biomarker of lung fibrogenesis. We therefore sought to test the hypothesis that the concentration and phenotype of circulating fibrocytes in patients with ARDS predicts clinical outcomes. METHODS: For the animal studies, C57Bl/6 mice were infected with experimental Klebsiella pneumoniae in a model of acute lung injury; one-way ANOVA was used to compare multiple groups and two-way ANOVA was used to compare two groups over time. For the human study, 42 subjects with ARDS and 12 subjects with pneumonia (without ARDS) were compared to healthy controls. Chi-squared or Fisher’s exact test were used to compare binary outcomes. Survival data was expressed using a Kaplan-Meier curve and compared by log-rank test. Univariable and multivariable logistic regression were used to predict death. RESULTS: In mice with acute lung injury caused by Klebsiella pneumonia, there was a time-dependent increase in lung soluble collagen that correlated with sequential expansion of fibrocytes in the bone marrow, blood, and then lung compartments. Correspondingly, when compared via cross-sectional analysis, the initial concentration of blood fibrocytes was elevated in human subjects with ARDS or pneumonia as compared to healthy controls. In addition, fibrocytes from subjects with ARDS displayed an activated phenotype and on serial measurements, exhibited intermittent episodes of markedly elevated concentration over a median of 1 week. A peak concentration of circulating fibrocytes above a threshold of > 4.8 × 10(6) cells/mL cells correlated with mortality that was independent of age, ratio of arterial oxygen concentration to the fraction of inspired oxygen, and vasopressor requirement. CONCLUSIONS: Circulating fibrocytes increase in a murine model of acute lung injury and elevation in the number of these cells above a certain threshold is correlated with mortality in human ARDS. Therefore, these cells may provide a useful and easily measured biomarker to predict outcomes in these patients.


Firearms, pesticides, and suicide: A look back for a way forward


title: Onboarding: How Newcomers Integrate into an Agile Project Team

text: Although a stable team is deemed optimal for agile project success, new team members need to join ongoing agile projects. Newcomers must rapidly assimilate into the organisational and project environment while learning how to contribute effectively to the project and integrate into the team without seriously interrupting project progress. This paper addresses how newcomers integrate into an established agile project team and the challenges newcomers and the team face during this process. This paper is a single case study of a co-located agile project team in a large IT department who regularly onboard inexperienced newcomers. We found a mixture of traditional onboarding practices and specific agile practices contribute to the onboarding process. Onboarding challenges include empowerment and mindset change, accommodating part-timers, conveying agile principles, and adjusting to changes in team composition.


title: A Quantitative Exploration of the 9-Factor Theory: Distribution of Leadership Roles Between Scrum Master and Agile Team

text: A number of qualitative studies find that team leadership is one essential success factor for evolving into a mature agile team. One such qualitative study suggests the 9-Factor Theory of Scrum Master roles, which claims that the Scrum Master performs a set of 9 leadership roles which are transferred to the team over time [14]. We aimed at conducting a quantitative exploration that examines the presence and change of the 9-Factor Theory in relation to team maturity. We conducted an online survey with 67 individuals at the conglomerate Robert Bosch GmbH. Descriptive statistics reveal that the Scrum Master and the agile team score differently on the 9 factors and that the Scrum Master role is most often distributed in teams that had been working between 3 and 5 months in an agile manner. Yet, we also find that the leadership roles predominantly remain with one dedicated Scrum Master. Based on our results we suggest to group the 9-Factor Theory into three clusters: the Scrum Master is rather linked to psychological team factors (1), while the team tends to be linked to rather product-related factors (2). Organizational factors (3) are less often present. Our practical implications suggest an extension of the Scrum Master description. Furthermore, our study lays groundwork for future quantitative testing of leadership in agile teams.


title: On the Use of Design Thinking: A Survey of the Brazilian Agile Software Development Community

text: Design Thinking (DT) has been chosen as an approach to support problem-solving by many software development companies. However, there are divergences between the professionals of these companies concerning which techniques are performed, which steps are followed, and the way to implement this approach, as it proposes itself, to be divergent to generate numerous alternatives and, also, convergent, to find a solution. For this reason, aiming to characterize how the software companies have been implemented DT, this paper presents the results of a survey answered by 127 professionals from the Brazilian software industry. The results report a variety of scenarios in which DT has been applied: more than ten different models (sets of steps) are followed by the professionals; more than 50 techniques have been used, mainly, for meeting the needs in the process, according to the context of use and based on previous experiences. We also present 29 computational tools that, according to the respondents, assist the execution of DT, in addition to the integration with agile methods, allowing them to generate ideas and solutions, to explore and understand the problem.


title: “I Don’t Understand!”: Toward a Model to Evaluate the Role of User Story Quality

text: User stories are popular for conveying requirements in agile software projects. Despite existing quality criteria, authors make formal mistakes that result in “bad” user story quality. If developers have insufficient experience in balancing quality problems, the creation of a shared mental model is impossible, thus increasing the risk of impacts on the project’s success. This article provides a work-in-progress research model to set these variables in relation and establish a systematic method to uncover answers regarding their correlation. Details on the effects support research in agile requirements engineering to gain a better understanding of cognitive processes in the comprehension of user stories. In addition, insights can help to develop design recommendations and AI tools to improve user stories. A first evaluation of the model provides promising insights into the behavior and forms a basis for future research.


docid: tckghfwd

title: Large-Scale Agile Transformation: A Case Study of Transforming Business, Development and Operations

text: Today, product development organizations are adopting agile methods in units outside the software development unit, such as in sales, market, legal, operations working with the customer. This broader adoption of agile methods has been labeled large-scale agile transformation and is considered a particular type of organizational change, originating in the software development units. So far, there is little research-based advice on conducting such transformations. Aiming to contribute towards providing relevant research advice on large-scale agile transformation, we apply a research-based framework for evaluating organizational agility on a product development program in a maritime service provider organization. We found that doing a large-scale agile transformation involves many significant challenges, such as having a shared understanding of the problem, getting access to users, and getting commitment to change that needs to be done. In order to overcome such challenges, we discuss the need for a holistic and integrated approach to agile transformation involving all the units linked to software development.


title: Jeffersonian Directions in Curricular Renovation

text: In a contemporary update to Jefferson’s spirit of curricular innovation and experimentation, the final chapter contends that the best way for the public schools to meet the evolving needs of American youth would be for them to implement a Jeffersonian-inspired reconstruction of educational purpose. The author maintains that the schools should “re-found” their fundamental civic purposes to better align themselves with that which is most revolutionary about the American Revolution. To achieve these ends, the final section of the book offers a K-12 Curricular Redesign. It highlights the need to restore the humanities to their place of privilege within the curriculum. It does so by reorganizing the curriculum to meet the evolving needs of American youth: the need to recover their civic selfhood, the need to become ecologically literate, and the need to be able to distinguish between truth and falsity in the age dominated by social media.


title: Agile Implementation and Expansive Learning: Identifying Contradictions and Their Resolution Using an Activity Theory Perspective

text: A key challenge organisations face when transitioning to agile delivery methods is that of quickly and effectively learning new ways of working. This study posits that fundamental historical, cultural and behavioural aspects affect the transition and contribute to the poor performance of many agile implementations. In order to address such factors, this study applies a modified Activity Theory (AT) based framework to a case study agile implementation within a large public sector organisation. An activity is closely defined, and six generic activities associated with all agile implementations are identified. These are validated against the agile maturity model literature and a set of evaluation criteria of contradictions, congruences and collaboration is established. Evidence is gathered from participant interviews and the framework is used to surface learning and development obstacles and issues within an expansive learning cycle. The study argues that analysis via this modified AT framework brings original insight. Initial findings indicate that there are relatively few learning and development issues associated with the use of agile tools and techniques themselves and that most problems arise at the interface where the “changed” (more agile) delivery teams meet the organisation’s behavioural norms and practices.


title: Agile Software Development Practices and Success in Outsourced Projects: The Moderating Role of Requirements Risk

text: Although agile practices are gaining in popularity, there is little evidence showing how particular agile practices, in particular those involving the client, affect the success of outsourced software projects. Data from a matched survey of sponsors and developers in 60 outsourced information systems projects indicate negative effects of continuous analysis and positive effects of joint decision making and continuous integration on project success. Moreover, interaction analyses show that some positive effects are enhanced and negative effects dampened when requirements risk is high. These findings caution against continuous analysis in outsourced projects while they support joint decision making and continuous integration. The findings also empirically substantiate the largely untested assertion that agile practices help cope with changing requirements.


title: What an Agile Leader Does: The Group Dynamics Perspective

text: When large industrial organizations change to (or start with) an agile approach to operations, managers and some employees are supposed to be “agile leaders” often without being given a clear definition of what that comprises when building agile teams. An inductive thematic analysis was used to investigate what 15 appointed leaders actually do and perceive as challenges regarding group dynamics working with an agile approach. Team maturity, Team design, and Culture and mindset were all categories of challenges related to group dynamics that the practitioners face and manage in their work-life that are not explicitly mentioned in the more process-focused agile transformation frameworks. The results suggest that leader mitigation of these three aspects of group dynamics is essential to the success of an agile transformation.


title: “This is Just a Prototype”: How Ethics Are Ignored in Software Startup-Like Environments

text: Artificial Intelligence (AI) solutions are becoming increasingly common in software development endeavors, and consequently exert a growing societal influence as well. Due to their unique nature, AI based systems influence a wide range of stakeholders with or without their consent, and thus the development of these systems necessitates a higher degree of ethical consideration than is currently carried out in most cases. Various practical examples of AI failures have also highlighted this need. However, there is only limited research on methods and tools for implementing AI ethics in software development, and we currently have little knowledge of the state of practice. In this study, we explore the state of the art in startup-like environments where majority of the AI software today gets developed. Based on a multiple case study, we discuss the current state of practice and highlight issues. The cases underline the complete ignorance of ethical consideration in AI endeavors. We also outline existing good practices that can already support the implementation of AI ethics, such as documentation and error handling.


docid: hldrkaxv

title: Improving Risk Management in a Scaled Agile Environment

text: Agile methods are designed for handling uncertainty as well as reducing risks in product development through transparency, inspection, and adaptation. Applying an effective risk management is in the nature of agile methods. However, when multiple agile teams work on the same product, a higher coordination effort is required and more formal practices are applied. The objective of this paper is to study how risk management can be improved in a scaled agile environment. Therefore, we conducted a case study in a large-sized ecommerce company and interviewed several project managers. The results show that there are differences for risk management in terms of two contexts. On the one hand, informal risk management is rated as good enough for one autonomous team. On the other hand, more formal approaches are needed, when several teams work on the same requirement. Furthermore, a tool for the support of risk management in a scaled agile environment is presented. We can conclude that hybrid development approaches consisting of agile practices and traditional practices, are beneficial, when several teams work in parallel.


title: “When in Rome, Do as the Romans Do”: Cultural Barriers to Being Agile in Distributed Teams

text: With the growing interest of adopting agile methods in offshored process, many companies realized that the use of agile methods and practices in companies located outside the location of early adopters of agile methods may be challenging. India, the main destination of offshoring contracts, have received particular attention, due to the big cultural differences. Critical analysis of related studies suggests that impeding behaviors are mostly rooted in the hierarchical culture of Indian organizations and related management behavior of command-and-control. But what happens in distributed projects with a more empowering onshore management? In this paper, we present the findings from a multiple-case study of DevOps teams with members from a mature agile company located in Sweden and a more hierarchical offshore vendor from India. Based on two focus groups we list culturally different behaviors of offshore engineers that were reported to impede agile ways of working. Furthermore, we report the findings from surveying 36 offshore team members from five DevOps teams regarding their likely behavior in situations reported to be problematic. Our findings confirm a number of previously reported behaviors rooted in cultural differences that impede the adoption of agile ways of working when collaborating with offshore engineers. At the same time, our survey results suggest that among the five surveyed teams there were teams that succeeded with the cultural integration of the offshore team members. Finally, our findings demonstrate the importance of cultural training especially when onboarding new team members.


title: Hypotheses Elicitation in Early-Stage Software Startups Based on Cognitive Mapping

text: Software startups develop innovative products for which there are typically no customers to refer to elicit requirements. Often, these companies develop a set of features without a better understanding of customer needs. An experiment-based approach to validate hypotheses about the customer and market could increase their chance of success or, at least, accelerate their realization of the product worthlessness. The first step of an experiment-based approach is to elicit hypotheses to guide experiments. Software startups base their products on business assumptions, but there is a lack of understanding of how these assumptions are formed and how teams could elicit hypotheses systematically. To fill this gap, we performed an empirical study consisted of two steps. First, we explored based on which assumptions startups define their products using a multiple case study. The results indicate that these companies developed their products based on founders’ assumptions derived from their previous experience. Second, we investigated cognitive mapping as a tool to elicit hypotheses systematically with two software startups. The results indicate that this approach can serve as the basis of a method to elicit hypotheses in early-stage software startups.