A Graphical Humor Ontology for Contemporary Cultural Heritage Access

Humor is a multifaceted communication phenomenon that has been approached from different perspectives in existing research studies. Such heterogeneity of views poses a challenge to the creation of metadata records for digital humor assets, if rich interpretive and descriptive sentences are desired for the construction of semantic access interfaces. Ontologies in the context of Semantic Web research provide the appropriate flexible representational framework for describing the many aspects and interpretations of digital humor artefacts. This paper reports the initial design of an ontology of humor artefacts engineered as part of a project that deals with semantic access to contemporary Spanish graphical humor. The main ontological commitments adopted – semantic frames and humor technique applications - are described, along with the annotation technique used and some illustrative examples of the richness that the approach provides to browsing interfaces

Contrasting Knowledge Organization Systems for the Description of Research Products: the Case of Overlapping in the Agricultural Domain

AbstractThe use of Knowledge Organization Systems (KOS) as ontologies or terminologies for the description of scholarly contents requires a careful consideration of the domain and the KOS available. KOS in the same domain may differ in several dimensions including purpose, level of formality, structure and language. In consequence, curators of scientific data face the problem of selecting the relevant KOS, developing mappings when appropriate and deciding on their usage for annotating resources. In domains in which more than a KOS is available, curators need tools to help them in the decision making process. Due to the available heterogeneity of KOS, exploratory tools are required for an initial assessment of overlapping and differences. This paper reports on a practical experience using simple mapping analysis and mapping visualizations in the domain of agriculture. These techniques represent promising directions for the development of decision tools based on the contrast of different KOS metrics

Sentiment analysis of COVID-19 cases in Greece using Twitter data.

Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics for the last two decades, using different sources from social media to search engine records. More recently, studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of pandemics. Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the sentiment impact of COVID-19 cases in Greece in real time as related to cases. Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year and were examined towards two sentimental lexicons: one in English language translated into Greek (using the Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness, Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data. Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of COVID-19. The correlation coefficient (R2 ) for the Vader lexicon is &#8722; 0.07454 related to cases and &#8722; 0.,70668 to the tweets, while the other lexicon had 0.167387 and &#8722; 0.93095 respectively, all measured at significance level of p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the interest in COVID-19 declined after a certain time

Combining VIVO and Google Scholar data as sources for CERIF linked data: a case in the agricultural domain

The needs of global science have fostered open access to the results and contextual information of research organizations at an international scale. This requires the use of standards or shared data models to exchange information preserving its semantics when transferred between systems. In that direction, standards as CERIF or projects as VIVO were developed to exchange or expose the scientific knowledge. Also, there are other sources of scientific information in the Web that are useful to complement institutional repositories and CRISes. The heterogeneity of data models behind each source in turn raises the need for mappings between them to ease interchange and aggregate information. In this paper, we present a tool that integrates three sources of research information and enables their aggregating and export into both VIVO and CERIF models. We present a case study in agriculture using OpenAGRIS, a bibliographic database linked to Web sources with more than 7 million records. Concretely, we describe the methods to combine Google Scholar data for the scholarly content indexed in OpenAGRIS and aggregating new information provided by the first one, using our tool. Finally the information is stored in a VIVO instance and then translated into CERIF using a conversion process mapping both data models. The case demonstrates the possibilities of mapping tools to aggregate and translate CRIS information

dataTEL - Datasets for Technology Enhanced Learning

The dataTEL white paper develop during the dataTEL workshop at the ARV2011. The workshop was motivated by the issue that very less educational datasets are publicly available in TEL, so that the outcomes of different TEL adaptive applications and recommender systems that support personalised learning are hardly comparable. In other domains like in e-commerce it is a common practise to use different datasets as benchmarks to evaluate recommender systems algorithms to make the results comparable (MovieLens, Book-Crossing, EachMovie dataset). So far, no universally valid knowledge exists in TEL on algorithm that can be successfully applied in a certain learning setting to personalise learning. Having a collection of datasets could be a first major step towards a theory of personalisation with in TEL that can be based on empirical experiments with verifiable and valid results. Therefore, the main objective of the dataTEL workshop was to explore suitable datasets for TEL with a specific focus on recommender and adaptive information systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required preprocessing procedures and rules to create sharable datasets, common evaluation criteria for recommender systems in TEL and how a dataset driven future in TEL could look like

STELLAR Alpine Rendez-Vous White Paper

Drachsler, H., Verbert, K., Sicilia, M. A., Wolpers, M., Manouselis, N., Vuorikari, R., Lindstaedt, S., & Fischer, F. (2011). dataTEL - Datasets for Technology Enhanced Learning. STELLAR Alpine Rendez-Vous White Paper. Alpine Rendez-Vous 2011 White paper collection, Nr. 13., France (2011) Accessible at: http://oa.stellarnet.eu/open-archive/browse?resource=6756_v1The dataTEL white paper develop during the dataTEL workshop at the ARV2011. The workshop was motivated by the issue that very less educational datasets are publicly available in TEL, so that the outcomes of different TEL adaptive applications and recommender systems that support personalised learning are hardly comparable. In other domains like in e-commerce it is a common practise to use different datasets as benchmarks to evaluate recommender systems algorithms to make the results comparable (MovieLens, Book-Crossing, EachMovie dataset). So far, no universally valid knowledge exists in TEL on algorithm that can be successfully applied in a certain learning setting to personalise learning. Having a collection of datasets could be a first major step towards a theory of personalisation within TEL that can be based on empirical experiments with verifiable and valid results. Therefore, the main objective of the dataTEL workshop was to explore suitable datasets for TEL with a specific focus on recommender and adaptive information systems that can take advantage of these datasets. In this context, new challenges emerge like unclear legal protection rights and privacy issues, suitable policies and formats to share data, required preprocessing procedures and rules to create sharable datasets, common evaluation criteria for recommender systems in TEL and how a dataset driven future in TEL could look like.dataTEL, NeLLL AlterEgo, STELLAR, MAVSE

Behavioral immune landscapes of inflammation.

Transcriptional or proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues. These approaches, however, fail to describe dynamic scenarios in which cells can change their biochemical properties and downstream “behavioral” outputs every few seconds or minutes. Here, we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamism of individual leukocytes at sites of active inflammation. By analyzing over 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioral descriptors of individual cells and used these high-dimensional datasets to build behavioral landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and inside blood vessels uncovered a continuum of neutrophil states, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioral in vivo screening of thousands of cells from 24 different mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and genetic or pharmacological interference of Fgr protected from inflammatory injury. Thus, behavioral landscapes report unique biological properties of dynamic environments at high cellular, spatial and temporal resolution.pre-print4302 K

Entities and Identities in Research Informatio​n Systems

Where science is increasingly becoming a global business and furthermore with open data and access initiatives, means to identify and thus to connect system-internal with non-internal entities are urgently needed. The need for identifiers beyond systems is thus a global requirement but also relevant within organization boundaries spanning multiple systems. Various identifier initiatives and systems have started in the scientific domain and beyond. However, they have not yet achieved the required interoperability. With this paper we aim to collect additional essential ingredients towards designing a sustainable CERIF identifier sub model, contributing to ongoing discussions and supporting design decisions. Here, we first present common issues with identifiers in the wider academic domain, before we analyze available systems, technologies and current initiatives solving the global identity gap

Handbook of metadata, semantics and ontologies

Metadata research has emerged as a discipline cross-cutting many domains, focused on the provision of distributed descriptions (often called annotations) to Web resources or applications. Such associated descriptions are supposed to serve as a foundation for advanced services in many application areas, including search and location, personalization, federation of repositories and automated delivery of information. Indeed, the Semantic Web is in itself a concrete technological framework for ontology-based metadata. For example, Web-based social networking requires metadata describing people an