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We investigate the psycho-linguistic features of the online discourse over climate change, focusing on its modifications throughout the years 2017–2019 as a result of collective actions emerging and spreading worldwide. We seek to understand the emerging connection between digital activism and the psychological processes related to its social drives. To this end, a semantic network is derived from the social platform Twitter, and its evolution is traced over time, tracking textual proxies of social identity and empowerment. Original proposals are made to identify communities and highlight the most important semantic contents of the corpus from a network perspective. These evaluations on semantic communities of related concepts further detail the shift in the rhetoric of collective actions. Finally, we explore projection of the ingroup to the future in the online discourse about climate change, which can point to developments of pro-environmental campaigns. • We have recently experienced a steady increase of collective engagement for climate. • Ranking and community detection uncover the key elements of the online discourse around climate change. • Climate activism is fostered by social drives common to general collective protest. • Affiliation and empowerment characterize the calling to action for climate. • Ingroup mobilization toward the future is specific to 2019 climate activism. [ABSTRACT FROM AUTHOR]

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. We aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate the pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing linguistic data. The third package, SemNeT, provides methods and measures for estimating and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis. [ABSTRACT FROM AUTHOR]

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Copyright of Papers: Revista de Sociologia is the property of Universitat Autonoma de Barcelona and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Investigación Bibliotecológica is the property of UNAM, Centro Universitario de Investigaciones Bibliotecologicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Collegium Antropologicum is the property of Croatian Anthropological Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study is intended to provide in-depth insights into how design thinking and creativity issues are understood and possibly evolve in the course of design discussions in a company context. For that purpose, we use the seminar transcripts of the Design Thinking Research Symposium 12 (DTRS12) dataset "Tech-centred Design Thinking: Perspectives from a Rising Asia," which are primarily concerned with how Korean companies implement design thinking and what role designers currently play. We employed a novel method of information processing based on constructed dynamic semantic networks to investigate the seminar discussions according to company representatives and company size. We compared the quantitative dynamics in two seminars: the first involved managerial representatives of four companies, and the second involved specialized designers and management of a design center of single company. On the basis of dynamic semantic networks, we quantified the changes in four semantic measures—abstraction, polysemy, information content, and pairwise word similarity—in chronologically reconstructed individual design-thinking processes. Statistical analyses show that design thinking in the seminar with four companies, exhibits significant differences in the dynamics of abstraction, polysemy, and information content, compared to the seminar with the design center of single company. Both the decrease in polysemy and abstraction and the increase in information content in the individual design-thinking processes in the seminar with four companies indicate that design managers are focused on more concrete design issues, with more information and less ambiguous content to the final design product. By contrast, specialized designers manifest more abstract thinking and appear to exhibit a slightly higher level of divergence in their design processes. The results suggest that design thinking and creativity issues are articulated differently depending on designer roles and the company size. • Design conversations are analyzed with an information-processing method based on semantic networks. • Design thinking in Korean companies is understood based on temporal dynamics of semantic networks. • Dynamic changes in abstraction, polysemy, information content and semantic similarity are quantified. • Individual design thinking is articulated differently depending on the designer role and company size. • AI co-creative partner programs employing semantic networks could assist the work of human designers. [ABSTRACT FROM AUTHOR]

Copyright of Informacao & Sociedade: Estudos is the property of Universidade Federal de Campina Grande, Centro de Humanidades and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This Commentary examines where the creativity research has been and where it is going. They key points include evolving definitions of creativity, interdisciplinarity in the creativity research, divergent thinking as a reliable index of creative potential, improvements in testing, the impact of technology, the inclusion of political, moral, and everyday creativity in the research, the use of semantic networks and conceptual maps, the creative economy, and the need to extricate personal creativity from social recognition. [ABSTRACT FROM AUTHOR]

Copyright of Redes is the property of Redes-Revista Hispana para el Analisis de Redes Sociales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Employed network science to analyze semantic memory structures related to convergent thinking and problem-solving styles on Remote Associates Test. • High-convergent thinkers had more flexible, interconnected networks; insight solvers showed shorter path distances between concepts, controlling for accuracy. • Semantic memory organization is crucial for convergent thinking and insight problem solving, beyond accuracy. The associative theory of creativity has long held that creative thinking involves connecting remote concepts in semantic memory. Network science tools have recently been applied to map the organization of concepts in semantic memory, and to study the link between semantic memory and creativity. Yet such work has largely overlooked the domain of convergent thinking, despite the theoretical importance of semantic memory networks for facilitating associative processes relevant for convergent problem solving (e.g., spreading activation). Convergent thinking problems, such as the Compound Remote Associates (CRA) test, can be solved with insight (the sudden "aha" experience) or analysis (deliberately and incrementally working towards the solution). In a sample of 477 participants, we adopted network science methods to compare semantic memory structure across two grouping variables: 1) convergent thinking ability (i.e., CRA accuracy), and 2) the self-reported tendency to solve problems with insight or analysis. Semantic memory networks were constructed from a semantic fluency task, and problem solving style (insight or analysis) was determined from judgments provided during solving of CRAs. We found that, compared to the low-convergent thinking group, the high-convergent thinking group exhibited a more flexible and interconnected semantic network—with short paths and many connections between concepts. Moreover, participants who primarily solved problems with insight (compared to analysis) showed shorter average path distances between concepts, even after controlling for accuracy. Our results extend the literature on semantic memory and creativity, and suggest that the organization of semantic memory plays a key role in convergent thinking, including insight problem solving. [ABSTRACT FROM AUTHOR]

The high importance of a proper definition of software architecture is obvious, but this task is not easy, especially in large projects. The project scheme should be legibly structured and clearly visualized. To that aim, usually widespread UML class diagrams are used. However, this way is not flexible and clear enough at the initial state of the development, so there is a need of a more appropriate method. Semantic networks are not so commonly used to meet these demands. Nevertheless, this notation is a fairly suitable approach to building evolutionary computing system architectures. Also, this can be a good mediator for other formats. Moreover, the tools for that may become quite powerful and relevant. [ABSTRACT FROM AUTHOR]

Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married). [ABSTRACT FROM AUTHOR]

We describe the construction of a bilingual (English-Russian /Russian-English) semantic network covering basic concepts of computing. To construct the semantic network, we used the Computing Curricular series created during 2000-2015 under the aegis of ACM and IEEE and the current standards of IT specialists training in Russia, as well as some other English language and Russian language sources. The resulting network can be used as a basic component in an intelligent information system that allows processing bilingual search queries while considering their semantics and to help support and guide automated translation efforts of academic texts from one language to the other. The network can also be useful to support comparative analysis and integration of the programs and teaching materials for Computing and IT education in Russia and English speaking countries. This network can support cross-lingual information retrieval, knowledge management and machine translation, which play an important role in e-learning personalization and retrieval in the computing domain, thus allowing to benefit from online educational resources that are available in both languages. [ABSTRACT FROM AUTHOR]

Semantic relationship among existing concepts in a text can be a solution for better and more precise representation of its content. in addition to common indexing methods. Semantic networks are a pace in this way. In this research, we have attempted to extract the concepts related to the Allusions (Talmihat) in Hafez's sonnets from six most frequently cited commentaries. The implied and literal meanings are extracted and separated subsequently, they are categorized in order to create connection among them and to draw their semantic networks. This effort improves retrieval in Hafez's poetry based on audience needs. quantitative and qualitative content analysis are employed here. Quantitative content analysis is used to determine the frequency of concepts. Qualitative content analysis is employed to obtain the meanings of verses and to determine the implied and verbal meanings, and explore relationship among concepts, Both has served to draw a semantic network. The concepts and their relationship between them were identified and two semantic networks were drawn. With this contrivance, the distance and proximity of the main node (Hafez) and from each other, and also the number of poems belonging to concept were determined. Thus, tangible understanding of allusions in the poems of Hafez becomes possibleand systematic and, to some extent hierarchical relationship among them can be visualized. Facilitating retrieval is the most important using the semantic networks. Ssearching in these networks: users can 1) find the poems that they need 2) view the position of the subject throughout networks and 3) distinguish relationship among subjects in respect with other diagnoses. [ABSTRACT FROM AUTHOR]

Objective To analyze the characteristics of semantic networks derived from fluency tests in patients with multiple sclerosis (MS). Methods We built semantic networks by applying co-occurrence statistics to the data from verbal fluency tests performed on patients with MS ( n =36) and matched controls ( n =200), assessing the differences in network topology. Results As expected, the semantic networks from both patients and controls showed 'small-world' properties. Topological analysis of these semantic networks indicated that there were fewer nodes (words) and links (defined by significant co-occurrence of words) in those derived from MS patients. The average connectivity was not significantly affected, while the local connectivity (clustering coefficient) is preserved. Quantifiers of the cohesiveness of the network, which reflect long distance connectivity, such as assortativity and maximum centrality coefficients, differed significantly between MS patients and controls. Conclusions The analysis of semantic networks reveals quantitative differences in MS patients and identifies preferential damage of long-range connectivity. The analysis of semantic networks may be useful in clinical practice for the assessment of cognitive impairment or recovery after damage. [ABSTRACT FROM AUTHOR]

Place is a notion closely linked with the wealth of human experience, and invested by values, attitudes, and cultural influences. In particular, many places are strongly related to music, which contributes to shaping the perception and meaning of a place. In this paper we propose a computational approach to identify musicians and music suited for a place of interest (POI)--which is based on a knowledge-based framework built upon the DBpedia ontology--and a graph-based algorithm that scores musicians with respect to their semantic relatedness with a POI and suggests the top scoring ones. Through empirical experiments we show that users appreciate and judge the musician recommendations generated by the proposed approach as valuable, and perceive compositions of the suggested musicians as suited for the POIs. [ABSTRACT FROM AUTHOR]

Abstract: This paper suggests the method of modeling of project breakdown structures (WBS, OBS, TRM, network model etc.) and its relationships on the basis of ER-methodology. This data model could be considered as a semantic network and application of the logical deduction algorithm for the determination of the project breakdown structures solving sequence is also proposed. The derived sequence defines the specific project management process taking into account the restrictions and priorities of the particular project. This algorithm could be applied for project planning and change management. [Copyright &y& Elsevier]

Abstract: The paper analyses the application of grammar knowledge representation of Bulgarian inflectional morphology. We present the implementation of inflectional morphology of Bulgarian possessive and reflexive-possessive pronouns using the semantic networks interpretation. It is focused on modelling the inflectional morphology of Bulgarian possessive and reflexive-possessive pronouns using the Universal Networking Language (UNL). The formal analysis and related encodings take into account the specific morphological and syntactic properties of possessive and reflexive-possessive pronouns, and represent their inflectional morphology interpretation with respect to the feature of definiteness. The definitions of presented inflectional rules are given after the detailed analysis of both semantic and grammar features of the related pronouns. The evaluation of generated inflected forms is given by some example words. The analysis of basic problems of rule-based inflectional morphology grammar representation of Bulgarian possessive and reflexive-possessive pronouns is offered, and the semantic network using rule-based knowledge representation and non-monotonic reasoning in UNL framework with related encoding is presented. The particular model is explained in terms of its linguistic motivation. [Copyright &y& Elsevier]

There is considerable attention in the literature to concepts including corporate branding, corporate identity, corporate image, corporate social responsibility, and corporate reputation. Nevertheless, it is not known how the terms are associated with one another in news documents about corporations. This research tests hypotheses about these concepts in natural language discourse in the press in relation to corporate reputation. From the Reputation Institute's 2009 Global Pulse2 ratings of 600 world corporations the top 30 and the bottom 30 in reputation are examined by mapping semantic networks containing crisis, corporate communication terms, and the corporation names. News documents are compared to see whether the top corporation names are closer to these communication terms and further from the crisis term, compared to the bottom-ranked corporations. [ABSTRACT FROM AUTHOR]

The advent of the participative Internet (Web 2.0) sheds a new light on traditional knowledge about communication practices. The role of information and communication technologies seems to be very central in managerial literature, while the human side of the issue is less considered. This paper argues that individuals establish communication genres as semantic templates for accomplishing their communicative projects. Communication genres are codes of default behavioral expectations resulting from recurrent communication actions over the time. By using semantic network analytical techniques, our argument is explored in a particular empirical setting, that is, a virtual community of open source software development. [ABSTRACT FROM AUTHOR]

Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localised environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources. • COVID-19 symptoms modelling using mobile data and complex networks. • Monitoring the progression of COVID-19 in specific regions. • Evaluating the severity of COVID-19 symptoms in specific groups of the population. • Characterising the symptom pattern for the circulating viral strains. • Supporting public health decisions in selective lockdown strategies. [ABSTRACT FROM AUTHOR]

Abstract: In an effort to optimize visualization and editing of OWL ontologies we have developed GrOWL—a browser and visual editor for OWL that accurately visualizes the underlying DL semantics of OWL ontologies while avoiding the difficulties of the verbose OWL syntax. In this paper, we discuss GrOWL visualization model and the essential visualization techniques implemented in GrOWL. [Copyright &y& Elsevier]

Abstract: Reasoning strategies are a key component in many medical tasks, including decision making, clinical problem solving, and understanding of medical texts. Identification of reasoning strategies used by clinicians may prove critical to the optimal design of decision support systems. This paper presents a formal method of cognitive-semantic analysis for the identification and characterization of reasoning strategies deployed in medical tasks and demonstrates its use through specific examples. Although semantic analysis was originally developed in the investigation of knowledge structures, it can also be applied to identify the reasoning and decision processes used by physicians and medical trainees in clinical tasks. Assumptions underlying the methods, as well as illustrations of their use in diagnostic explanation tasks, are presented. We discuss semantic analysis in the context of the current interests in developing medical ontologies and argue that a frame-based propositional analytic methodology can provide a systematic way of addressing the construction of such ontologies. Although the application of propositional analysis methods has some limitations, we show how such limitations are being addressed and present some examples of information tools that have been developed to ease, and make more systematic, the process of analysis. [Copyright &y& Elsevier]

The retrieval and exchange of information between medical databases is often impeded by the semantic heterogeneity of concepts contained within the databases. Manual identification of equivalent database elements consumes time and resources, and may often be the rate-limiting technological step in integrating disparate data sources. By employing semantic networks as an intermediary representation of the native databases, automated mapping algorithms can identify equivalent concepts in disparate databases. The algorithms take advantage of the conceptual “context” embodied within a semantic network to produce candidate concept mappings. The performance of automated concept mapping was evaluated by creating semantic network representations for two test laboratory databases. The mapping algorithms identified all equivalent concepts that were present in the databases, and did not leave any equivalent concepts unmapped. The utilization of conceptual context to perform automated concept mapping facilitates the identification of equivalent database concepts and may help decrease the work and costs associated with retrieval and integration of information from disparate databases. [Copyright &y& Elsevier]

RDF-based P2P networks have a number of advantages compared to simpler P2P networks such as Napster, Gnutella or to approaches based on distributed indices on binary keys such as CAN and CHORD. RDF-based P2P networks allow complex and extendable descriptions of resources instead of fixed and limited ones, and they provide complex query facilities against these metadata instead of simple keyword-based searches.In this paper, we will discuss RDF-based P2P networks like Edutella as a specific example of a new type of P2P networks-schema-based P2P networks—and describe the use of super-peer based topologies for these networks. Super-peer based networks can provide better scalability than broadcast based networks, and provide support for inhomogeneous schema-based networks, with different metadata schemas and ontologies (crucial for the Semantic Web). Based on (dynamic) metadata routing indices, stated in RDF, the super-peer network supports sophisticated routing and distribution strategies, as well as preparing the ground for advanced mediation and clustering functionalities. [Copyright &y& Elsevier]

• Memory impairment is frequently present in multiple sclerosis (MS) patients. • Memory alteration can be detected by focusing on memory distortions. • MS patients showed reduced number of false recalls for semantically-related items. • Reduced false memories might be related to alteration of semantic memory. Memory impairment is one of the most frequently and early detected impairment in multiple sclerosis (MS) patients. Several authors have argued that when a failure occurs in the retrieval of lexical information, this might be due to a reduction of the lexical pool, related to semantic memory. Here we further investigated memory alteration in MS patients, by focusing on memory distortions (i.e., false memories) for semantically-related material. A group of 40 consecutive relapsing remitting MS (RRMS) patients and a matched control group of 40 healthy controls performed the Deese-Roediger-McDermott (DRM), a false memory task for lists of associated words. At recall, RRMS patients reported a reduced number of false recalls for semantically-related but non-presented items (i.e., critical false recalls) compared to HCs; at recognition, RRMS patients showed a reduced level of confidence for false recognitions of critical items. We found a reduced susceptibility to false memories in RRMS patients compared to HCs. The potential mechanisms underlying this effect are discussed in light of the alterations in the structure of semantic memory. [ABSTRACT FROM AUTHOR]

Background: Wikipedia is an important source of medical information for both patients and medical professionals. Given its wide reach, improving the quality, completeness, and accessibility of medical information on Wikipedia could have a positive impact on global health. Objective: We created a prototypical implementation of an automated system for keeping drug-drug interaction (DDI) information in Wikipedia up to date with current evidence about clinically significant drug interactions. Our work is based on Wikidata, a novel, graph-based database backend of Wikipedia currently in development. Methods: We set up an automated process for integrating data from the Office of the National Coordinator for Health Information Technology (ONC) high priority DDI list into Wikidata. We set up exemplary implementations demonstrating how the DDI data we introduced into Wikidata could be displayed in Wikipedia articles in diverse languages. Finally, we conducted a pilot analysis to explore if adding the ONC high priority data would substantially enhance the information currently available on Wikipedia. Results: We derived 1150 unique interactions from the ONC high priority list. Integration of the potential DDI data from Wikidata into Wikipedia articles proved to be straightforward and yielded useful results. We found that even though the majority of current English Wikipedia articles about pharmaceuticals contained sections detailing contraindications, only a small fraction of articles explicitly mentioned interaction partners from the ONC high priority list. For 91.30% (1050/1150) of the interaction pairs we tested, none of the 2 articles corresponding to the interacting substances explicitly mentioned the interaction partner. For 7.21% (83/1150) of the pairs, only 1 of the 2 associated Wikipedia articles mentioned the interaction partner; for only 1.48% (17/1150) of the pairs, both articles contained explicit mentions of the interaction partner. Conclusions: Our prototype demonstrated that automated updating of medical content in Wikipedia through Wikidata is a viable option, albeit further refinements and community-wide consensus building are required before integration into public Wikipedia is possible. A long-term endeavor to improve the medical information in Wikipedia through structured data representation and automated workflows might lead to a significant improvement of the quality of medical information in one of the world's most popular Web resources. [ABSTRACT FROM AUTHOR]