Semantic Network Analysis in Social Sciences introduces the fundamentals of semantic network analysis and its applications in the social sciences. Readers learn how to easily transform any given text into a visual network of words co-occurring together, a process that allows mapping the main themes appearing in the text and revealing its main narratives and biases. Semantic network analysis is particularly useful today with the increasing volumes of text-based information available. It is one of the developing, (...) cutting-edge methods to organize, identify patterns and structures, and understand the meanings of our information society. The first chapters in this book offer step-by-step guidelines for conducting semantic network analysis, including choosing and preparing the text, selecting desired words, constructing the networks, and interpreting their meanings. Free software tools and code are also presented. The rest of the book displays state-of-the-art studies from around the world that apply this method to explore news, political speeches, social media content, and even to organize interview transcripts and literature reviews. Aimed at scholars with no previous knowledge in the field, this book can be used as a main or a supplementary textbook for general courses on research methods or network analysis courses, as well as a starting point to conduct your own content analysis of large texts. (shrink)

The mobile game “Immortal Conquest,” created by NetEase Games, caused a dramatic user dissatisfaction event after an introduction of a sudden and uninvited “pay-to-win” update. As a result, many players filed grievances against NetEase in a court. The official game website issued three apologies, with mix results, to mitigate the crisis. The goal of the present study is to understand user feedback content from the perspective of Situational Crisis Communication Theory through semantic network analysis and sentiment analysis to explore (...) how an enterprise’s crisis communication strategy affects users’ attitudes. First, our results demonstrate that the diminishing crisis communication strategies do not change players’ negative attitudes. It was not a failure because it successfully alleviated the players’ legal complaints and refocused their attention on the game itself. Second, the rebuild strategy was effective because it significantly increased the percentage of positive emotions and regenerated expectations for the game. The litigation crisis was identified within gamer communications with respect to Chinese gaming companies for the first time. Nevertheless, this does not indicate an increase in overall legal awareness among the larger Chinese population. It may only reflect greater legal awareness among Chinese online gamers. Fourth, gamers emphasized that they and enterprises should be equally involved when communicating with each other. Finally, in-game paid items should be reasonably priced, otherwise, they will drive users to competitors. (shrink)

According to Mednick’s (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related (...) to each other by their association correlations - overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick’s (1962) theory and the feasibility of using network science paradigms to investigate high level cognition. (shrink)

Semantic Field Analysis in the Study of Parent-Child Relationships This article discusses the applicability of semantic field analysis to the study of development and change in important interpersonal relations on the example of parent-child relationships. The narrative material was compiled from responses of 348 teenagers and young adults aged 13-30 years. Participants wrote about their parents. On the basis of the context, semantic fields were generated for the high-incidence phrase "to love one's parents", which is the primary model of conceptualizing (...) the parent-child relationship in our culture. The results demonstrate the material complexity of the "love for parents" semantic field in the study group, and reveal the associative network of other semantic relations involving this concept. They also confirm the hypotheses on subtle developmental changes in the understanding of "love for parents" between early adolescence and adulthood. The study presents the application of methods based on linguistic analysis of language to the analysis of developmental changes in important personal relationships. (shrink)

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. (...) To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches. (shrink)

This article correlates fine-grained semantic variability and change with measures of occurrence frequency to investigate whether a word’s degree of semantic change is sensitive to how often it is used. We show that this sensitivity can be detected within a short time span (i.e., 20 years), basing our analysis on a large corpus of German allowing for a high temporal resolution (i.e., per month). We measure semantic variability and change with the help of local semantic networks, combining elements of deep (...) learning methodology and graph theory. Our micro-scale analysis complements previous macro-scale studies from the field of natural language processing, corroborating the finding that high token frequency has a negative effect on the degree of semantic change in a lexical item. We relate this relationship to the role of exemplars for establishing form–function pairings between words and their habitual usage contexts. (shrink)

Upon what philosophical foundation are semantic network graphs based? Does this foundation allow for the legitimization of other semantic networks and ontological diversity? How can we design our computational and informational systems to accommodate this ontological diversity and the variety of semantic networks? Are semantic networks segmentations of larger semantic landscapes? This paper explores semantic networks from a Heideggerian existentialist and phenomenological perspective. The analysis presented uses cultural schema theory to bridge the syntactic and lexical elements to the semantic (...) and conceptual dimensions of semantic network graphs and offers reasons why the viability of such graphs as they are currently constructed are insufficient for creating semantic interoperability for our informationtechnologies. Reconceptualizing semantic networks as cultural landscapes offers us. (shrink)

The associative theory of creativity states that creativity is associated with differences in the structure of semantic memory, whereas the executive theory of creativity emphasises the role of top-down control for creative thought. For a powerful test of these accounts, individual semantic memory structure was modelled with a novel method based on semantic relatedness judgements and different criteria for network filtering were compared. The executive account was supported by a correlation between creative ability and broad retrieval ability. The associative (...) account was independently supported, when network filtering was based on a relatedness threshold, but not when it was based on a fixed edge number or on the analysis of weighted networks. In the former case, creative ability was associated with shorter average path lengths and higher clustering of the network, suggesting that the semantic networks of creative people show higher small-worldness. (shrink)

The rise of Internet-mediated communication poses possibilities and challenges for organisation studies, also in the area of corporate social responsibility and business and society interactions. Although social media are attracting more and more attention in this domain, websites also remain an important channel for CSR debate. In this paper, we present an explorative study of activist groups’ online presence via their websites and propose a combination of methods to study both the structural positioning of websites and the meanings in these (...) websites. We focus on the websites of SOMO, the Centre for Research on Multinational Corporations, and of one of its campaigns, makeITfair, concerned with labour conditions in the IT industry worldwide. This allows us to show how this combination of methods can further our understanding of the way activist networks’ online presence can provide insights into the tactics these networks apply to achieve institutional change on CSR issues. Meanwhile, we identify some notable differences between styles and word use in the two organisations’ websites. We conclude with a set of suggestions for future research. (shrink)

Among business ethicists, Adam Smith is widely viewed as the defender of an amoral if not anti-moral economics in which individuals’ pursuit of their private self-interest is converted by an ‘invisible hand’ into shared economic prosperity. This is often justified by reference to a select few quotations from The Wealth of Nations. We use new empirical methods to investigate what Smith actually had to say, firstly about the relationship between free market institutions and individuals’ moral virtues, and secondly about the (...) further relationship between virtues and societal flourishing. We show with more quantitative precision than traditional scholarship that the invisible hand reading dramatically misrepresents both the nuance and the sum of Smith’s analysis. Smith paid a great deal of attention to a flourishing society’s dependence on virtues, including the non-self-regarding virtues of justice and benevolence, and he worried also about their fragility in the face of the changed incentives and social conditions of commercial society. (shrink)

Exploratory analysis is an area of increasing interest in the computational linguistics arena. Pragmatically speaking, exploratory analysis may be paraphrased as natural language processing by means of analyzing large corpora of text. Concerning the analysis, appropriate means are statistics, on the one hand, and artificial neural networks, on the other hand. As a challenging application area for exploratory analysis of text corpora we may certainly identify text databases, be it information retrieval or information filtering systems. With this paper we present (...) recent findings of exploratory analysis based on both statistical and neural models applied to legal text corpora. Concerning the artificial neural networks, we rely on a model adhering to the unsupervised learning paradigm. This choice appears naturally when taking into account the specific properties of large text corpora where one is faced with the fact that input-output-mappings as required by supervised learning models cannot be provided beforehand to a satisfying extent. This is due to the fact of the highly changing contents of text archives. In a nutshell, artificial neural networks count for their highly robust behavior regarding the parameters for model optimization. In particular, we found statistical classification techniques much more susceptible to minor parameter variations than unsupervised artificial neural networks. In this paper we describe two different lines of research in exploratory analysis. First, we use the classification methods for concept analysis. The general goal is to uncover different meanings of one and the same natural language concept. A task that, obviously, is of specific importance during the creation of thesauri. As a convenient environment to present the results we selected the legal term of neutrality, which is a perfect representative of a concept having a number of highly divergent meanings. Second, we describe the classification methods in the setting of document classification. The ultimate goal in such an application is to uncover semantic similarities of various text documents in order to increase the efficiency of an information retrieval system. In this sense, document classification has its fixed position in information retrieval research from the very beginning. Nowadays renewed massive interest in document classification may be witnessed due to the appearance of large-scale digital libraries. (shrink)

The article addresses the problem of how semantic information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for semantic information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising (...) the target semantic information t by default, is correctly satisfied by the information flow of correct answers provided by an informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an informational analysis of knowledge, according to which knowledge is accounted semantic information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained. (shrink)

Children tend to produce words earlier when they are connected to a variety of other words along the phonological and semantic dimensions. Though these semantic and phonological connectivity effects have been extensively documented, little is known about their underlying developmental mechanism. One possibility is that learning is driven by lexical network growth where highly connected words in the child's early lexicon enable learning of similar words. Another possibility is that learning is driven by highly connected words in the external (...) learning environment, instead of highly connected words in the early internal lexicon. The present study tests both scenarios systematically in both the phonological and semantic domains across 10 languages. We show that phonological and semantic connectivity in the learning environment drives growth in both production‐ and comprehension‐based vocabularies, even controlling for word frequency and length. This pattern of findings suggests a word learning process where children harness their statistical learning abilities to detect and learn highly connected words in the learning environment. (shrink)

As one of the key subjects of multi-center governance of environmental concerns, public perception is crucial in forming and implementing environmental policy. Based on data science research theory and the original theory of public perception, this study proposes a research framework to analyze environmental policy through network text analysis. The primary contents are bidirectional encoder representation from transformers-convolution neural network sentiment tendency analysis, word frequency characteristic analysis, and semantic network analysis. The realism of the suggested framework is (...) demonstrated by using the waste classification policy as an example. The findings indicate a substantial relationship between perceived subject participation and policy pilot areas and that perceived subject participation is repeating. On this premise, specific recommendations are made to encourage policy implementation. (shrink)

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and (...) errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia. (shrink)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel approach to generating (...) embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

Ritual studies, a pivotal domain in the scholarly tradition of ancient China, occupy a crucial role in the exploration of humanistic and theological disciplines. Utilizing bibliometric analysis, corpus linguistics, and scientometric mapping, this paper examines a comprehensive dataset of 15,221 ritual studies articles spanning from 1916 to 2022, sourced from the CNKI database. The focus of these studies has traditionally been on ideological exploration, textual scrutiny, and archaeological verification, with a notable shift in the post-reform era from Confucian critiques to (...) authentication of ancient rituals, propelled by significant archaeological discoveries. Entering the 21st century, the field has broadened to intersect with intellectual history, philosophy, literature, and theological studies, reflecting an expanded methodological repertoire that includes ideological articulation, historical comparative analysis, and interdisciplinary exploration. This research introduces an innovative methodological approach by employing word embedding and semantic networks to delineate and trace underrepresented yet crucial research trajectories within the discipline. Through this semantic analysis, key scholars such as Kuang Heng, Cao Bao, Wang Chong, He Yang, Chen Li, and Chu Yinliang, along with cornerstone texts like the "Collected Commentaries on the Book of Rites" and "Illustrations of the Rites Classic," are identified as significant yet previously underexplored nodes within the academic discourse. This study not only sheds light on the evolution and current state of ritual studies in China but also highlights the profound philosophical and religious implications of these rituals in understanding the divine, the moral, and the human condition, thus contributing to a deeper comprehension of the spiritual and ethical dimensions that these practices embody. (shrink)

The description of named entities in terminological knowledge bases has never been addressed in any depth in terminology. Firm preconceptions, rooted in philosophy, about the only referential function of proper names have presumably led to disparage their inclusion in terminology resources, despite the relevance of named entities having been highlighted by prominent figures in the discipline of terminology. Scholars from different branches of linguistics depart from the conservative stance on proper names and have foregrounded the need for a novel approach, (...) more linguistic than philosophical, to describing proper names. Therefore, this paper proposed a linguistic and terminological approach to the study of named entities when used in scientific discourse, with the purpose of representing them in EcoLexicon, an environmental knowledge base designed according to the premises of Frame-based Terminology. We focused more specifically on named rivers mentioned in a coastal engineering corpus. Inclusion of named entities in terminological knowledge bases requires analyzing the context that surrounds them in specialized texts because these contexts convey specialized knowledge about named entities. For the semantic representation of context, this paper thus analyzed the local syntactic and semantic contexts that surrounded potamonyms in coastal engineering texts and described the semantic annotation of the predicate-argument structure of sentences where a potamonym was mentioned. The semantic variables annotated were the following: semantic category of the arguments; semantic role of the arguments; semantic relation between the arguments; and lexical domain of the verbs. This method yielded valuable insight into the different semantic roles that named rivers played, the entities and processes that participated in the events educed by potamonyms through verbs, and how they all interacted. Furthermore, since arguments are specialized terms and verbs are relational constructs, the analysis of argument structure led to the construction of semantic networks that depicted specialized knowledge about named rivers. These conceptual networks were then used to craft the thematic description of potamonyms. Accordingly, the semantic network and the thematic description not only constituted the representation of a potamonym in EcoLexicon, but also allowed the geographic contextualization of specialized concepts in the terminological resource. (shrink)

Impaired performance in verbal fluency tasks is a frequent observation in Parkinson’s disease. As to the nature of the underlying cognitive deficit, it is commonly attributed to a frontal-type dysexecutive syndrome due to nigrostriatal dopamine depletion. Whereas dopaminergic medication typically improves VF performance in PD, e.g., by ameliorating impaired lexical switching, its effect on semantic network activation is unclear. Data from priming studies suggest that dopamine causes a faster decay of semantic activation spread. The aim of the current study (...) was to examine the impact of dopaminergic medication on the dynamic change of word frequency during VF performance as a measure of semantic spreading activation. To this end, we performed a median split analysis of word frequency during phonemic and semantic VF task performance in a PD group tested while receiving dopaminergic medication as well as after drug withdrawal, and in a sample of age-matched healthy volunteers. Dopaminergic medication in the PD group significantly affected phonemic VF with improved word production as well as increased error-rates. The expected decrease of word frequency during VF task performance was significantly smaller in the PD group ON medication than in healthy volunteers across semantic and phonemic VF. No significant group-difference emerged between controls and the PD group in the OFF condition. The comparison between both treatment conditions within the PD group did not reach statistical significance. The observed pattern of results indicates a faster decay of semantic network activation during lexical access in PD patients on dopaminergic medication. In view of improved word generation, this finding is consistent with a concept of more focused neural activity by an increased signal-to-noise ratio due to dopaminergic neuromodulation. However, the effect of dopaminergic stimulation on VF output suggests a trade-off between these beneficial effects and increased error-rates. (shrink)

As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern—especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and (...) implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens. (shrink)

Along with urban development globally, the NIMBY crisis has been a complex social problem, which requires urgent remedial action. The inevitable management of Municipal Solid Waste has been one of the toughest risk management tasks in the worldwide modernization process. At present, certain fuzzy and unstructured results and methods have been formed for MSW-NIMBY crisis response, mainly focusing on the sociology and politics which scatter in complex and sensitive reports and news. Aiming at enhancing the effectiveness of data mining from (...) specific sparse text of MSW-NIMBY crisis, an improved knowledge extraction method is developed. Through rule-based text mining and complex network analysis, the Entity Relationship network of MSW-NIMBY crisis is reconstructed. Meanwhile, a novel transitivity for relationship between entities in semantic analysis is proposed to improve the feasibility and accuracy of information extraction. Characteristics and regularity of MSW-NIMBY crisis evolution and experience of crisis governance could be identified effectively. Results show that knowledge integration and ER transitivity can enhance knowledge recognition and major other factors, which could help formulate the governance strategies of NIMBY crisis from academic texts. (shrink)

Some of the fundamental activities of the software development process are related to the discipline of Requirements Engineering. Their objectives are to discover, analyze, document, and verify the system’s requirements. The requirements are the conditions or capabilities that software needs to have or fulfill to meet its users’ needs, and problems in its identification can mean the failure of a software project. This study is part of the research that is being developed to propose a model based on Know­ledge Organization (...) Systems to be used in the Requirements engineering process. This article aims to present the results of an analysis on a set of Know­ledge Organization Systems to identify whether they are likely to be applied in the Requirements engineering process and identify at which stage of this process each one of them can be implemented. The Know­ledge Organization Systems analyzed were the authority files, gazetteers, glossaries, subject headings, classification systems, thesauri, semantic networks, and ontologies. Based on the results obtained, it was possible to conclude that the Know­ledge Organization Systems analyzed can be used in the Requirements engineering process and, consequently, contribute to increasing the software requirements’ quality. (shrink)

A defining characteristic of Alzheimer’s disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of semantic fluency data, we derive semantic network representations of patients with Alzheimer’s disease and of healthy controls. We contrast our network-based approach with analyzing fluency (...) data with the standard method of counting the total number of items and perseverations in fluency data. We find that the networks of Alzheimer’s patients are more connected and that those connections are more randomly distributed than the connections in networks of healthy individuals. These results suggest that the semantic memory impairment of Alzheimer’s patients can be modeled through the inclusion of spurious associations between unrelated concepts in the semantic store. We also find that information from our network analysis of fluency data improves prediction of patient diagnosis compared to traditional measures of the semantic fluency task. (shrink)

This book attempts to marry truth-conditional semantics with cognitive linguistics in the church of computational neuroscience. To this end, it examines the truth-conditional meanings of coordinators, quantifiers, and collective predicates as neurophysiological phenomena that are amenable to a neurocomputational analysis. Drawing inspiration from work on visual processing, and especially the simple/complex cell distinction in early vision (V1), we claim that a similar two-layer architecture is sufficient to learn the truth-conditional meanings of the logical coordinators and logical quantifiers. As a (...) prerequisite, much discussion is given over to what a neurologically plausible representation of the meanings of these items would look like. We eventually settle on a representation in terms of correlation, so that, for instance, the semantic input to the universal operators (e.g. and, all)is represented as maximally correlated, while the semantic input to the universal negative operators (e.g. nor, no)is represented as maximally anticorrelated. On the basis this representation, the hypothesis can be offered that the function of the logical operators is to extract an invariant feature from natural situations, that of degree of correlation between parts of the situation. This result sets up an elegant formal analogy to recent models of visual processing, which argue that the function of early vision is to reduce the redundancy inherent in natural images. Computational simulations are designed in which the logical operators are learned by associating their phonological form with some degree of correlation in the inputs, so that the overall function of the system is as a simple kind of pattern recognition. Several learning rules are assayed, especially those of the Hebbian sort, which are the ones with the most neurological support. Learning vector quantization (LVQ) is shown to be a perspicuous and efficient means of learning the patterns that are of interest. We draw a formal parallelism between the initial, competitive layer of LVQ and the simple cell layer in V1, and between the final, linear layer of LVQ and the complex cell layer in V1, in that the initial layers are both selective, while the final layers both generalize. It is also shown how the representations argued for can be used to draw the traditionally-recognized inferences arising from coordination and quantification, and why the inference of subalternacy breaks down for collective predicates. Finally, the analogies between early vision and the logical operators allow us to advance the claim of cognitive linguistics that language is not processed by proprietary algorithms, but rather by algorithms that are general to the entire brain. Thus in the debate between objectivist and experiential metaphysics, this book falls squarely into the camp of the latter. Yet it does so by means of a rigorous formal, mathematical, and neurological exposition – in contradiction of the experiential claim that formal analysis has no place in the understanding of cognition. To make our own counter-claim as explicit as possible, we present a sketch of the LVQ structure in terms of mereotopology, in which the initial layer of the network performs topological operations, while the final layer performs mereological operations. The book is meant to be self-contained, in the sense that it does not assume any prior knowledge of any of the many areas that are touched upon. It therefore contains mini-summaries of biological visual processing, especially the retinocortical and ventral /what?/ parvocellular pathways computational models of neural signaling, and in particular the reduction of the Hodgkin-Huxley equations to the connectionist and integrate-and-fire neurons Hebbian learning rules and the elaboration of learning vector quantization the linguistic pathway in the left hemisphere memory and the hippocampus truth-conditional vs. image-schematic semantics objectivist vs. experiential metaphysics and mereotopology. All of the simulations are implemented in MATLAB, and the code is available from the book’s website. • The discovery of several algorithmic similarities between visison and semantics. • The support of all of this by means of simulations, and the packaging of all of this in a coherent theoretical framework. (shrink)

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9‐year‐old children. The neural network was trained and validated in the child (...) semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed‐effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed. (shrink)

This article explores the domain of legal analysis and its methodologies, emphasising the significance of generalisation in legal systems. It discusses the process of generalisation in relation to legal concepts and the development of ideal concepts that form the foundation of law. The article examines the role of logical induction and its similarities with semantic generalisation, highlighting their importance in legal decision-making. It also critiques the formal-deductive approach in legal practice and advocates for more adaptable models, incorporating fuzzy logic, non-monotonic (...) defeasible reasoning, and artificial intelligence. The potential application of neural networks, specifically deep learning algorithms, in legal theory is also discussed. The article discusses how neural networks encode legal knowledge in their synaptic connections, while the syllogistic model condenses legal information into axioms. The article also highlights how neural networks assimilate novel experiences and exhibit evolutionary progression, unlike the deductive model of law. Additionally, the article examines the historical and theoretical foundations of jurisprudence that align with the basic principles of neural networks. It delves into the statistical analysis of legal phenomena and theories that view legal development as an evolutionary process. The article then explores Friedrich Hayek’s theory of law as an autonomous self-organising system and its compatibility with neural network models. It concludes by discussing the implications of Hayek’s theory on the role of a lawyer and the precision of neural networks. (shrink)

Standard psychological scaling methods have been widely used as knowledge elicitation tools to uncover structural characteristics of a given domain. However, these methods traditionally rely on relatedness ratings from human experts, which is often time-consuming and tedious. We describe an integrated approach to knowledge elicitation and representation using Latent Semantic Analysis and Pathfinder Network Scaling techniques. The semantic structure of a subject domain can be automatically characterised from a collection of published documents in the domain. The method is illustrated (...) with an example of organising a digital library in accordance to latent semantic structures. (shrink)

Dispositional mindfulness and emotion regulation are two psychological constructs closely interrelated, and both appear to improve with the long-term practice of mindfulness meditation. These constructs appear to be related to subcortical, prefrontal, and posterior brain areas involved in emotional processing, cognitive control, self-awareness, and mind wandering. However, no studies have yet discerned the neural basis of dispositional mindfulness that are minimally associated with emotion regulation. In the present study, we use a novel brain structural network analysis approach to study (...) the relationship between structural networks and dispositional mindfulness, measured with two different and widely used instruments [Mindfulness Attention Awareness Scale and Five Facet Mindfulness Questionnaire ], taking into account the effect of emotion regulation difficulties. We observed a number of different brain regions associated with the different scales and dimensions. The total score of FFMQ and MAAS overlap with the bilateral parahippocampal and fusiform gyri. Additionally, MAAS scores were related to the bilateral hippocampus and the FFMQ total score to the right insula and bilateral amygdala. These results indicate that, depending on the instrument used, the characteristics measured could differ and could also involve different brain systems. However, it seems that brain areas related to emotional reactivity and semantic processing are generally related to Dispositional or trait mindfulness, regardless of the instrument used. (shrink)

Collaborative forms of work such as extended networks, expert groups, and consortia increasingly structure biomedical activities. They are particularly prominent in the cancer field, where procedures such as multicenter clinical trials have been instrumental in establishing the specialty of oncology, and subfields such as cancer genetics, where bioclinical activities—for example, testing for breast and ovarian cancer genes and follow-up interventions—are predicated on the articulation of a number of tasks performed by new clinical collectives. In this article, we examine the founding (...) and development of a French bioclinical collective—the Groupe Génétique et Cancer —that coordinates and structures the activities of most French actors in cancer genetics and operates simultaneously in the clinical, research, and regulatory domains. To examine the group’s structure and dynamics, the article combines information gathered through traditional fieldwork methods with information elicited from a coauthorship and semantic-network analysis of the publications of GGC members from 1969 to 2001. (shrink)

Conceptual knowledge is dynamic, fluid, and flexible, changing as a function of contextual factors at multiple scales. The Covid-19 pandemic can be considered a large-scale, global context that has fundamentally altered most people's experiences with the world. It has also introduced a new concept, COVID (or COVID-19), into our collective knowledgebase. What are the implications of this introduction for how existing conceptual knowledge is structured? Our collective emotional and social experiences with the world have been profoundly impacted by the Covid-19 (...) pandemic, and experience-based perspectives on concept representation suggest that emotional and social experiences are critical components of conceptual knowledge. Such changes in collective experience should, then, have downstream consequences on knowledge of emotion- and social-related concepts. Using a naturally occurring dataset derived from the social media platform Twitter, we show that semantic spaces for concepts related to our emotional experiences with Covid-19 (i.e., emotional concepts like FEAR)—but not for unrelated concepts (i.e., animals like CAT)—show quantifiable shifts as a function of the emergence of COVID-19 as a concept and its associated emotional and social experiences, shifts which persist 6 months after the onset of the pandemic. The findings support a dynamic view of conceptual knowledge wherein shared experiences affect conceptual structure. (shrink)

Semantic networks were developed in cognitive science and artificial intelligence studies as graphical knowledge representation and inference tools emulating human thought processes. Formal analysis of the representation and inference capabilities of the networks modeled them as subsets of standard first-order logic (FOL), restricted in the operations allowed in order to ensure the tractability that seemed to characterize human reasoning capabilities. The graphical network representations were modeled as providing a visual language for the logic. Sub-sets of FOL targeted on knowledge (...) representation came to be called description logics, and research on these logics has focused on issues of tractability of subsets with differing representation capabilities, and on the implementation of practical inference systems achieving the best possible performance. Semantic network research has kept pace with these developments, providing visual languages for knowledge entry, editing, and presenting the results of inference, that translate unambiguously to the underlying description logics. This paper discusses the design issues for such semantic network formalisms, and illustrates them through detailed examples of significant generic knowledge structures analyzed in the literature, including determinables, contrast sets, genus/differentiae, taxonomies, faceted taxonomies, cluster concepts, family resemblances, graded concepts, frames, definitions, rules, rules with exceptions, essence and state assertions, opposites and contraries, relevance, and so on. Such examples provide important test material for any visual language formalism for logic. (shrink)

The article discusses the possibilities of the formal analysis of the fine-art painting composition on the basis of the classical definitions of beauty and computational aesthetics’ approaches of the second half of the 20th century he authors define the problem and consider solutions for the formalization of aesthetic perception in the context of aesthetic text, i.e., as part of the fine arts composition – a formal sequence of signs simply ordered in accordance with the syntactic rules’ system. The methodology of (...) the research is defined by the general semiotics, distinguishing semantics, syntax, and pragmatics of a sign, by the aesthetic analysis’ methods, ranging according to the author’s message aesthetics, receptive aesthetics, and text aesthetics, as well as by the computational analysis methods connected with neural network means of defining the images’ symmetry. The article reveals preconditions for the emergence and also the current state of computational aesthetics as an interdisciplinary branch of knowledge. Analyzing the problem from the perspective of philosophy, aesthetics, semiotics, and technology, the authors draw attention to the need to improve the computational aesthetics methods. Firstly, the existing methods do not always enable to describe the fine-art object adequately. Secondly, there exists the so-called reduction of aesthetic assertion transforming it into the assertion concerning the object’s external characteristics. As a result, the authors assume that the increasing complexity of the current mathematical models and the experts’ subjective assessment support will allow to reach a compromise solution that enables the development of computational aesthetics as a branch of knowledge. Enhancement and development of the mathematical models, taking into account the rules of semiotics and subjectivism of the human perception, is the relevant objective of computational analysis of the aesthetic fine-arts text. The results of the present research supports the classic statement regarding the underivability of semantic and pragmatic propositions from syntax. The research concludes that relevant objectives are to find a correlation between, one the one hand, the axes and points of symmetry, deriving from the neural simulation, and, on the other hand, aesthetic effect, emerging from the perception of fine-art paintings. (shrink)

This is a contribution to construction of a research roadmap for future cognitive systems, including intelligent robots, in the context of the euCognition network, and UKCRC Grand Challenge 5: Architecture of Brain and Mind. -/- A meeting on the euCognition roadmap project was held at Munich Airport on 11th Jan 2007. This document was in part a response to discussions at that meeting. An explanation of why specifying requirements is a hard problem, and why it needs to be done, (...) along with some suggestions for making progress, can be found in this presentation: http://www.cs.bham.ac.uk/research/projects/cosy/papers/#pr0701 "What's a Research Roadmap For? Why do we need one? How can we produce one?" Working on that presentation made me realise that certain deceptively familiar words and phrases frequently used in this context (e.g. "robust". "flexible", "autonomous") appear not to need explanation because everyone understands them, whereas in fact they have obscure semantics that needs to be elucidated. Only then can we understand what the implications are for research targets. In particular, they need explanation and analysis if they are to be used to specify requirements and research goals, especially for publicly funded projects. -/- First draft analyses are presented here. In the long term I would like to expand and clarify those analyses, and to provide many different examples to illustrate the points made. This will probably have to be a collaborative research activity. (shrink)

The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and (...) the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory. (shrink)

We are frequently exposed to hand written digits 0-9 in today’s modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain-computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of fMRI (functional Magnetic Resonance (...) imaging) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25-30. Results in different brain lobes (frontal, occipital, parietal and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor and language processing), but the most relevant one to the task was language processing network according to the voxel selection. (shrink)

Scientific networks are often investigated by means of citation analyses. Yet, interpretation of such networks in terms of semantic (and often disciplinary) content heavily depends on supplementary knowledge, notably about author research specialties. Similar situations arise more generally in many types of social networks whose semantic interpretation relies on supplementary information. Here, author community net-works are inferred from a topic model which provides direct insights into the semantic specificity of the identified “hidden communities of interest” (HCoI). Using a philosophy of (...) science corpus of full-text articles (N=16,917), we investigate its underlying communities by measuring topic profile correlations be-tween authors. A diachronic perspective is built by modeling the research networks over different time-periods and mapping genealogical relationships be-tween communities. The results show a marked in-crease in philosophy of science communities over time and trace the progressive appearance of the specialization areas that structure the field today. (shrink)

The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I (...) spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals. (shrink)

Two approaches to the study of diagnostic thinking are compared, one mainly propositional, namely that of Patel and Groen (1986), the other mainly semantic, that of Lemieux and Bordage (1986). Patel and Groen analyzed the linear dimension of cardiologists' discourses while solving a case of acute bacterial endocarditis, that is, the before and after propositional rules. A secondary analysis of two of their pothophysiological protocols is done using structural semantic techniques from Lemieux and Bordage where the vertical dimension of the (...) discourses is analyzed, that is, the levels of meaning or semantic axes. Contrary to Patel and Groen's position, making an accurate diagnosis is not explained in terms of pure forward reasoning through networks of causal rules, but by means of networks of semantic qualities abstracted from the symptoms and signs. The semantic qualities operate in the clinician's mind in terms of binary oppositions (e.g., sudden-gradual, unilateral-bilateral) whereby each pair of properties constitutes a semantic axis (e.g., sudden-gradual). The successful diagnosticians are those who use the most diversified and pertinent set of semantic axes and, therefore, have a deeper representation of the problem. (shrink)

John Mair (1467–1550) was an influential post-medieval scholar. This paper focuses on his Tractatus insolubilium, in which he proposed semantic analysis of self-referential phenomena, in particular on his solution to alethic and correspondence paradoxes and his treatment of their general semantic aspects as well as particular applications. His solution to paradoxes is based on the so-called “network evaluation”, i.e. on a semantics which defines the concepts of truth and correspondence with reality in contextual terms. Consequently, the relation between (...) semantic valuation, synonymy and contradiction must be redefined. (shrink)

Smart communities have recently gained much attention. Researchers have been trying to tackle a number of challenges faced by smart communities. Interoperability is one key challenge that occurs due to different systems using different knowledge representations. To solve interoperability problems, ontologies are seen as a promising solution as they provide a commonly agreed vocabulary for representing data that are understandable by stakeholders of smart communities. Smart communities make use of Internet of Things (IoT) and ubiquitous networks to support communication among (...) objects and devices in such environments. Smart campuses are examples of smart communities. Recently, many articles related to ontologies focusing on smart communities and smart campuses in IoT environments, have been published. This paper presents a Systematic Literature Review that has been conducted using Google Scholar. 18 ontologies for smart communities/smart campuses have been identified and analyzed out of 341 articles from year 2010 to 2019. The review classifies the ontologies in terms of domain, ontologies being reused, availability online, limitations, language adopted and coverage. It additionally discusses on the standards, the level of expressiveness, the ontology development approaches and methodologies adopted by the identified ontologies. Our analysis shows that the identified ontologies have been developed based on different ontological commitments. None of them have come up with a core semantic model that models different collaborating domains in a smart campus such as smart learning, smart management, smart governance, smart room, smart health, smart library and smart parking among others and that enhances cross-domain interoperability in a such an environment. Further details on our findings are presented and discussed in the paper. (shrink)

Mining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. (...) After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately. (shrink)

In 2023, artificial intelligence was announced as a “game changer”—marking a rapid revolution in thinking technologies. A global debate began to emerge. By conducting a discourse analysis of 2023 US congressional testimonies and AI manifestos, we aim to map the emergence of debates over the start-up of a global governance controversy. Qualitative topical identification and semantic network analysis are deployed to identify the primary stakeholders and their contesting arguments. The resulting polylog exhibits sharp divisions among multiple, distinct pro-tech and (...) pro-rights groups. Pro-tech stakeholders emphasize innovation and economic benefits, while pro-rights groups prioritize human rights and safety. Our linguistic and semantic network analyses provide micro-level insights into the polylogue entanglements of ethics and governance. The analysis of US AI advocacy discourse furnishes a baseline of contestation from which the ongoing development of the complex arguments of AI policies can be identified and evaluated. (shrink)

A distributed neural network model called SPEC for processing sentences with recursive relative clauses is described. The model is based on separating the tasks of segmenting the input word sequence into clauses, forming the case‐role representations, and keeping track of the recursive embeddings into different modules. The system needs to be trained only with the basic sentence constructs, and it generalizes not only to new instances of familiar relative clause structures but to novel structures as well. SPEC exhibits plausible (...) memory degradation as the depth of the center embeddings increases, its memory is primed by earlier constituents, and its performance is aided by semantic constraints between the constituents. The ability to process structure is largely due to a central executive network that monitors and controls the execution of the entire system. This way, in contrast to earlier subsymbolic systems, parsing is modeled as a controlled high‐level process rather than one based on automatic reflex responses. (shrink)

Beginning with the Untimely Meditations (1873) and continuing until his final writings of 1888-9, Nietzsche refers to humility (Demuth or a cognate) in fifty-two passages and to modesty (Bescheidenheit or a cognate) in one hundred and four passages, yet there are only four passages that refer to both terms. Moreover, perhaps surprisingly, he often speaks positively of modesty, especially in epistemic contexts. These curious facts might be expected to lead scholars to explore what Nietzsche thinks of humility and modesty, but (...) to date there have been no systematic analyses of Nietzsche’s reflections on these dispositions. In this chapter, I fill that gap in the literature using semantic network analysis and systematically-guided close-reading. In so doing, I show that Nietzsche sharply distinguished between humility and modesty, considering the former a vice (for certain types of people in certain contexts) and the latter a virtue (again, for certain types of people in certain contexts). (shrink)

If connectionism is to be an adequate theory of mind, we must have a theory of representation for neural networks that allows for individual differences in weighting and architecture while preserving sameness, or at least similarity, of content. In this paper we propose a procedure for measuring sameness of content of neural representations. We argue that the correct way to compare neural representations is through analysis of the distances between neural activations, and we present a method for doing so. We (...) then use the technique to demonstrate empirically that different artificial neural networks trained by backpropagation on the same categorization task, even with different representational encodings of the input patterns and different numbers of hidden units, reach states in which representations at the hidden units are similar. We discuss how this work provides a rebuttal to Fodor and Lepore's critique of Paul Churchland's state space semantics. (shrink)

The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting (...) a computational experiment using Binder et al.'s (2016) featural conceptual representations based on neurobiologically motivated attributes. In an experiment, these conceptual vectors are predicted from text‐based word vectors using a neural network and linear transformation, and prediction performance is compared among various types of information. The analysis demonstrates that abstract information is generally predicted more accurately by word vectors than perceptual and spatiotemporal information, and specifically, the prediction accuracy of cognitive and social information is higher. Emotional information is also found to be successfully predicted for abstract words. These results indicate that language can be a major source of knowledge about abstract attributes, and they support the recent view that emphasizes the importance of language for abstract concepts. Furthermore, we show that word vectors can capture some types of perceptual and spatiotemporal information about concrete concepts and some relevant word categories. This suggests that language statistics can encode more perceptual knowledge than often expected. (shrink)

This study examined the relation between professional affiliation and the framing of expert congressional testimony about nicotine's addictiveness. Experts were chosen from three different types of sponsoring organizations: the tobacco industry, government, and independent research organizations, both pro- and anti-tobacco. The study sought to identify common technical biases and policy concerns that could define an overall “expert” attitude, as well as differences where the experts’ framing of nicotine addiction would reveal attempts to favor their own institutions. Semantic network analysis (...) was applied to each group's discourse, thereby clustering associated words that represented major themes in each type of expert group. Clusters revealed a common preoccupation with narrowly defined, lab-based evidence, but more locally, each group framed the issues to support its sponsor's strategy. (shrink)