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This article proposes an approach to compare semantic networks using concept-centered sub-networks. A concept-centered sub-network is defined as an induced network whose vertex set consists of the given concept (ego) and all its adjacent concepts (alters) and whose link set consists of all the links between the ego and alters (including alter-alter...
Citations
... Second, we need an approach that allows us to estimate the extent of individual variations in lexical networks. Third, in educational applications simple enough methods for text analysis are needed because advanced methods of text analysis [8][9][10][11] require expertise and are thus not likely to be adopted. ...
A simple method to construct lexical networks (lexicons) of how students use scientific terms in written texts is introduced. The method is based on a recently introduced quantum semantics generalization of a word-pair co-occurrence. Quantum semantics allows entangled co-occurrence, thus allowing to model the effect of subjective bias on weighting the importance of word co-occurrence. Using such a generalized word-pair co-occurrence counting, we construct students’ lexicons of scientific (life-science) terms they use in their written responses to questions concerning food chains in life-science contexts. The method allows us to construct ensembles of lexicons that probabilistically simulate the variability of individual lexicons. The re-analyses of the written reports show that while sets of top-ranking terms contain nearly the same terms irrespective of details of the method used to count co-occurrences, the relative rankings of some key-terms may be different in quantum semantic analysis.
... For Muscolino et al. (2022), a semantic network (also called a knowledge graph) connected data and information to knowledge systematically. A semantic network could be undirected or directed, and edges/arcs, pondered or not, were part of the definition presented for Medeuov et al. (2021). ...
... The mathematical representation employed byAllen et al. (1990),Bomberger et al. (2007),Camargo et al. (2012), Lim (2019),Medeuov et al. (2021),Muscolino et al. (2022),Pereira et al. (2016), and Pflüger (2021) to define semantic networks was a graph with vertices and edges. ForAllen et al. (1990), the edges must be labeled, andCamargo et al. (2012) added the condition that the graph must be connected and acyclic. ...
Knowledge can be represented through semantic networks, which have the advantages of being simple, clear, and easy to visualize. The literature points to the existence of a diversity of “semantic network” definitions, which can be conceived of as structures based on graphs with edges or arcs. According to this assumption, vertices can be assigned meanings or be labeled, and they can represent concepts, objects, or individuals; edges can have direction or be labeled, and they represent relationships or associations. A semantic network model can be designed to capture only one or several aspects of a semantic structure. We performed a systematic review of the literature with the aim of identifying and classifying the family of “semantic network” definitions adopted by the authors of the studies reviewed. This study mapped the “semantic network” definitions present in the literature and analyzed aspects such as the mathematical representation used, vertices and edges representation, the attribution of meanings to each network component and the semantic network representation. We also analyzed the core of words linked to the family of the “semantic network” term in a semantic network based on their definitions.
... The co-word mapping methods capture syntactic structures only up to word adjacency, but they can be generalized to take into account more complicated syntactic structures composed of subject-verb-object triads and networks constituted by such triads [31]. Other methods going beyond coword analysis are looking in detail for example: concept diversity (cognitive content) through lexical diversity of text [14]; relation of key concepts and the architecture of the text structure [32,33]; searching semantic frames through finding communities of verbs and their arguments [34]; creating semantic networks by using concept-centered sub-networks [35]; and finding the larger-scale semantic structure of texts [36,37]. In all these cases, complex networks methods are used in construction of the semantic, lexical, or concept networks, and different network measures (closeness centrality, betweenness centrality, or some specially engineered measures for network topology) are utilized in analysis. ...
Complex networks are often used to analyze written text and reports by rendering texts in the form of a semantic network, forming a lexicon of words or key terms. Many existing methods to construct lexicons are based on counting word co-occurrences, having the advantage of simplicity and ease of applicability. Here, we use a quantum semantics approach to generalize such methods, allowing us to model the entanglement of terms and words. We show how quantum semantics can be applied to reveal disciplinary differences in the use of key terms by analyzing 12 scholarly texts that represent the different positions of various disciplinary schools (of conceptual change research) on the same topic (conceptual change). In addition, attention is paid to how closely the lexicons corresponding to different positions can be brought into agreement by suitable tuning of the entanglement factors. In comparing the lexicons, we invoke complex network-based analysis based on exponential matrix transformation and use information theoretic relative entropy (Jensen–Shannon divergence) as the operationalization of differences between lexicons. The results suggest that quantum semantics is a viable way to model the disciplinary differences of lexicons and how they can be tuned for a better agreement.
... Second, we need an approach that allows us to estimate the extent of individual variations in lexical networks. Third, in educational applications simple enough methods for text analysis are needed because advanced methods of text analysis [8][9][10][11] require expertise and are thus not likely to be adopted. ...
The lexical structure of language of science as it appears in teaching and teaching materials plays a crucial role in learning the language of science. We inspect here the lexical structure of two texts, written for didactic purposes and discussing the topic of wave-particle dualism as it is addressed in science education. The texts are analyzed as lexical networks of terms. The analysis is based on construction of stratified lexical networks, which allows us to analyze the lexical connections from the level of cotext (sentences) to context. Based on lexical networks, we construct lexicon profiles as they appear in two texts addressing the wave-particle dualism of electrons and photons. We demonstrate that the lexicon profiles of the two texts, although they discuss the same topic with similar didactic goals, nevertheless exhibit remarkable variation and differences. The consequences of such variation of lexicon profiles for practical teaching are discussed.
Nested structure is a structural feature that is conducive to system stability formed by co-evolution. In our opinion, it is just like what the biological species do in the mutualistic ecosystem that enterprises collaborate to apply for patents in the technical cooperation network, changing to form one dynamic equilibrium after another. In this paper, a nestedness-based analytical framework is built to reflect the topological stability of the technical cooperation network of Zhongguancun Science Park (Z-Park). We study why this technically mutualistic ecosystem can reach a stable equilibrium with time going by, as well as, we propose an index called Nestedness Disturbance Index (NDI) to study what the role park areas and technical fields play in the steady states.
