Swahili conditional constructions in embodied Frames of Reference: Modeling semantics, pragmatics, and context-sensitivity in UML mental spaces

Studies of several languages, including Swahili [swa], suggest that realis (actual, realizable) and irrealis (unlikely, counterfactual) meanings vary along a scale (e.g., 0.0–1.0). T-values (True, False) and P-values (probability) account for this pattern. However, logic cannot describe or explain (a) epistemic stances toward beliefs, (b) deontic and dynamic stances toward states-of-being and actions, and (c) context-sensitivity in conditional interpretations. (a)–(b) are deictic properties (positions, distance) of ‘embodied’ Frames of Reference (FoRs)—space-time loci in which agents perceive and from which they contextually act (Rohrer 2007a, b). I argue that the embodied FoR describes and explains (a)–(c) better than T-values and P-values alone. In this cognitive-functional-descriptive study, I represent these embodied FoRs using Unified Modeling LanguageTM (UML) mental spaces in analyzing Swahili conditional constructions to show how necessary, sufficient, and contributing conditions obtain on the embodied FoR networks level.Swahili, conditional constructions, UML, mental spaces, Frames of Reference, epistemic stance, deontic stance, dynamic stance, context-sensitivity, non-monotonic logi

Swahili conditional constructions in embodied Frames of Reference: Modeling semantics, pragmatics, and context-sensitivity in UML mental spaces

Studies of several languages, including Swahili [swa], suggest that realis (actual, realizable) and irrealis (unlikely, counterfactual) meanings vary along a scale (e.g., 0.0–1.0). T-values (True, False) and P-values (probability) account for this pattern. However, logic cannot describe or explain (a) epistemic stances toward beliefs, (b) deontic and dynamic stances toward states-of-being and actions, and (c) context-sensitivity in conditional interpretations. (a)–(b) are deictic properties (positions, distance) of ‘embodied’ Frames of Reference (FoRs)—space-time loci in which agents perceive and from which they contextually act (Rohrer 2007a, b). I argue that the embodied FoR describes and explains (a)–(c) better than T-values and P-values alone. In this cognitive-functional-descriptive study, I represent these embodied FoRs using Unified Modeling Language (UML) mental spaces in analyzing Swahili conditional constructions to show how necessary, sufficient, and contributing conditions obtain on the embodied FoR networks level

Constructive Use of Errors in Teaching the UML Class Diagram in an IS Engineering Course

A class diagram is one of the most important diagrams of Unified Modeling Language (UML) and can be used for modeling the static structure of a software system. Learning from errors is a teaching approach based on the assumption that errors can promote learning. We applied a constructive approach of using errors in designing a UML class diagram in order to (a) categorize the students’ errors when they design a class diagram from a text scenario that describes a specific organization and (b) determine whether the learning-from-errors approach enables students to produce more accurate and correct diagrams. The research was conducted with college students (N = 45) studying for their bachelor’s degree in engineering. The approach is presented, and the learning-from-errors activity is illustrated. We present the students’ errors in designing the class diagram before and after the activity, together with the students’ opinions about applying the new approach in their course. Twenty errors in fundamental components of the class diagram design were observed. The students erred less after the activity of learning from errors. The displayed results show the relevance and potential of embedding our approach in teaching. Furthermore, the students viewed the learning-from-errors activity favorably. Thus, one of the benefits of our developed activity is increased student motivation. In light of the improved performance of the task, and the students’ responses to the learning-from-errors approach, we recommend that information systems teachers use similar activities in different fields and on various topics

Kirjoitetut tunnisteet peruskoulun luonnontieteiden diagrammeissa: kielelliset rakenteet ja diskurssisuhteet

Communication, by nature, is multimodal: it uses various forms (modes) of communication, such as spoken language, written language, illustrations, and many others to create meaning. Multimodality research is the study of communicative situations that rely on such various modes and their combinations. One form of multimodality very commonly seen in everyday life comes in diagrams, which can convey very complex concepts by combining visual expressive resources (such as illustrations or photographs), written language, and diagrammatic elements such as lines and arrows. The primary aim of my thesis is to establish whether the linguistic structures of written labels – that is, textual elements – in diagrams can inform the decomposition of visual expressive resources. Put simply, I seek to find if said visual elements can more accurately be divided into further, more granular units in accordance with linguistic patterns in their accompanying textual elements. To answer my main research question, I posit three sub-questions. First, if certain diagram types (macro-structures), such as tables, cycles, or cross-sections co-occur with specific linguistic patterns; second, if different rhetorical functions found in diagrams employ different structures in their written labels as well; and third, if these functions are signaled by other means in tandem with written language. Answering these questions can help in designing future multimodal corpora and their annotation schemata, increasing annotation accuracy and possibilities for their processing. The theoretical framework used in this thesis synthesizes theories from multimodality theory, discourse studies, and diagrams research. I approach diagrams from the perspective of multimodality, highlighting them as discursive artefacts. This is enabled by the diagrammatic mode, which establishes how discourse semantics can function in the context of diagrams and how their interpretation is dynamic; that is, each element or combination of multiple elements can in turn contextualize or be a part of other elements and their combinations on a different scale. I also discuss the discourse-semantic concepts of coherence and cohesion as they relate to multimodal artefacts: different elements, even if not linguistic, can combine to create semantically meaningful connections between constituents in such an artefact. To exemplify this, I also apply Rhetorical Structure Theory (RST), which seeks to formalize how units of discourse are interconnected and work towards a shared communicative goal. RST employs rhetorical relations such as ELABORATION and IDENTIFICATION to describe how units and their combinations relate to other parts of a text (or other communicative whole). The data I use consists of two interrelated and complementary multimodal corpora: AI2D and AI2D-RST. AI2D is a collection of primary-school textbook science diagrams, annotated for blobs (visual expressive resources), labels, and diagrammatic elements, created for question-answering purposes. It also contains the linguistic data in each of the corpus’s diagrams. AI2D-RST contains a subset of the diagrams in AI2D, expanding them with additional annotation layers for information on macro-structures, visual connectivity, and RST, describing each element’s rhetorical relation in the diagram. I computationally find each rhetorical relation containing a label in AI2D-RST, noting its type, the type of the diagram it appears in, and fetching the labels’ linguistic content from AI2D. I then process each label’s contents with spaCy, a library for natural language processing, for linguistic elements such as phrase types, part-of-speech patterns, and average word counts. The output contains data on each label’s rhetorical relation, the possible macro-structure it is contained in, and said linguistic structures. The results show that there are indeed some differences in how distinct rhetorical relations and macro-groups use language: for example, cycles contain the most verb phrases and highest word count, indicating the use of written language to explicate certain processes to viewers. As linguistic patterns differ across these classes and are contextualized by surrounding diagrammatic elements, approaching diagrams from a discursive standpoint may be beneficial for future empirical multimodality research as well as designing annotation schemata to be more intuitive for annotators. With larger datasets and further research, precise sets of rules containing linguistic structures and layout information may be developed to increase accuracy in probability-based computational analysis of diagrams

A REVIEW OF PROBLEMS AND CHALLENGES OF USING MULTIPLE CONCEPTUAL MODELS

Conceptual models are used to visualise, envisage, and communicate the requirements, structure, and behaviour of a system. Particularly, during design and analysis phases, a model can serve as a tool to recognise different components, elements, actors, and relationships involved in a system. However, as a domain becomes complex, multiple models are needed to capture different aspects of a system. Further, each conceptual model develops using different grammars, methods, and tools. Therefore, using multiple models to represent a complex system may result in several problems, and challenges. This research aims to identify, analyse, and classify the different problems and issues encountered when using multiple models during information systems analysis and design through a structured literature review. Several problems are identified and are classified into seven main categories based on their common characteristics. The results of this study may serve as a baseline information for researchers in further understand-ing different modelling approaches and how multiple models can be used in harmony and reduce risks and issues. Also, the list of problems gathered will give insights to professionals on which issues they may possibly encounter when inter-relating various models

Semantic networks

AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies

Optimality of syntactic dependency distances

It is often stated that human languages, as other biological systems, are shaped by cost-cutting pressures but, to what extent? Attempts to quantify the degree of optimality of languages by means of an optimality score have been scarce and focused mostly on English. Here we recast the problem of the optimality of the word order of a sentence as an optimization problem on a spatial network where the vertices are words, arcs indicate syntactic dependencies, and the space is defined by the linear order of the words in the sentence. We introduce a score to quantify the cognitive pressure to reduce the distance between linked words in a sentence. The analysis of sentences from 93 languages representing 19 linguistic families reveals that half of languages are optimized to a 70% or more. The score indicates that distances are not significantly reduced in a few languages and confirms two theoretical predictions: that longer sentences are more optimized and that distances are more likely to be longer than expected by chance in short sentences. We present a hierarchical ranking of languages by their degree of optimization. The score has implications for various fields of language research (dependency linguistics, typology, historical linguistics, clinical linguistics, and cognitive science). Finally, the principles behind the design of the score have implications for network science.Peer ReviewedPostprint (published version