Graphical Knowledge Representation Research Articles

Yes We Can: Promoting Gender Equality from University to Primary Education Teaching through the Design of Knowledge Models

Gender equality and women’s empowerment are important issues that still need to be addressed, despite some progress in recent years. The United Nations 2030 Agenda for Sustainable Development includes 17 Sustainable Development Goals (SDGs) which aim to tackle various global challenges, including the 4th (Quality Education) and 5th (Gender Equality) goals that were used to frame an activity in a science education subject in a primary education teaching degree. Previous research has found a decrease in girls’ interest in STEM and STEM-related jobs in primary and secondary education. To encourage girls in primary education towards STEM-related professions, an activity was organized in a science subject at a university in Navarre, Spain. The goal of this activity was to identify inspiring women scientists who could serve as role models for university and primary school students. A knowledge model, which is a concise and graphical representation of knowledge through concept maps, was created using the methodology of knowledge model design (Cañas et al. Revista De Informática Educativa 13:145–158, 2000) to showcase all the women scientists selected by the students. This final model is publicly accessible and can be shared with any educational center interested in promoting gender diversity in STEM fields. The results showed that the students learned meaningfully as they inquired about women scientists in professions that are typically considered more suitable for men. As these students are trainee primary school teachers, they can become part of an educational network that empowers girls by developing STEM-related activities that promote gender equality.

A New Distance-Type Fuzzy Inference Method Based on Characteristic Parameters

Reasoning is a cognitive activity that leverages knowledge to generate solutions to problems. Knowledge representations in the brain require both symbolic and graphical information since visual information is figurative and conveys a large amount of information. Consequently, graphical knowledge representation is often employed in reasoning. Distance-type fuzzy inference utilizes the distance information between the antecedent and the set of facts as the basis for inference. Compared to Mamdani inference, the distance-type fuzzy inference method not only satisfies the convexity and asymptotic properties of the inference results but also adheres to the separation rule (modus ponens), a fundamental principle in inference. This paper discusses extensions of distance-type fuzzy inference methods to handle spatial figures. In this paper, we first explain the distance-type fuzzy inference method. Then, we discuss the concept representation in the feature space and independent parameters that can completely express the characteristics of a figure in space, which are defined as “characteristic parameters”. Furthermore, we describe the correspondence between figures and vectors in the feature space, propose a new distance-type fuzzy inference method based on characteristic parameters and describe its characteristics. Finally, an example is used to demonstrate the inference results of this new distance-type fuzzy inference method.

Spatial versus graphical representation of distributional semantic knowledge.

Spatial distributional semantic models represent word meanings in a vector space. While able to model many basic semantic tasks, they are limited in many ways, such as their inability to represent multiple kinds of relations in a single semantic space and to directly leverage indirect relations between two lexical representations. To address these limitations, we propose a distributional graphical model that encodes lexical distributional data in a graphical structure and uses spreading activation for determining the plausibility of word sequences. We compare our model to existing spatial and graphical models by systematically varying parameters that contributing to dimensions of theoretical interest in semantic modeling. In order to be certain about what the models should be able to learn, we trained each model on an artificial corpus describing events in an artificial world simulation containing experimentally controlled verb-noun selectional preferences. The task used for model evaluation requires recovering observed selectional preferences and inferring semantically plausible but never observed verb-noun pairs. We show that the distributional graphical model performed better than all other models. Further, we argue that the relative success of this model comes from its improved ability to access the different orders of spatial representations with the spreading activation on the graph, enabling the model to infer the plausibility of noun-verb pairs unobserved in the training data. The model integrates classical ideas of representing semantic knowledge in a graph with spreading activation and more recent trends focused on the extraction of lexical distributional data from large natural language corpora. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A synergy-effect-incorporated fuzzy Petri net modeling paradigm with application in risk assessment

As a graphical knowledge representation and reasoning tool, fuzzy Petri net (FPN) is widely used in risk assessment, fault diagnosis and many other fields. It can express the complex causal relationships among events and make fuzzy reasoning about them. In practical applications, there may be synergy effects among events, i.e., the occurrence of event a may increase or decrease the truth degree of event b. Traditional FPNs fail to express such effects, thus neglecting them during their reasoning procedure. To address this issue, this study proposes an FPN considering them for the first time. Firstly, this study illustrates the concept of synergy effects via practical application examples. Secondly, it develops the transition enabling conditions and truth degree calculation rules incorporated with synergy effects. Then, it formally defines a synergy-effect-incorporated fuzzy Petri net (SFPN). On this basis, it presents the production rules, transition firing rules and reasoning algorithm of SFPN to perform knowledge representation and reasoning for knowledge-based systems affected by synergy effects. Finally, it takes the risk assessment of equipment failure caused by corrosion as a running case to validate its feasibility and advantages.

Perspective Statement on the Life Given by Mrs. Cantlie—Legacy of an Iconic Graphical Representation of Neuroscientific Knowledge

Perspective Statement on the Life Given by Mrs. Cantlie—Legacy of an Iconic Graphical Representation of Neuroscientific Knowledge

Homonyms and Synonyms in NOK Method

Knowledge representation is one of the areas covered by artificial intelligence. Method Nodes of Knowledge (NOK) is one of the methods which allows a graphical representation and organization of knowledge in the form of a DNOK diagram (Diagram of Nodes Of Knowledge) as a network of nodes. With this diagram the meaning of sentences is preserved. The problem occurs when words that have synonyms or homonyms, and whose meaning depends on the context, appear in the sentence which is being modeled. Every term used in modeling sentences is written in a dictionary, including homonyms and synonyms. This paper presents a metamodel mode of writing homonyms and synonyms into the dictionary from which words are used for modeling. This demonstrates that the NOK method can be applied to modeling dictionaries of natural languages.

What helps most in learning how to draw a human figure? A study of children’s epistemological, pictorial and learning conceptions / ¿Qué ayuda más para aprender a dibujar una persona? Un estudio de las concepciones epistemológicas, pictóricas y de aprendizaje en la niñez

This study explores how school-aged children conceive relevant sources in learning how to draw the human figure: a live model, a photograph, a drawing and thought. Our purpose was to identify children´s preferences in this regard, to study their evaluation of the potential for help they see in those sources, and analyse whether those justifications were related to children’s developmental-educational level. Sixty children from first (20), third (20) and fifth year of elementary school (20) were interviewed individually. Children’s preferences did not vary according to school level, but their justifications regarding the value of each source did. A developmental trend in the direction of a more flexible consideration of the drawer/learner’s agency and of an incipient epistemological multiplism was found. Results contribute to two fields of developmental-educational research: development of graphical representation and conceptions of learning and knowledge.

Conceptology: Using Concept Map for Knowledge Representation and Ontology Construction

Tools on using concept map for knowledge representation, ontology for domain rule generation and conversion from concept map to ontology are invented over recent years. Some focus on graphical representation, such as concept map, of domain knowledge for better user understanding, while others introduce rules that concept map fails to represent to extend the contents of generated ontology. To combine both advantages, a tool Conceptology is presented. In the context of MVB (Multifunction Vehicle Bus) standard domain, a group of generation rules from enriched concept map to OWL (Web Ontology Language) are presented, and Conceptology offers implementation of proposed rules to support graphical representation of MVB domain knowledge and conversion from concept map to OWL. Two Conceptology application cases from MVB standards are demonstrated.

RDF annotation of Second Life objects: Knowledge Representation meets Social Virtual reality

We have designed and implemented an application running inside Second Life that supports user annotation of graphical objects and graphical visualization of concept ontologies, thus providing a formal, machine-accessible description of objects. As a result, we offer a platform that combines the graphical knowledge representation that is expected from a MUVE artifact with the semantic structure given by the Resource Framework Description (RDF) representation of information.

Temporal Bayesian Knowledge Bases – Reasoning about uncertainty with temporal constraints

Time is ubiquitous. Accounting for time and its interaction with change is crucial to modeling the dynamic world, especially in domains whose study of data is sensitive to time such as in medical diagnosis, financial investment, and natural language processing, to name a few. We present a framework that incorporates both uncertainty and time in its reasoning scheme. It is based on an existing knowledge representation called Bayesian Knowledge Bases. It provides a graphical representation of knowledge, time and uncertainty, and enables probabilistic and temporal inferencing. The reasoning scheme is probabilistically sound and the fusion of temporal fragments is well defined. We will discuss some properties of this framework and introduce algorithms to ensure groundedness during the construction of the model. The framework has been applied to both artificial and real world scenarios.

MAKING CONCEPT MAPS USEFUL FOR PHYSICS TEACHER EDUCATION: ANALYSIS OF EPISTEMIC CONTENT OF LINKS

In physics teacher education the use of graphical knowledge representation tools like concept maps are abundantly used because they are known to support the formation of organised knowledge. It is widely assumed that certain structural characteristics of concept maps can be connected to the usefulness of content. In order to study this relationship, the concept maps made by pre-service physics teachers are examined here. The design principles of the concept maps are based on quantitative experiments and modelling as the basic procedures in physics concept formation. The epistemic plausibility of justifications written in links is evaluated by using a four-level classification introduced here. The results show that the epistemic analysis of links affects remarkably to the acceptability of knowledge. The advantages of such concept mapping technique in supporting the conceptual understanding in physics teacher education are discussed as well as their usefulness in making plans for teaching. Key words: concept maps, epistemic content, physics teacher education, science education.

Student modeling for a web-based self-assessment system

Detecting weaknesses in students’ knowledge may constitute an objective of testing. Computerized test systems, which can be tailored to students’ knowledge level, are appropriate to realize this objective. These systems are not only used to reveal the students’ knowledge, but also help tutors understand the problems in the educational process. This paper reviews the student modeling problem for computer-based test systems, and also proposes a novel method for the graphical representation of student knowledge. First, we present our test practice system ‘Intelligent’, which is followed by the evaluation of the system in real classroom conditions as well as the effect over students’ knowledge acquisition.

A Bayesian model for disclosure control in statistical databases

The paper proposes a novel approach for on-line max and min query auditing, in which a Bayesian network addresses disclosures based on probabilistic inferences that can be drawn from released data. In the literature, on-line max and min auditing has been addressed with some restrictive assumptions, primarily that sensitive values must be all distinct and the sensitive field has a uniform distribution. We remove these limitations and propose a model able to: provide a graphical representation of user knowledge; deal with the implicit delivery of information that derives from denying the answer to a query; and capture user background knowledge. Finally, we discuss the results of experiments aimed at assessing the scalability of the approach, in terms of response time and size of the conditional probability table, and the usefulness of the auditor system, in terms of probability to deny.

Designing Visual Languages for Description Logics

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.

Mining e-Learning domain concept map from academic articles

Recent researches have demonstrated the importance of concept map and its versatile applications especially in e-Learning. For example, while designing adaptive learning materials, designers need to refer to the concept map of a subject domain. Moreover, concept maps can show the whole picture and core knowledge about a subject domain. Research from literature also suggests that graphical representation of domain knowledge can reduce the problems of information overload and learning disorientation for learners. However, construction of concept maps typically relied upon domain experts in the past; it is a time consuming and high cost task. Concept maps creation for emerging new domains such as e-Learning is even more challenging due to its ongoing development nature. The aim of this paper is to construct e-Learning domain concept maps from academic articles. We adopt some relevant journal articles and conference papers in e-Learning domain as data sources, and apply text-mining techniques to automatically construct concept maps for e-Learning domain. The constructed concept maps can provide a useful reference for researchers, who are new to the e-Leaning field, to study related issues, for teachers to design adaptive learning materials, and for learners to understand the whole picture of e-Learning domain knowledge.

Data mining of Bayesian networks using cooperative coevolution

This paper describes a novel data mining algorithm that employs cooperative coevolution and a hybrid approach to discover Bayesian networks from data. A Bayesian network is a graphical knowledge representation tool. However, learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second approach searches good network structures according to a metric. Unfortunately, the two approaches both have their own drawbacks. Thus, we propose a novel algorithm that combines the characteristics of these approaches to improve learning effectiveness and efficiency. The new learning algorithm consists of the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian networks are generated by a cooperative coevolution genetic algorithm (GA). We conduct a number of experiments and compare the new algorithm with our previous algorithm, Minimum Description Length and Evolutionary Programming (MDLEP), which uses evolutionary programming (EP) for network learning. The results illustrate that the new algorithm has better performance. We apply the algorithm to a large real-world data set and compare the performance of the discovered Bayesian networks with that of the back-propagation neural networks and the logistic regression models. This study illustrates that the algorithm is a promising alternative to other data mining algorithms.

Conceptual framework for document semantic modelling: an application to document and knowledge management in the legal domain

The main contribution of this paper is to lay down a conceptual framework for document semantics modeling. This framework provides a generic graphical knowledge representation model based on Sowa’s conceptual structures. Modeling primitives are introduced to represent factual and ontological knowledge that can be expressed in electronic documents. Binding features are proposed so as to keep knowledge representation and knowledge formulation linked together. This framework may be applied to various domains and may accept, for this purpose, many different ontological extensions. Thus an extension is provided so as to properly handle the particular kind of knowledge encountered in the legal domain.

A model for a graphical representation of knowledge applied in two DSS's

ABSTRACT Most existing project management softwares use as their input, the description of the project activities, their duration and their interrelations. Regardless of the simplicity of the software, whenever users design a new schedule for their project, they are still forced to prepare manually the planning model and the scheduling network. These types of software possess few mechanisms for the validation of data used to prepare the scheduling network and, consequently, users are still solely responsible for their preparation. This paper presents a model for the graphical representation of knowledge and the implementation of this approach in two Knowledge—based DSS's to generate and process the necessary information for the scheduling of a building and road construction project. The results demonstrate that this approach is well suited for this kind of application. This work describes the approach of knowledge modelling, the computer—assisted generation of a validated scheduling network for building and roads construction, and the Knowledge—based DSS's in the context of the project management.

An extended temporal system based on points and intervals

This paper proposes axioms for temporal systems based on a discrete set of intervals and points which are treated equally as primitive elements. Temporal ordering is specified by means of the primitive relation “meets”. The axioms, and a graphical representation of temporal knowledge, are specified formally by using the Z language. A consistency condition for a temporal database is given in terms of the cyclic properties of the graphical representation, and an algorithm for consistency checking is provided. Formal proofs of Allen's transitivity table for interval relations are given. The paper addresses some hitherto unresolved issues in the use of interval based systems for temporal databases and proposes a solution. These issues are the problems involved in modelling “open” and “closed” intervals.

Graphical representation of help knowledge for intelligent tutoring systems

Today an expected facility of interactive computer systems and application programs is an extensive, well-designed, and well-written online help system. A help system frequently provides online tutorial text concerning the operation of the system or application. In the case of Intelligent Tutoring Systems (ITS), an embedded help system provides help with topics in the training domain. In this environment, the help system not only provides user initiated help, but also help text introduced by the embedded computer expert for the purpose of directing or correcting student actions. Explanatory material contained in help systems is called help documentation, and is usually accessed through a “HELP” command. Identification of relevant help texts is difficult for a user, and is a special case of the general information retrieval problem. This paper discusses methods for the retrieval of help knowledge items using graphical, on-screen, tree structures. The configuration of these “study trees” conforms to the hierarchical nature of the tutorial information being referenced. Selection of natural language terms affords initial entry to the trees. A user of the help system browses the trees directly on-screen and consults the subject help texts at will. The study tree presentation facilitates effective selection of pertinent help texts and relates the collection of help material into an integrated whole.