Semantic Network Representation Research Articles

Effect of semantic distance on learning structured query language: An empirical study.

Students of database courses usually encounter difficulties in learning structured query language (SQL). Numerous studies have been conducted to improve how students learn SQL. However, learning SQL remains difficult. This study analyzed the difficulties in learning SQL from the viewpoint of semantic distance by using semantic network theory. An experiment involving a database course was performed to assess the influence of semantic distance on learners' understanding of SQL. The participants were requested to perform a query-writing task at the end of the course to investigate their understanding of SQL. The data analysis results indicated that the participants developed a better understanding of the formulation-to-planning transformation than the planning-to-coding transformation. This implies that the semantic distance of the planning-to-coding transformation is greater than that of the formulation-to-planning transformation, and the semantic distance of the planning-to-coding transformation is attributable to the semantic transformation from natural language to SQL, which are two essentially different languages and belong to different knowledge categories. Accordingly, this study concludes that SQL learning difficulties can mainly be ascribed to the planning-to-coding transformation because the large semantic distance. The findings suggest that SQL instructions should emphasize the semantic mapping of the planning-to-coding transformation by incorporating materials related to the transformation and should shorten the semantic distance involved in learning SQL. These two principles can be used to evaluate the effectiveness of SQL teaching methods in assisting SQL learning, and motivate researchers to develop more effective teaching methods from the viewpoint of semantic distance.

Design representation as semantic networks

Design representation is a common task in the design process to facilitate learning, analysis, redesign, communication, and other design activities. Traditional representation techniques rely on human expertize and manual construction and are difficult to repeat and scale. Here, we present a methodology that utilizes a readily available large-scale multidisciplinary design knowledge base (KB) to automatically generate design representation as a semantic network, i.e., a network of the entities and relations, based on design descriptions in textual form. The methodology requires no ad hoc statistics, but a readily available KB. Thus, the KB has an essential impact on the usefulness and effectiveness of the methodology. Based on a participatory study, we observe the effectiveness and differences of the semantic network representations that are automatically generated with alternative KBs. Specifically, a KB that is trained on engineering-related data, TechNet, provides a more sensible representation of engineering design than commonsense KBs, WordNet and ConceptNet, to the participants who are engineers. We further discuss the implications of the findings and future research directions to enhance design representation as semantic networks.

Understanding Enthymemes in Deductive Argumentation Using Semantic Distance Measures

An argument can be regarded as some premises and a claim following from those premises. Normally, arguments exchanged by human agents are enthymemes, which generally means that some premises are implicit. So when an enthymeme is presented, the presenter expects that the recipient can identify the missing premises. An important kind of implicitness arises when a presenter assumes that two symbols denote the same, or nearly the same, concept (e.g. dad and father), and uses the symbols interchangeably. To model this process, we propose the use of semantic distance measures (e.g. based on a vector representation of word embeddings or a semantic network representation of words) to determine whether one symbol can be substituted by another. We present a theoretical framework for using substitutions, together with abduction of default knowledge, for understanding enthymemes based on deductive argumentation, and investigate how this could be used in practice.

Teaching Method for Entity–Relationship Models Based on Semantic Network Theory

Students in database courses often have difficulty learning entity–relationship (ER) modeling. According to semantic network theory, learning to construct an ER diagram for a database problem requires complex semantic transformations between the problem and the diagram. Such complex transformation may require excessive mental effort by learners, jeopardizing their learning outcomes. A concept map is a learning tool that incorporates elements of both learning theory and semantic network theory. In this study, concept maps were used to describe the semantic transformation process to increase learner understanding of ER modeling. An empirical experiment was conducted on two database courses (one concept-map-based and one conventional course) to examine the effect of using concept maps on understanding ER modeling according to cognitive load theory. The experimental results revealed that the concept-map-based teaching method was superior to the conventional teaching method because it improved mental efficiency by reducing extraneous load while increasing germane load. Moreover, concept maps can be used as a medium to facilitate communication regarding ER modeling problems between learners and instructors, thereby improving learning efficiency. The results can help educators and researchers understand the effectiveness of concept maps for ER model learning, motivate them to resolve learning difficulties, and encourage them to develop improved teaching methods by using semantic network theory.

Analyzing Knowledge Retrieval Impairments Associated with Alzheimer's Disease Using Network Analyses.

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 (listing of items in a category), 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.

Implementation of the Intelligent Question Answering System Based on Remote Service Framework

Remote answering system of modern distance education system is an indispensable component. However, the current system is based on answering user input on the exam questions simple keyword matching, accuracy and query the user interface cannot meet the needs of users. We have designed a semantic network theory to build up intelligent answering system. We analyzed the necessity of establishing intelligent answering system, and presents a model of intelligent question answering system based on its technology defined areas, and two university computer courses as a source of knowledge to achieve the function of the system. The results show that the proposed method can improve the accuracy of the query, user-friendly and convenient. System Based on J2EE three-tier system, in the middle layer of the advanced MVC design pattern thought to achieve page display and business logic separation, improve system maintainability.

Information Resources Usage in Project Management Digital Learning System

The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that takes into account these features. The paper deals with the structuring of project management programs of academic mobility. The features of these projects show their impact on the progress and results of the implementation.It is shown that traditional approaches to solving the problems of academic mobility management applications to integrate the process of setting objectives and planning projects. But due to the complexity of academic mobility programs to solve these problems effectively without decomposing it into components can be managed quite difficult. Therefore, the main idea of this paper is that an increasing the efficiency of the university on the basis of a possible application of rational structuring of academic mobility programs. The features of these projects shows their impact on the progress and results of their implementation and the ways of construction of project management Digital learning systems.The experience of universities of many countries shows that an effective tool to overcome these obstacles is Digital learning, which allows to get all necessary knowledge at any time and in any place. The identity of urgency takes in terms of academic mobility when students and teachers work in another country. The paper presents the analyse of the structure of scientific and methodological basis of project management systems. The model determines the effect of academic mobility project management tools through knowledge of business administration at the progress of the educational projects. In this structure are identified two parts: creation tool to identify knowledge users and how they change, and the creation of information tools effect on these people.

A Study on the Difficulties of Learning Phase Transition in Object-Oriented Analysis and Design From the Viewpoint of Semantic Distance

Students in object-oriented analysis and design (OOAD) courses typically encounter difficulties transitioning from object-oriented analysis (OOA) to logical design (OOLD). This study conducted an empirical experiment to examine these learning difficulties by evaluating differences between OOA-to-OOLD and OOLD-to-object-oriented-physical-design (OOPD) transitions from the perspective of semantic distance in the context of semantic network theory. The experimental results indicated that two factors inhibit the learning of OOA-to-OOLD transition: the semantic distance between phases and the precise degree of transition relations. The OOA-to-OOLD transition exhibits a greater semantic distance than the OOLD-to-OOPD transition, which causes difficulty in retrieving and understanding the semantics of OOA-to-OOLD transition relations. OOA-to-OOLD transition relations are less precise, which causes learners to have difficulty in developing efficient schema acquisition and automation when learning OOA-to-OOLD transition. These results provide further insight into the cognitive processes of learners who are learning phase transition. This may guide educators to develop an effective learning instruction method for phase transition and motivate researchers to remedy the inherent deficiency of phase transition in OOAD by considering the cognitive processes of developers.

Modeling semantic knowledge structures for creative problem solving: Studies on expressing concepts, categories, associations, goals and context

This paper presents a model for semantic network representation. According to the model, the main purpose of a semantic network is to help to correctly and quickly understand the features and structure of the solutions, and then make reliable predictions about their outcomes and rewards. These objectives are achieved under constrains like limited resources (e.g., memory size and access), time limitations, and variations of solution characteristics. The paper summarizes the elements of the model and presents a set of properties that describe the effectiveness of semantic networks in creative problem solving, including design. Six examples (based on three case studies from the related literature and three experimental studies conducted by the authors) discuss the model elements, characteristics, and properties.

OBJECT-BASED KNOWLEDGE REPRESENTATION OF FEMALE RELATED ISSUES FROM THE HOLY QURAN

Focusing on the use of Semantic Network and Conceptual Graph (CG) representations, this paper presents an easy way in understanding concepts discussed in the Holy Quran. Quran is known as the main source of knowledge and has been a major source reference for all types of problems. However understanding the issues and the solution from the Quran is diffi cult due to lack of understanding of Quran literature. Meticulously, the Quran contains much important information related to female. However, this information are scattered and complexly linked. Technically, to extract and present the encapsulated knowledge on female matters in the Quran is a challenging task. Thus, this paper discusses on how to understand and represent the knowledge in an easy way. A total of 18 female terms are identifi ed. Through the terms, the name of surah, verses number and text from the verses are gathered. The texts are then analyzed and clustered into specifi c issues. Result of the analysis that consists of extracted knowledge on female issues is presented in a systematic structure using Semantic Network and CG. The strength and advantages of both approaches are compared, discussed and presented.

Priming semantic concepts affects the dynamics of aesthetic appreciation

Aesthetic appreciation (AA) plays an important role for purchase decisions, for the appreciation of art and even for the selection of potential mates. It is known that AA is highly reliable in single assessments, but over longer periods of time dynamic changes of AA may occur. We measured AA as a construct derived from the literature through attractiveness, arousal, interestingness, valence, boredom and innovativeness. By means of the semantic network theory we investigated how the priming of AA-relevant semantic concepts impacts the dynamics of AA of unfamiliar product designs (car interiors) that are known to be susceptible to triggering such effects. When participants were primed for innovativeness, strong dynamics were observed, especially when the priming involved additional AA-relevant dimensions. This underlines the relevance of priming of specific semantic networks not only for the cognitive processing of visual material in terms of selective perception or specific representation, but also for the affective–cognitive processing in terms of the dynamics of aesthetic processing.

Conceptual modeling of causal map: Object oriented causal map

This study has been started from question “Is there a methodology that can make causal map comprised of causality as a database by using conceptual modeling method?” In this research, causal map is proposed to represent causal relation by using conceptual modeling method. Therefore, we formalize causality as a cognitive rule to allow us to control changes in the decision making environment. Such causality is embedded in the real world (application domain). And, user (decision maker) use to represent set of causality which decision-makers have retrieved from the set of knowledge or experiences in application domain. Such set of causality that decision-makers possess in their know–how and short (long) term memory are usually formalized by causal map. It is verified for users whether this causal map is helpful in solving their problems. By extending the basis of conceptual modeling theory (ontology theory, classification theory, decomposition theory, and semantic network theory), we introduce a concept of causal entity diagram and address why causal map is needed to analyze a specific domain knowledge for given decision problem solving. Finally, object oriented causal map (O2CM) were employed to verify usefulness of causal map for user (decision maker) in this study.

How Does the General Public Evaluate Risk Information? The Impact of Associations with Other Risks

There is a considerable body of knowledge about the way people perceive risks using heuristics and qualitative characteristics, and about how risk information should be communicated to the public. However, little is known about the way people use the perception of known risks (associated risks) to judge an unknown risk. In a first, qualitative study, six different risks were discussed in in-depth interviews and focus group interviews. The interviews showed that risk associations played a prominent role in forming risk perceptions. Associated risks were often mentioned spontaneously. Second, a survey study was conducted to confirm the importance of risk associations quantitatively. This study investigated whether people related unknown risks to known risks. This was indeed confirmed. Furthermore, some insight was gained into how and why people form risk associations. Results showed that the semantic category of the unknown risks was more important in forming associations than the perceived level of risk or specific risk characteristics. These findings were in line with the semantic network theory. Based on these two studies, we recommend using the mental models approach in developing new risk communications.

Semantic network representation of computer-mediated discussions: Conceptual facilitation form and knowledge acquisition

Prior research on computer-mediated discussions examined their effects on knowledge acquisition without considering the role of the discussion representation. In this study, we investigate and compare the effect of semantic network discussion representations on knowledge acquisition to that of the threaded representations featured in most traditional discussion forums. Furthermore, we identify, define and operationalize a new, i.e., conceptual facilitation (validation of the conceptual organization of the discussion), assessing its role in knowledge acquisition at different levels of restrictiveness. The empirical results of a field experiment indicate that semantic network discussion representations enable the acquisition of more complex and better-integrated knowledge structures than threaded discussion representations. Conceptual facilitation forms entailing different levels of restrictiveness are also found to play a significant role. As the level of conceptual facilitation restrictiveness decreases, knowledge acquisition improves. Our findings empirically demonstrate the importance of accounting for discussion representation as a contingency factor in explaining group discussion processes and outcomes. To practitioners, our study provides empirical evidence on the advantages of semantic networks over threaded representations as an alternative mode for computer-mediated discussion representations. We also suggest guidelines for the selection of appropriate conceptual facilitation for discussion forums intended for knowledge acquisition.

Semantic Network Discussion Representation: Applicability and Some Potential Benefits

Collaborative tools typically employ linear threading for representing and organizing the contributions of the discussants. We argue that linear representations provide little support for the conceptualization, contextualization, and visualization of the discussion. To address these drawbacks, we propose the usage of semantic networks for representing discussions. In this paper, we demonstrate the applicability of semantic network representation to discussions through the development of a prototype named CollectiveThought. We also test some of the potential benefits of semantic network discussion representation with an empirical study. More specifically, we compare these two modes of discussion representation (i.e., linear versus semantic network) in terms of effects on the communication process as measured by perceived contextualization and on the communication outcome as measured by mutual understanding, using both subjective and objective indicators. The results show that semantic network discussion representation leads to a higher level of perceived contextualization and better mutual understanding.

Alcohol and Aggression Without Consumption

Researchers and the lay public have long known of a link between alcohol and aggression. However, whether this link results from alcohol's pharmacological effects or is merely an artifact of the belief that alcohol has been consumed (i.e., placebo effect) has been debated. The current experiments examined the propensity for alcohol-related cues to elicit aggressive thoughts and hostile perceptions in the absence of alcohol or placebo consumption. In Experiment 1, participants made faster lexical decisions concerning aggression-related words following alcohol-related primes compared with neutral primes. In Experiment 2, participants who first were exposed to alcohol advertisements subsequently rated the behavior of a target person as more hostile than participants who initially viewed control advertisements. Furthermore, this effect was largest among participants who most strongly associated alcohol and aggression. Findings are discussed in terms of semantic network theory and links in memory between alcohol and its anticipated effects.

The impact of alternative diagrams on the accuracy of recall: A comparison of star-schema diagrams and entity-relationship diagrams

Data warehouses can be constructed using either a relational or a dimensional model. This research compares the diagrammatic representations of these two models. Semantic network theory suggests that, because of their structure, star-schema diagrams (SSDs) are more understandable than entity-relationship diagrams (ERDs). Through two experiments that test both the accuracy and pattern of users' recall, we find strong evidence that SSDs are easier to understand than ERDs. Our findings imply that the use of the dimensional model should help decision makers query the warehouse directly, thereby reducing costs and the need for intervention and support by IT professionals.

Methods for automated concept mapping between medical databases

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

A geographic knowledge representation system for multimedia geospatial retrieval and analysis

Digital libraries serving multimedia information that may be accessed in terms of geographic content and relationships are creating special challenges and opportunities for networked information systems. An especially challenging research issue concerning collections of geo-referenced information relates to the development of techniques supporting geographic information retrieval (GIR) that is both fuzzy and concept-based. Viewing the meta-information environment of a digital library as a heterogeneous set of services that support users in terms of GIR, we define a geographic knowledge representation system (GKRS) in terms of a core set of services of the meta-information environment that is required in supporting concept-based access to collections of geospatial information. In this paper, we describe an architecture for a GKRS and its implementation in terms of a prototype system. Our GKRS architecture loosely couples a variety of multimedia knowledge sources that are in part represented in terms of the semantic network and neural network representations developed in artificial intelligence research. Both textual analysis and image processing techniques are employed in creating these textual and iconic geographical knowledge structures. The GKRS also employs spreading activation algorithms in support of concept-based knowledge retrieval. The paper describes implementational details of several of the components of the GKRS as well as discussing both the lessons learned from, and future directions of, our research.

The SNePS BDI architecture

Most AI systems are effective either for inference or for acting/planning but not for both. The SNePS BDI architecture uses propositional semantic network representations of beliefs, goals, acts, plans, and a representational concept called transformers. Transformers help capture a unified approach to acting and inference. They can be used to represent reasoning rules, reactive desires, desires for acquiring knowledge, preconditions and effects of actions as well as plan decompositions. A rational engine module operates on these representations and is responsible for the agent's reasoning and acting behavior. SNeRE, the SNePS rational engine, employs a quasi-concurrent message passing scheme, implements the underlying logic as well as the action theory, has forward, backward and bidirectional inference and acting. It also incorporates the notions of truth maintenance and spreading activation.