• Journal of Educational Research in Mathematics
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• 제11권2호
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• pp.239-256
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• 2001

The notion of metaphor has been increasingly popular in research of mathematics education. In particular, metaphor becomes a useful unit for analysis to provide a profound insight into mathematical reasoning and problem solving. In this context, this paper takes metaphor as an analytic unit to examine the relationship between objectivity and subjectivity in mathematical reasoning. Specifically, the discourse analysis focuses on the code switching between literal language and metaphor in mathematical discourse. It is shown that the linguistic code switching is parallel with the switching between two different kinds of mathematical knowledge, that is, factual knowledge and mathematical imagination, which constitute objectivity and subjectivity in mathematical reasoning. Furthermore, the pattern of the linguistic code switching reveals the dialectical relationship between the two poles of mathematical reasoning. Based on the understanding of the dialectical relationship, this paper provides some educational implications. First, the code-switching highlights diverse aspects of mathematics learning. Learning mathematics is concerned with developing not only technicality but also mathematical creativity. Second, the dialectical relationship between objectivity and subjectivity suggests that teaching and teaming mathematics is socioculturally constructed. Indeed, it is shown that not all metaphors are mathematically appropriated. They should be consistent with the cultural model of a mathematical concept under discussion. In general, this sociocultural perspective on mathematical metaphor highlights the sociocultural organization of teaching and loaming mathematics and provides a theoretical viewpoint to understand epistemological diversities in mathematics classroom.

• IE interfaces
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• 제23권1호
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• pp.48-57
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• 2010

A tryout is one of the most important process in development and production of dies. For automotive press dies, it takes 3 to 4 months during the vehicle development process. Moreover, useful information and knowledge from tryout process is very important to design and production planning of dies. In this paper, we developed a new supporting system for making and managing tryout reports of an automotive press die. The CBDTS(Case-Based reasoning for Die Tryout report System) was developed and applied using case-based reasoning method in order to reduce time and manage knowledge of tryout. It consists of "Class Retrieval Wizard", "Case Cleansing Module", and "Case Viewer." Also, this CBDTS could be a channel to integrate field information with enterprise-wide information management systems as well. The CBDTS was applied to a Korean automotive press die shop, and the results were very satisfied in both quantitative and qualitative manners.

• Journal of Information Processing Systems
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• 제13권6호
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

• Journal of Korean Society for Geospatial Information Science
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• 제19권1호
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• pp.13-20
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• 2011

LBS services is a user-centric location based information service, where its importance has been discussed as an essential engine in an Ubiquitous Age. We aimed to develop an ontology reasoning system that enables users to derive recommended results suitable through selection standard reasoning according to various users' preferences. In order to achieve this goal, we designed the Geo-ontology system which enabled the construction of personal characteristics of users, knowledge on personal preference and knowledge on spatial and geographical preference. We also integrated a function of reasoning relevant information through the construction of Cost Value ontology using multi-criteria decision making by giving weight according to users' preference.

• KIPS Transactions on Software and Data Engineering
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• 제9권8호
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• pp.243-250
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• 2020

Knowledge graph-based question answering not only requires deep understanding of the given natural language questions, but it also needs effective reasoning to find the correct answers on a large knowledge graph. In this paper, we propose a deep neural network model for effective reasoning on a knowledge graph, which can find correct answers to complex questions requiring multi-hop inference. The proposed model makes use of highly expressive bilinear graph neural network (BGNN), which can utilize context information between a pair of neighboring nodes, as well as allows bidirectional feature propagation between each entity node and one of its neighboring nodes on a knowledge graph. Performing experiments with an open-domain knowledge base (Freebase) and two natural-language question answering benchmark datasets(WebQuestionsSP and MetaQA), we demonstrate the effectiveness and performance of the proposed model.

• Journal of Korea Multimedia Society
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• 제4권2호
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• pp.189-196
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• 2001

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule - inference network. and the traditional propagation rule is modified.

• The KIPS Transactions:PartB
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• 제9B권6호
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• pp.783-790
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• 2002

This paper proposes a fuzzy Pr/T net representation of interval-valued fuzzy set reasoning, where fuzzy production rules are used for knowledge representation, and the belief of fuzzy production rules are represented by interval-valued fuzzy sets. The presented interval-valued fuzzy reasoning algorithm is much closer to human intuition and reasoning than other methods because this algorithm uses the proper belief evaluation functions according to fuzzy concepts in fuzzy production rules.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 한국산업정보학회 2000년도 추계공동학술대회논문집
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• Spatial Information Research
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• 제9권2호
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• pp.171-184
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• 2001

The nature of ill-defined planning problems makes expert systems difficult to acquire and represent knowledge for decision making in urban planning processes. In order to resolve these problems, a case-based reasoning method was applied to develop a spatial analysis system for urban planning. A case study was conducted in a residential land use planning process. The result of the study revealed the effectiveness of reasoning by the spatial analysis system and the possibility of its future application. More accumulation of information on other successful cases should be sought to yield better results

• ETRI Journal
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• 제46권1호
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• pp.11-21
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• 2024

We focus on open-domain question-answering tasks that involve a chain-of-reasoning, which are primarily implemented using large language models. With an emphasis on cost-effectiveness, we designed EffiChainQA, an architecture centered on the use of small language models. We employed a retrieval-based language model to address the limitations of large language models, such as the hallucination issue and the lack of updated knowledge. To enhance reasoning capabilities, we introduced a question decomposer that leverages a generative language model and serves as a key component in the chain-of-reasoning process. To generate training data for our question decomposer, we leveraged ChatGPT, which is known for its data augmentation ability. Comprehensive experiments were conducted using the HotpotQA dataset. Our method outperformed several established approaches, including the Chain-of-Thoughts approach, which is based on large language models. Moreover, our results are on par with those of state-of-the-art Retrieve-then-Read methods that utilize large language models.