A Bisociative Design Framework for Knowledge Discovery Across Seemingly Unrelated Product Domains

2012 ◽  
Author(s):  
Conrad S. Tucker ◽  
Sung Woo Kang
Keyword(s):  
Domain Knowledge ◽  
Current Data ◽  
Data Driven ◽  
Design Framework ◽  
Specific Domain ◽  
Cross Domain ◽  
Product Design Process ◽  
Product Domains ◽  
Multiple Domains ◽  
Novel Design

The Bisociative Design framework proposed in this work aims to quantify hidden, previously unknown design synergies/insights across seemingly unrelated product domains. Despite the overabundance of data characterizing the digital age, designers still face tremendous challenges in transforming data into knowledge throughout the design processes. Data driven methodologies play a significant role in the product design process ranging from customer preference modeling to detailed engineering design. Existing data driven methodologies employed in the design community generate mathematical models based on data relating to a specific domain and are therefore constrained in their ability to discover novel design insights beyond the domain itself (I.e., cross domain knowledge). The Bisociative Design framework proposed in this work overcomes the limitations of current data driven design methodologies by decomposing design artifacts into form patterns, function patterns and behavior patterns and then evaluating potential cross-domain design insights through a proposed multidimensional Bisociative Design metric. A hybrid marine model involving multiple domains (capable of flight and marine navigation) is used as a case study to demonstrate the proposed Bisociative Design framework and explain how associations and novel design models can be generated through the discovery of hidden, previously unknown patterns across multiple, unrelated domains.

Categorization flexibility and unconventional choices: is life an adventure?

2020 ◽  
Vol 54 (8) ◽  
pp. 1963-1986
Author(s):  
Tilottama G. Chowdhury ◽  
Feisal Murshed
Keyword(s):  
Interactive Effect ◽  
Interactive Effects ◽  
Cognitive Capacity ◽  
Purchase Behavior ◽  
Consumer Decision Making ◽  
Cross Sectional ◽  
Content Type ◽  
Cross Domain ◽  
Multiple Domains

Purpose This paper proposes that categorization flexibility, operationalized as the cognitive capacity that cross-categorizes products in multiple situational categories across multiple domains, might favorably influence a consumer’s evaluation of unconventional options. Design/methodology/approach Experimental research design is used to test the theory. An exploratory study first establishes the effect of categorization flexibility in a non-food domain. Study 1 documents the moderating role of decision domain, showing that the effect works only under low- (vs high-) consequence domain. Studies 2A and 2B further refine the notion by showing that individuals can be primed in a relatively higher categorization flexibility frame of mind. Study 3 demonstrates the interactive effect of categorization flexibility and adventure priming in a high-consequence domain. Study 4 integrates the interactive effects of decisions with low- vs high-consequence, adventure priming and categorization flexibility within a single decision domain of high consequence. Findings Consumers with higher- (vs lower-) categorization flexibility tend to opt for unconventional choices when the decision domain entails low consequences, whereas such a result does not hold under decision domain of high consequences. The categorization flexibility effects in case of low-consequence decision domain holds true even when consumers are primed to be categorization flexible. Furthermore, with additional adventure priming, consumers show an increased preference for unconventional options even under a decision domain with high consequence. Research limitations/implications This study could not examine real purchase behavior as results are based on cross-sectional, behavioral intention data. In addition, it did not examine the underlying reason for presence of cross-domain categorization flexibility index. Practical implications The results suggest that stimuli may be tailored to consumers in ways that increase the salience and the perceived attractiveness of unconventional choices. Further, data reinforce the notion of cross-categorical interrelations among different domains, which could be leveraged by marketers. Originality/value This study represents the first documentation of the potential ways by which unconventional product choice might be a function of individuals’ categorization flexibility level across different types of decision domains. The findings yield implications that are novel to both categorization and consumer decision-making literature.

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

DATA-DRIVEN DISCOVERY OF MATERIAL STATES IN COMPOSITES UNDER FATIGUE LOADS

2021 ◽  
Author(s):  
MUTHU RAM ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
RASSEL RAIHAN ◽  
KENNETH REIFSNIDER
Keyword(s):  
Domain Knowledge ◽  
Data Science ◽  
Damage Tolerance ◽  
Electrochemical Impedance ◽  
Research Work ◽  
Rate Of Change ◽  
Data Driven ◽  
Develop Model ◽  
Damage Development ◽  
Fatigue Loads

Our community has a widespread knowledge on the damage tolerance and durability of the composites, developed over the past few decades by various experimental and computational efforts. Several methods have been used to understand the damage behavior and henceforth predict the material states such as residual strength (damage tolerance) and life (durability) of these material systems. Electrochemical Impedance Spectroscopy (EIS) and Broadband Dielectric Spectroscopy (BbDS) are such methods, which have been proven to identify the damage states in composites. Our previous work using BbDS method has proven to serve as precursor to identify the damage levels, indicating the beginning of end of life of the material. As a change in the material state variable is triggered by damage development, the rate of change of these states indicates the rate of damage interaction and can effectively predict impending failure. The Data-Driven Discovery of Models (D3M) [1] aims to develop model discovery systems, enabling users with domain knowledge but no data science background to create empirical models of real, complex processes. These D3M methods have been developed severely over the years in various applications and their implementation on real-time prediction for complex parameters such as material states in composites need to be trusted based on physics and domain knowledge. In this research work, we propose the use of data-driven methods combined with BbDS and progressive damage analysis to identify and hence predict material states in composites, subjected to fatigue loads.

Application of a methodological approach to compare ontologies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yudith Cardinale ◽  
Maria Alejandra Cornejo-Lupa ◽  
Alexander Pinto-De la Gala ◽  
Regina Ticona-Herrera
Keyword(s):  
Domain Knowledge ◽  
Design Methodology ◽  
Methodological Approach ◽  
Quality Model ◽  
Specific Domain ◽  
Content Type ◽  
Localization And Mapping ◽  
Golden Standard ◽  
Knowledge Levels ◽  
Practical Implications

Purpose This study aims to the OQuaRE quality model to the developed methodology. Design/methodology/approach Ontologies are formal, well-defined and flexible representations of knowledge related to a specific domain. They provide the base to develop efficient and interoperable solutions. Hence, a proliferation of ontologies in many domains is unleashed. Then, it is necessary to define how to compare such ontologies to decide which one is the most suitable for the specific needs of users/developers. As the emerging development of ontologies, several studies have proposed criteria to evaluate them. Findings In a previous study, the authors propose a methodological process to qualitatively and quantitatively compare ontologies at Lexical, Structural and Domain Knowledge levels, considering correctness and quality perspectives. As the evaluation methods of the proposal are based on a golden-standard, it can be customized to compare ontologies in any domain. Practical implications To show the suitability of the proposal, the authors apply the methodological approach to conduct comparative studies of ontologies in two different domains, one in the robotic area, in particular for the simultaneous localization and mapping (SLAM) problem; and the other one, in the cultural heritage domain. With these cases of study, the authors demonstrate that with this methodological comparative process, we are able to identify the strengths and weaknesses of ontologies, as well as the gaps still needed to fill in the target domains. Originality/value Using these metrics and the quality model from OQuaRE, the authors are incorporating a standard of software engineering at the quality validation into the Semantic Web.

scholarly journals Creative exploration using topic-based bisociative networks

2018 ◽  
Vol 4 ◽  
Author(s):  
Faez Ahmed ◽  
Mark Fuge
Keyword(s):  
Idea Generation ◽  
Information Network ◽  
Large Collection ◽  
Computational Framework ◽  
Conceptual Similarity ◽  
Cross Domain ◽  
Unstructured Text ◽  
Collaborative Community ◽  
Creative Solutions ◽  
Multiple Domains

Bisociative knowledge discovery is an approach that combines elements from two or more ‘incompatible’ domains to generate creative solutions and insight. Inspired by Koestler’s notion of bisociation, in this paper we propose a computational framework for the discovery of new connections between domains to promote creative discovery and inspiration in design. Specifically, we propose using topic models on a large collection of unstructured text ideas from multiple domains to discover creative sources of inspiration. We use these topics to generate a Bisociative Information Network – a graph that captures conceptual similarity between ideas – that helps designers find creative links within that network. Using a dataset of thousands of ideas from OpenIDEO, an online collaborative community, our results show usefulness of representing conceptual bridges through collections of words (topics) in finding cross-domain inspiration. We show that the discovered links between domains, whether presented on their own or via ideas they inspired, are perceived to be more novel and can also be used as creative stimuli for new idea generation.

scholarly journals Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8009
Author(s):  
Abdulmajid Murad ◽  
Frank Alexander Kraemer ◽  
Kerstin Bach ◽  
Gavin Taylor
Keyword(s):  
Deep Learning ◽  
Air Quality ◽  
Probabilistic Models ◽  
Performance Metrics ◽  
Current Data ◽  
Data Driven ◽  
Quality Data ◽  
Empirical Performance ◽  
Adversarial Training ◽  
Scalable Methods

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

scholarly journals A Novel Design Framework for Structures/Materials with Enhanced Mechanical Performance

Materials ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 576 ◽  
Author(s):  
Jie Liu ◽  
Xiaonan Fan ◽  
Guilin Wen ◽  
Qixiang Qing ◽  
Hongxin Wang ◽  
...  
Keyword(s):  
Mechanical Performance ◽  
Design Framework ◽  
Novel Design
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