scholarly journals Adaptive Inspirational Design Stimuli: Using Design Output to Computationally Search for Stimuli That Impact Concept Generation

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Kosa Goucher-Lambert ◽  
Joshua T. Gyory ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Semantic Similarity ◽  
Semantic Analysis ◽  
Similarity Measures ◽  
Design Activity ◽  
Concept Generation ◽  
Experimental Conditions ◽  
Final Design ◽  
Design Solutions ◽  
Design Innovation ◽  
The Impact

Abstract Design activity can be supported using inspirational stimuli (e.g., analogies, patents) by helping designers overcome impasses or in generating solutions with more positive characteristics during ideation. Design researchers typically generate inspirational stimuli a priori in order to investigate their impact. However, for a chosen stimulus to possess maximal utility, it should automatically reflect the current and ongoing progress of the designer. In this work, designers receive computationally selected inspirational stimuli midway through an ideation session in response to the contents of their current solution. Sourced from a broad database of related example solutions, the semantic similarity between the content of the current design and concepts within the database determines which potential stimulus is received. Designers receive a particular stimulus based on three experimental conditions: a semantically near stimulus, a semantically far stimulus, or no stimulus (control). Results indicate that adaptive inspirational stimuli can be determined using latent semantic analysis (LSA) and that semantic similarity measures are a promising approach for real-time monitoring of the design process. The ability to achieve differentiable near versus far stimuli was validated using both semantic cosine similarity values and participant self-response ratings. As a further contribution, this work also explores the impact of different types of adaptive inspirational stimuli on design outcomes using a newly introduced “design innovation” measure. The design innovation measure mathematically captures the overall goodness of a design concept by uniquely combining expert ratings across easier to evaluate subdimensions of feasibility, usefulness, and novelty. While results demonstrate that near inspirational stimuli increase the feasibility of design solutions, they also show the significant impact of the overall inspirational stimulus innovativeness on final design outcomes. In fact, participants are more likely to generate innovative final design solutions when given innovative inspirational stimuli, regardless of their experimental condition.

Computationally Derived Adaptive Inspirational Stimuli for Real-Time Design Support During Concept Generation

2019 ◽  
Author(s):  
Kosa Goucher-Lambert ◽  
Joshua T. Gyory ◽  
Kenneth Kotovsky ◽  
Jonathan Cagan
Keyword(s):  
Real Time ◽  
Semantic Similarity ◽  
Semantic Analysis ◽  
A Priori ◽  
Similarity Measures ◽  
Design Activity ◽  
Concept Generation ◽  
Experimental Conditions ◽  
Final Design ◽  
The Impact

Abstract Design activity can be supported using inspirational stimuli (e.g., analogies, patents, etc.), by helping designers overcome impasses or in generating solutions with more positive characteristics during ideation. Design researchers typically generate inspirational stimuli a priori in order to investigate their impact. However, for a chosen stimulus to possess maximal utility, it should automatically reflect the current and ongoing progress of the designer. In this work, designers receive computationally selected inspirational stimuli midway through an ideation session in response to the state of their current solution. Sourced from a broad database of related example solutions, the semantic similarity between the content of the current design and concepts within the database determine which potential stimulus is received. Designers receive a particular stimulus based on three experimental conditions: a semantically near stimulus, a semantically far stimulus, or no stimulus (control). Results indicate that adaptive inspirational stimuli can be determined using Latent Semantic Analysis (LSA) and that semantic similarity measures are a promising approach for real-time monitoring of the design process. The ability to achieve differentiable near vs. far stimuli was validated using both semantic cosine similarity values and participant self-response ratings. As a further contribution, this work also explores the impact of different types of adaptive inspirational stimuli on design outcomes. Here, near inspirational stimuli increase the feasibility of design solutions. Results also demonstrate the significant impact of the overall inspirational stimulus innovativeness on final design outcomes, which may be greater than differences across individual sub-dimensions.

Port-Based Ontology Semantic Similarities for Module Concept Creation

2009 ◽  
Author(s):  
Dongxing Cao ◽  
Karthik Ramani ◽  
Ming Wang Fu ◽  
Runli Zhang
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Similarity Measures ◽  
Concept Generation ◽  
Syntactic Structures ◽  
Ontology Language ◽  
Modular Concept ◽  
Semantic Link ◽  
Functional Blocks ◽  
Physical Components

The modularity indicates a one-to-one mapping between functional concepts and physical components. It can allow us to generate more product varieties at lower costs. Functional concepts can be described by precise syntactic structures with functional terms. Different semantic measures can be used to evaluate the strength of the semantic link between two functional concepts from port ontology. In this paper, different methods of modularity based on ontology are first investigated. Secondly, the primitive concepts are presented based on port ontology by using natural language, and then their semantic synthesis is used to describe component ontology. The taxonomy of port-based ontology are built to map the component connections and interactions in order to build functional blocks. Next, propose an approach to computing semantic similarity by mapping terms to functional ontology and by examining their relationships based on port ontology language. Furthermore, several modules are partitioned on the basis of similarity measures. The process of module construction is described and its elements are related to the similarity values between concepts. Finally, a case is studied to show the efficiency of port ontology semantic similarity for modular concept generation.

scholarly journals Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Gene Ontology ◽  
Information Content ◽  
Semantic Similarity ◽  
Experimental Evaluation ◽  
Similarity Measures ◽  
Mathematical Framework ◽  
Unified Framework ◽  
The Impact ◽  
Unified Description ◽  
Similarity Scores

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term’s specificity in the GO DAG.

Impact of low-confidence interactions on computational identification of protein complexes

2020 ◽  
Vol 18 (04) ◽  
pp. 2050025
Author(s):  
Madhusudan Paul ◽  
Ashish Anand
Keyword(s):  
Semantic Similarity ◽  
Protein Complexes ◽  
Similarity Measures ◽  
Disease Diagnosis ◽  
False Positives ◽  
Detection Methods ◽  
Computational Techniques ◽  
Ppi Networks ◽  
Complex Detection ◽  
The Impact

Protein complexes are the cornerstones of most of the biological processes. Identifying protein complexes is crucial in understanding the principles of cellular organization with several important applications, including in disease diagnosis. Several computational techniques have been developed to identify protein complexes from protein–protein interaction (PPI) data (equivalently, from PPI networks). These PPI data have a significant amount of false positives, which is a bottleneck in identifying protein complexes correctly. Gene ontology (GO)-based semantic similarity measures can be used to assign a confidence score to PPIs. Consequently, low-confidence PPIs are highly likely to be false positives. In this paper, we systematically study the impact of low-confidence PPIs on the performance of complex detection methods using GO-based semantic similarity measures. We consider five state-of-the-art complex detection algorithms and nine GO-based similarity measures in the evaluation. We find that each complex detection algorithm significantly improves its performance after the filtration of low-similarity scored PPIs. It is also observed that the percentage improvement and the filtration percentage (of low-confidence PPIs) are highly correlated.

scholarly journals Semantic Measures for Enhancing Creativity in Design Education

2019 ◽  
Vol 1 (1) ◽  
pp. 369-378
Author(s):  
Georgi V. Georgiev ◽  
Hernan Casakin
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Significant Relationship ◽  
Design Education ◽  
Semantic Analysis ◽  
Semantic Content ◽  
Design Studio ◽  
Design Activity ◽  
Design Creativity ◽  
Verbal Data

AbstractAnalysing verbal data produced during the design activity is helpful to gain a better understanding of design creativity. To understand exchange of information in terms of creative outcomes, a semantic analysis approach was used to measure the semantic content of communications between students and teachers. The goal was to use this tool to analyse design conversations, and to investigate their relation to design creativity, assessed in terms of originality, usability, feasibility, aesthetics, elaboration, overall value and overall creativity. Abstraction, Polysemy, Information Content and Semantic Similarity were employed to explore 35 design conversations from the DTRS10 dataset. Main findings suggest that a significant relationship exists between Information Content and Originality, and between Information Content and Overall creativity of the produced design outcomes. Significant relations were also found between Abstraction, Polysemy, Information Content, and Feasibility, as well as between Semantic Similarity and Overall Value of the outcomes. Implications for the use of semantic measures for encouraging creativity in the design studio are discussed.

scholarly journals Evaluating Semantic Similarity Methods for Comparison of Text-derived Phenotype Profiles

2021 ◽  
Author(s):  
Luke T Slater ◽  
Sophie Russell ◽  
Silver Makepeace ◽  
Alexander Carberry ◽  
Andreas Karwath ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Semantic Similarity ◽  
Large Body ◽  
Similarity Measures ◽  
Primary Diagnosis ◽  
Experimental Conditions ◽  
Clinical Text ◽  
Automated Coding ◽  
Protein Interaction Prediction ◽  
Mimic Iii

Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance 'patient-like me' analyses, automated coding, differential diagnosis, and outcome prediction, by leveraging the wealth of background knowledge provided by biomedical ontologies. While a large body of work exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. Moreover, there are no experimental explorations of optimal parameters or methods in the area. In this work, we develop a reproducible platform for benchmarking experimental conditions for patient phentoype similarity. Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from text narrative associated with admissions in MIMIC-III. In doing this, we identify and interpret the performance of a large number of semantic similarity measures for this task, and provide a basis for further research on related tasks in the area.

scholarly journals English Semantic Feature Production Norms: An Extended Database of 4,436 Concepts

2018 ◽  
Author(s):  
Erin Michelle Buchanan ◽  
Kathrene D Valentine ◽  
Nicholas Maxwell
Keyword(s):  
Semantic Similarity ◽  
Semantic Priming ◽  
Experimental Studies ◽  
Similarity Measures ◽  
Semantic Feature ◽  
Easy Access ◽  
Limiting Factor ◽  
Word Form ◽  
Feature Overlap ◽  
The Impact

A limiting factor in understanding memory and language is often the availability of large numbers of stimuli to use and explore in experimental studies. In this study, we expand on three previous databases of concepts to over 4,000 words including nouns, verbs, adjectives, and other parts of speech. Participants in the study were asked to provide lists of features for each concept presented (a semantic feature production task), which were combined with previous research in this area. These feature lists for each concept were then coded into their root word form and affixes (i.e., *cat* and *s* for *cats*) to explore the impact of word form on semantic similarity measures, which are often calculated by comparing concept feature lists (feature overlap). All concept features, coding, and calculated similarity information is provided in a searchable database for easy access and utilization for future researchers when designing experiments that use word stimuli. The final database of word pairs was combined with the Semantic Priming Project to examine the relation of semantic similarity statistics on semantic priming in tandem with other psycholinguistic variables.

scholarly journals Glucuronides Hydrolysis by Intestinal Microbial β-Glucuronidases (GUS) Is Affected by Sampling, Enzyme Preparation, Buffer pH, and Species

Pharmaceutics ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1043
Author(s):  
Christabel Ebuzoeme ◽  
Imoh Etim ◽  
Autumn Ikimi ◽  
Jamie Song ◽  
Ting Du ◽  
...  
Keyword(s):  
Enzyme Preparation ◽  
Reaction System ◽  
Reaction Rates ◽  
Experimental Conditions ◽  
Model Animal ◽  
Fresh Feces ◽  
Time Buffer ◽  
Exogenous Compounds ◽  
Activity Loss ◽  
The Impact

Glucuronides hydrolysis by intestinal microbial β-Glucuronidases (GUS) is an important procedure for many endogenous and exogenous compounds. The purpose of this study is to determine the impact of experimental conditions on glucuronide hydrolysis by intestinal microbial GUS. Standard probe 4-Nitrophenyl β-D-glucopyranoside (pNPG) and a natural glucuronide wogonoside were used as the model compounds. Feces collection time, buffer conditions, interindividual, and species variations were evaluated by incubating the substrates with enzymes. The relative reaction activity of pNPG, reaction rates, and reaction kinetics for wogonoside were calculated. Fresh feces showed the highest hydrolysis activities. Sonication increased total protein yield during enzyme preparation. The pH of the reaction system increased the activity in 0.69–1.32-fold, 2.9–12.9-fold, and 0.28–1.56-fold for mouse, rat, and human at three different concentrations of wogonoside, respectively. The Vmax for wogonoside hydrolysis was 2.37 ± 0.06, 4.48 ± 0.11, and 5.17 ± 0.16 μmol/min/mg and Km was 6.51 ± 0.71, 3.04 ± 0.34, and 0.34 ± 0.047 μM for mouse, rat, and human, respectively. The inter-individual difference was significant (4–6-fold) using inbred rats as the model animal. Fresh feces should be used to avoid activity loss and sonication should be utilized in enzyme preparation to increase hydrolysis activity. The buffer pH should be appropriate according to the species. Inter-individual and species variations were significant.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

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