Translating Terms of the Functional Basis Into Biologically Meaningful Keywords

2008 ◽  
Author(s):  
Hyunmin Cheong ◽  
L. H. Shu ◽  
Robert B. Stone ◽  
Daniel A. McAdams
Keyword(s):  
Natural Language ◽  
Engineering Design ◽  
Semantic Relations ◽  
Biological Information ◽  
Functional Basis ◽  
Design Students ◽  
Design Projects ◽  
Systematic Identification ◽  
Excellent Source ◽  
Selection Of

Biology has long been recognized as an excellent source of analogies and stimuli for engineering design. Previous work focused on the systematic identification of relevant biological analogies by searching for instances of functional keywords in biological information in natural language format. This past work revealed that engineering keywords couldn’t always be used to identify the most relevant biological analogies, as the vocabularies between biology and engineering are sufficiently distinct. Therefore, a method of identifying biologically meaningful keywords that correspond to engineering keywords was developed. Here, we apply and refine this method by generating biologically meaningful keywords for the terms of the Functional Basis, which is widely accepted as a standardized representation of the functionality of engineering products. We present insights gained on the selection of biologically meaningful keywords for the function sets based on semantic relations. We then observe the use of our keywords by providing 4th year undergraduate design students with the biologically meaningful keywords that are related to the desired functions of their design projects.

scholarly journals Active Learning Classes (in KOSEN Colleges of Japan) Using ICT and Tools for Obtaining Biological Information to Enhance the Creativity of Engineering Design Students

2020 ◽  
Vol 176 ◽  
pp. 2108-2116
Author(s):  
Nobuyuki Ogawa ◽  
Hideyuki Kanematsu ◽  
Dana M. Barry ◽  
Tatsuya Shirai ◽  
Masashi Kawaguchi ◽  
...  
Keyword(s):  
Active Learning ◽  
Engineering Design ◽  
Biological Information ◽  
Design Students

Biologically Meaningful Keywords for Functional Terms of the Functional Basis

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
H. Cheong ◽  
I. Chiu ◽  
L. H. Shu ◽  
R. B. Stone ◽  
D. A. McAdams
Keyword(s):  
Engineering Students ◽  
Biological Information ◽  
Retrieval Algorithm ◽  
Engineering Product ◽  
Functional Basis ◽  
Biological Phenomena ◽  
Preliminary Validation ◽  
Validation Experiment ◽  
Systematic Identification ◽  
High Degree

Biology is recognized as an excellent source of analogies and stimuli for engineering design. Previous work focused on the systematic identification of relevant biological analogies by searching for instances of functional keywords in biological information in natural-language format. This past work revealed that engineering keywords could not always be used to identify the most relevant biological analogies as the vocabularies between biology and engineering are sufficiently distinct. Therefore, a retrieval algorithm was developed to identify potential biologically meaningful keywords that are more effective in searching biological text than corresponding engineering keywords. In our current work, we applied and refined the retrieval algorithm to translate functional terms of the functional basis into biologically meaningful keywords. The functional basis is widely accepted as a standardized representation of engineering product functionality. Therefore, our keywords could serve as a thesaurus for engineers to find biological analogies relevant to their design problems. We also describe specific semantic relationships that can be used to identify biologically meaningful keywords in excerpts describing biological phenomena. These semantic relations were applied as criteria to identify the most useful biologically meaningful keywords. Through a preliminary validation experiment, we observed that different translators were able to apply the criteria to identify biologically meaningful keywords with a high degree of agreement to those identified by the authors. In addition, we describe how fourth-year undergraduate mechanical engineering students used the biologically meaningful keywords to develop concepts for their design projects.

Fast Neural Network Engine for Natural Science Language Processing: A Drug-Search Case.

2020 ◽  
Author(s):  
Vadim V. Korolev ◽  
Artem Mitrofanov ◽  
Kirill Karpov ◽  
Valery Tkachenko
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Natural Science ◽  
Therapeutic Agent ◽  
Semantic Relations ◽  
Chemical Data ◽  
Processing Methods ◽  
Modern Natural

The main advantage of modern natural language processing methods is a possibility to turn an amorphous human-readable task into a strict mathematic form. That allows to extract chemical data and insights from articles and to find new semantic relations. We propose a universal engine for processing chemical and biological texts. We successfully tested it on various use-cases and applied to a case of searching a therapeutic agent for a COVID-19 disease by analyzing PubMed archive.

scholarly journals DRr-Net: Dynamic Re-Read Network for Sentence Semantic Matching

2019 ◽  
Vol 33 ◽  
pp. 7442-7449 ◽  
Author(s):  
Kun Zhang ◽  
Guangyi Lv ◽  
Linyuan Wang ◽  
Le Wu ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Natural Language ◽  
Small Region ◽  
Psychological Research ◽  
Attention Mechanism ◽  
Semantic Relations ◽  
Semantic Matching ◽  
Close Attention ◽  
Original Sentence ◽  
Benchmark Datasets ◽  
Sentence Matching

Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks such as Natural Language Inference (NLI) and Paraphrase Identification (PI). Among all matching methods, attention mechanism plays an important role in capturing the semantic relations and properly aligning the elements of two sentences. Previous methods utilized attention mechanism to select important parts of sentences at one time. However, the important parts of the sentence during semantic matching are dynamically changing with the degree of sentence understanding. Selecting the important parts at one time may be insufficient for semantic understanding. To this end, we propose a Dynamic Re-read Network (DRr-Net) approach for sentence semantic matching, which is able to pay close attention to a small region of sentences at each step and re-read the important words for better sentence semantic understanding. To be specific, we first employ Attention Stack-GRU (ASG) unit to model the original sentence repeatedly and preserve all the information from bottom-most word embedding input to up-most recurrent output. Second, we utilize Dynamic Re-read (DRr) unit to pay close attention to one important word at one time with the consideration of learned information and re-read the important words for better sentence semantic understanding. Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. In addition, it is very interesting that some of finding in our experiments are consistent with the findings of psychological research.

Evaluating the Effectiveness of Problem Formulation and Ideation Skills Learned Throughout an Engineering Design Course

2015 ◽  
Author(s):  
Mahmoud Dinar ◽  
Yong-Seok Park ◽  
Jami J. Shah
Keyword(s):  
Engineering Design ◽  
Objective Assessment ◽  
Problem Formulation ◽  
Engineering Product ◽  
Design Problems ◽  
Web Based ◽  
Final Project ◽  
Design Projects ◽  
Design Skills ◽  
Long Course

Conventional syllabi of engineering design courses either do not pay enough attention to conceptual design skills, or they lack an objective assessment of those skills to show students’ progress. During a semester-long course of advanced engineering product design, we assigned three major design projects to twenty five students. For each project we asked them to formulate the problems in the Problem Formulator web-based testbed. In addition, we collected sketches for all three design problems, feasibility analyses for the last two, and a working prototype for the final project. We report the students’ problem formulation and ideation in terms of a set of nine problem formulation characteristics and ASU’s ideation effectiveness metrics respectively. We discuss the limitations that the choice of the design problems caused, and how the progress of a class of students during a semester-long design course resulted in a convergence in sets of metrics that we have defined to characterize problem formulation and ideation. We also review the results of students of a similar course which we reported last year in order to find common trends.

scholarly journals Comparison of Templates with Word2vec in Finding Semantic Relations Between Words

2018 ◽  
pp. 13-17
Author(s):  
Kaan Ant ◽  
Ugur Sogukpinar ◽  
Mehmet Fatif Amasyali
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Relations ◽  
Template Method ◽  
Semantic Relationships ◽  
Semantic Spaces

The use of databases those containing semantic relationships between words is becoming increasingly widespread in order to make natural language processing work more effective. Instead of the word-bag approach, the suggested semantic spaces give the distances between words, but they do not express the relation types. In this study, it is shown how semantic spaces can be used to find the type of relationship and it is compared with the template method. According to the results obtained on a very large scale, while is_a and opposite are more successful for semantic spaces for relations, the approach of templates is more successful in the relation types at_location, made_of and non relational.

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