Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications

Chandrashekara, Arjun Ankathatti; Talluri, Radha Krishna Murthy; Sivarathri, Sai Swathi; Mitra, R.; Calyam, Prasad; Kee, Kerk F.; Nair, Satish S.

doi:10.1109/bigdata.2018.8622046

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…Herein, we detail the working of the Vidura Chatbot Assistant to guide data consumers in easy navigation of the KnowCOVID-19 system functionality. Our Vidura implementation, adopted from [29], uses Google Dialogflow which is a conversational intelligence service that uses natural language processing techniques to find the intentions in the user queries. Through Google Dialogflow, we are able to develop intelligent and responsive agent interactions in our Vidura implementation that hold definitions for various functions involving 'intents', and 'fulfillments' based on user input.…”

Section: B Vidura Chatbot Assistantmentioning

confidence: 99%

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

et al. 2021

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The rapid growth of COVID-19 publications has driven clinical researchers and healthcare professionals in pursuit to reduce the knowledge gap on reliable information for effective pandemic solutions. The manual task of retrieving high-quality publications based on the evidence pyramid levels, however, presents a major bottleneck in researchers' workflows. In this paper, we propose an "evidencebased" recommender system namely, KnowCOVID-19 that utilizes an edge computing service to integrate recommender modules for data analytics using end-user thin-clients. The edge computing service features chatbot-based web interface that handles a given COVID-19 publication dataset using two recommender system modules: (i) evidence-based filtering that observes domain specific topics across the literature and classifies the filtered information according to a clinical category, and (ii) social filtering that allows diverse experts with similar objectives to collaborate via a "social plane" to jointly find answers to critical clinical questions to fight the pandemic. We compare the Domain-specific Topic Model (DSTM) used in our evidence-based filtering with state-of-the-art models considering the CORD-19 dataset (a COVID-19 publication archive) and show improved generalization effectiveness as well as knowledge pattern query effectiveness. In addition, we conduct a comparison study between a manual literature review process and the KnowCOVID-19 augmented process, and evaluate the benefits of our information retrieval techniques over important queries provided by COVID-19 clinical experts.

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Section: B Vidura Chatbot Assistantmentioning

confidence: 99%

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

et al. 2021

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“…This may also be implied by the discussions of training and building relationships with trust, especially during the implementation phase. However, we can see the use of community forums, 23 chatbots 31 to aid in user support. In addition, as we write this article during the COVID‐19 crisis, we recognize that tools designed for (or that can be repurposed to address aspects of) a major crisis will also receive a sudden surge in awareness and publicity, because of the crisis.…”

Section: Conclusion Limitations and Implicationsmentioning

confidence: 99%

If you build it, promote it, and they trust you, then they will come: Diffusion strategies for science gateways and cyberinfrastructure adoption to harness big data in the science, technology, engineering, and mathematics (STEM) community

Kee

Jitkajornwanich

2021

Concurrency and Computation

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In the big data era, for science gateways (SG) and cyberinfrastructure (CI) projects to have the greatest impacts, they need to be widely adopted in the scientific community. However, diffusion activities, or activities aimed to spread SG/CI in the science, technology, engineering, and mathematics community, are often an afterthought in projects. We warn against the fallacy of "If You Build It, They Will Come." Projects could be intentional in promoting tool adoption. Based on an analysis of 83 interviews with 66 administrators, developers, scientists/users, and outreach educators of SG/CI, we identified seven external communication practices-raising awareness, personalizing demonstrations, providing online and offline training, networking with the community, building relationships with trust, stimulating word-of-mouth persuasion, and keeping reliable documentation. With these strategies, we revised the pop culture line to "If You Build It, Promote It, and They Trust You, Then They Will Come." We also observed the beliefs that external communication is mainly necessary when seeking continuous funding, and it belongs to the skillset of nontechnical staff. These two beliefs may explain why external communication is underemphasized in many SG/CI projects. The article serves as evidence to justify a bigger budget in funding proposals for diffusion strategies to increase adoption and broader impacts.

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“…In e-learning platforms, the user proficiency is one of the most significant factors that influences the absorption of learning content. 26 In this work, a questionnaire related to domain knowledge is provided to the student to assess his/her knowledge level and a Mamdani approach 27 is used to decide how much content should be presented to a student according to his/her intellectual level. We also demonstrate a conversational agent acting as a recommender.…”

Section: User Profiling and Recommender Systemmentioning

confidence: 99%

“…Throughout a user's workflow on our system, the user interacts with a context‐aware chatbot, which is embedded within OnTimeRecommend and provides guided user interface, step‐by‐step navigational support, and generates distinct responses for users based on user's proficiency and intent. By utilizing concept of our previous work 27 to determine user's proficiency, we developed a questionnaire related to HPC/CI in the neuroscience and bioinformatics science domains to evaluate the user's overall proficiency. For this purpose, we used many‐valued logic in the fuzzy approach 41 .…”

Section: The Ontimerecommend Systemmentioning

confidence: 99%

“…Jupyter Notebooks 44 are shared documents that contain computer code, equations, and rich text elements, which can be run to perform data analysis and visualization. Our Jupyter Notebook Recommender 27 takes as input of a list of keywords that a user provides, and searches through all the notebooks in a repository to extract the visible text from it. The Jupyter Notebook Recommender uses an embedded Whoosh Python module to index the notebook documents in the database, and then queries the index by using the user‐provided input parameters.…”

Section: Recommender Modulesmentioning

confidence: 99%

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Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

Vekaria

Calyam

Sivarathri

et al. 2020

Concurrency and Computation

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Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, data sets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task.To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). K E Y W O R D Schatbot-guided user interface, knowledge discovery, microservices, recommender system, science gateway INTRODUCTIONRecent science and engineering research tasks are increasingly becoming data-intensive and thus relying on workflows to automate integration and analysis of voluminous data to test hypotheses. For example, research and training in neural science and engineering increasingly deal with diverse and voluminous multiparameter data, 1 posing unique challenges outlined in an NSF iNeuro report 2 as limited access to: multisomics data archives, 3 heterogeneous software 4 and computing resources (Neuroscience Gateway, 5 Amazon Web Services [AWS]), and multisite interdisciplinary expertise (e.g., engineering, biology and psychology). Existing distributed high-performance computing resources (HPC) and other cyberinfrastructure (CI) tools for data management support the related data analysis and visualization capabilities. However, to fully utilize such capabilities, neuroscientists (often with limited CI skills) are required to take valuable time away from the focus of knowledge discovery in neuroscience, in order to learn about how to use the various technologies.

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Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications

Cited by 13 publications

References 8 publications

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

Evidence-Based Recommender System for a COVID-19 Publication Analytics Service

If you build it, promote it, and they trust you, then they will come: Diffusion strategies for science gateways and cyberinfrastructure adoption to harness big data in the science, technology, engineering, and mathematics (STEM) community

Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

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