Knowledge base construction, knowledge representation in neo4j and implementation UI in voice user interfaced intelligent interview chatbot (Record no. 6118)

MARC details
000 -LEADER
fixed length control field 02428nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122841.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180605b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Lemiya Reem K (91616010)
9 (RLIN) 14320
245 ## - TITLE STATEMENT
Title Knowledge base construction, knowledge representation in neo4j and implementation UI in voice user interfaced intelligent interview chatbot
300 ## - PHYSICAL DESCRIPTION
Extent MSC MI 2016-2018
500 ## - GENERAL NOTE
General note <br/>A chatbot is a program that communicates with human. It is powered by Machine Learning, <br/>more commonly it is driven using intelligent rules. The term chatbot is synonymous with text<br/>conversation but is growing quickly through voice communication. Chat interface may be<br/>conversational or voice user interfaced chatbot. Natural language processing technology was<br/>good enough to understand all kinds of user requests.NLP is a way for computers to analyze,<br/>understand and derive meaning from human language in a smart and useful way .A<br/>conversational UI gives the privilege of interacting with the computer. It allows a user to tell the<br/>computer what to do. It takes two forms i.e., voice assistant that allows you to talk and chatbots<br/>that allow you to type. First, conversational interfaces are text based dialog systems for question<br/>answering and conversational chatbots. Then the speech based dialog systems began to appear. <br/> I-aBro is a Voice user interfaced neo4j data-driven intelligent interviewer<br/>chatbot. I-aBro uses both Rule based and Generative based chatbot. One function is based on the<br/>set of rules and the other more advanced version uses Machine Learning. It generates questions<br/>automatically. Named Entity Recognition technology is used to generate the question, which<br/>made more reliable interview approach. Question Generation is fully relying on user inputs that<br/>mean question is generated by the previous response of the user. Scoring and evaluation is the<br/>another main advantage of the I-aBro system. Chat-log is converted to PDF format and it sent via<br/>mail to the close group of experts for evaluation is one method. And another method is<br/>evaluation by system itself. This approach is based on the syntactic similarity of candidate<br/>response and knowledge present in knowledge base. <br/> <br/>
502 ## - DISSERTATION NOTE
Degree type MSC MI
Name of granting institution 2016-2018
Year degree granted INT
-- Dr. Asharaf S
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element CHATBOT
9 (RLIN) 14321
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element WEB UI
9 (RLIN) 14322
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element NATURAL LANGUAGE PROCESSING
9 (RLIN) 14323
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
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Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   05/06/2018   R-1412 05/06/2018 05/06/2018 Project Reports