EVOLUTIVE CONTENT-BASED SEARCH SYSTEM - Semantic Search System based on Case-based-Reasoning and Ontology Enrichment

2010 ◽  
Keyword(s):  
Semantic Search ◽  
Case Based Reasoning ◽  
Search System ◽  
Case Based

scholarly journals A Semi-Automatic Semantic Annotation and Authoring Tool for a Library Help Desk Service

2011 ◽  
pp. 100-114 ◽  
Author(s):  
Antti Vehviläinen ◽  
Eero Hyvönen ◽  
Olli Alm
Keyword(s):  
Information Needs ◽  
Semantic Annotation ◽  
Real Life ◽  
Semantic Search ◽  
Data Sources ◽  
Case Based Reasoning ◽  
Help Desk ◽  
Natural Language Text ◽  
Language Text ◽  
Case Based

This chapter discusses how knowledge technologies can be utilized in creating help desk services on the Semantic Web. To ease the content indexer’s work, we propose semi-automatic semantic annotation of natural language text for annotating question-answer (QA) pairs, and case-based reasoning techniques for finding similar questions. To provide answers matching the content indexer’s and end-user’s information needs, methods for combining case-based reasoning with semantic search, linking, and authoring are proposed. We integrate different data sources by using large ontologies. Techniques to utilize these sources in authoring answers are suggested. A prototype implementation of a real life ontology-based help desk application, based on an existing national library help desk service in Finland, is presented as a proof of concept.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

Feature level intentions based product recommendations with case-based reasoning

2018 ◽  
Vol 6 (1) ◽  
pp. 266-274
Author(s):  
D. Teja Santosh ◽  
◽  
K.C. Ravi Kumar ◽  
P. Chiranjeevi ◽  
◽  
...  
Keyword(s):  
Case Based Reasoning ◽  
Product Recommendations ◽  
Case Based
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