Design of Question Answering System of Intelligent Customer Service Robot Based on Knowledge Base

2019 ◽  
Vol 09 (11) ◽  
pp. 2098-2104
Author(s):  
婕 陆
Keyword(s):  
Knowledge Base ◽  
Customer Service ◽  
Question Answering ◽  
Service Robot ◽  
Question Answering System

scholarly journals O ONTOLOGY-BASED PARAGRAPH EXTRACTION AND CAUSALITY DETECTION-BASED SIMILARITY FOR ANSWERING WHY-QUESTION

2018 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
A A I N Eka Karyawati
Keyword(s):  
Knowledge Base ◽  
Main Part ◽  
Question Answering ◽  
Document Retrieval ◽  
Domain Ontology ◽  
Typical Problem ◽  
Scoring Method ◽  
Method Performance ◽  
Question Answering System ◽  
Selection For

Paragraph extraction is a main part of an automatic question answering system, especially in answering why-question. It is because the answer of a why-question usually contained in one paragraph instead of one or two sentences. There have been some researches on paragraph extraction approaches, but there are still few studies focusing on involving the domain ontology as a knowledge base. Most of the paragraph extraction studies used keyword-based method with small portion of semantic approaches. Thus, the question answering system faces a typical problem often occuring in keyword-based method that is word mismatches problem. The main contribution of this research is a paragraph scoring method that incorporates the TFIDF-based and causality-detection-based similarity. This research is a part of the ontology-based why-question answering method, where ontology is used as a knowledge base for each steps of the method including indexing, question analyzing, document retrieval, and paragraph extraction/selection. For measuring the method performance, the evaluations were conducted by comparing the proposed method over two baselines methods that did not use causality-detection-based similarity. The proposed method shown improvements over the baseline methods regarding MRR (95%, 0.82-0.42), P@1 (105%, 0.78-0.38), P@5(91%, 0.88-0.46), Precision (95%, 0.80-0.41), and Recall (66%, 0.88-0.53).

scholarly journals An ontology driven question answering system for fatawa retrieval

2021 ◽  
Vol 23 (2) ◽  
pp. 980
Author(s):  
Keltoum Benlaharche ◽  
Zakaria Laboudi ◽  
Nabila Nouaouria ◽  
Djamel Eddine Zegour
Keyword(s):  
Human Resources ◽  
Knowledge Base ◽  
Question Answering ◽  
Islamic Finance ◽  
Case Based Reasoning ◽  
Question Answering System ◽  
Long Time ◽  
Reasoning Mechanism ◽  
Case Based

This work aims to propose a system for the Algerian Fatawa House in orderto facilitate the task of the Expert Mufti who is responsible of giving fatawa for Algerian people inquiries. In fact, as this house is recent and does not have sufficient human resources, it is difficult to satisfy all inquiries coming daily, this leads the askers to wait for a long time before getting answers. The proposed system allows the askers to express concerns they may have. By using a case-based reasoning mechanism combined with ontology domain, the system tries to retrieve similar cases from the knowledge base. In the casewhere the response already exists, the system immediately provides the answer to the askers. Otherwise, an inquery is automatically formulated and sent to the expert Mufti-which is a certified scholar-in order either to validate the generated response by the system or give a new answer. Such a question-answering system may be very helpful for askers to get their answers faster since it allows both the storage of previous <em>fatawas</em> and their retrieval for processing coming inquiries. To validate our proposal, we rely on <em>fatawas</em> concerning the Islamic finance and banking transactions domain. Overall, the results are encouraging and satisfactory.

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Automated System ◽  
Free Form ◽  
Question Answering System ◽  
Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

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