Addressing ontology-based question answering with collections of user queries

2009 ◽  
Vol 45 (2) ◽  
pp. 175-188 ◽  
Author(s):  
Óscar Ferrández ◽  
Rubén Izquierdo ◽  
Sergio Ferrández ◽  
José Luis Vicedo
Keyword(s):  
Question Answering ◽  
User Queries

SINA: Semantic Interpretation of User Queries for Question Answering on Interlinked Data

2015 ◽  
Author(s):  
Saeedeh Shekarpour ◽  
Edgard Marx ◽  
Axel-Cyrille Ngonga Ngomo ◽  
SSren Auer
Keyword(s):  
Question Answering ◽  
Semantic Interpretation ◽  
User Queries

SINA: Semantic interpretation of user queries for question answering on interlinked data

2015 ◽  
Vol 30 ◽  
pp. 39-51 ◽  
Author(s):  
Saeedeh Shekarpour ◽  
Edgard Marx ◽  
Axel-Cyrille Ngonga Ngomo ◽  
Sören Auer
Keyword(s):  
Question Answering ◽  
Semantic Interpretation ◽  
User Queries

scholarly journals Natural Language Query Based Question Answering System

2020 ◽  
Vol 9 (3) ◽  
pp. 2977-2981
Keyword(s):  
Natural Language ◽  
Comparative Study ◽  
Question Answering ◽  
Question Answering System ◽  
Natural Language Query ◽  
User Queries

Development of natural language query based automatic question answering system is in huge demand these days and is a rapidly growing field. It is considered to be the most powerful application for answering different user queries not only on limited domains but also in multi domain environments. In this work, a natural language query based intelligible question answering system is presented that extracts relevant answers from the documents and present the answer in a pre-defined format to the user. A comparative study of the presented model with the traditional techniques is also presented.

Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 67-78
Author(s):  
Guy Barash ◽  
Mauricio Castillo-Effen ◽  
Niyati Chhaya ◽  
Peter Clark ◽  
Huáscar Espinoza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cyber Security ◽  
Question Answering ◽  
Intent Recognition ◽  
Affective Content ◽  
Learning Plan ◽  
Dialog System ◽  
Affective Content Analysis ◽  
Games And Simulations

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held.

Automated question answering in Webclopedia

2002 ◽  
Author(s):  
Ulf Hermjakob ◽  
Eduard Hovy ◽  
Chin-Yew Lin
Keyword(s):  
Question Answering

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

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