scholarly journals Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Miaoyuan Shi
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Intelligent Manufacturing ◽  
Focus Of Attention ◽  
Knowledge Graph ◽  
Single Entity ◽  
Question And Answer ◽  
Natural Language Query ◽  
The One ◽  
Answering Questions

With the development of deep learning and its wide application in the field of natural language, the question and answer research of knowledge graph based on deep learning has gradually become the focus of attention. After that, the natural language query is converted into a structured query sentence to identify the entities and attributes in the user’s natural language query and the specified entities and attributes are used to retrieve answers to the knowledge graph. Using the advantage of deep learning in capturing sentence information, it incorporates the attention mechanism to obtain the semantic vector of the relevant attributes in the query and uses the parameter sharing mechanism to insert candidate attributes into the triple in the same model to obtain the semantic vector of typical candidates. The experiment measured that under the 100,000 RDF dataset, the single entity query of the MIQE model does not exceed 3 seconds, and the connection query does not exceed 5 seconds. Under the one-million RDF dataset, the single entity query of the MIQE model does not exceed 8 seconds, and the connection query will not be more than 10 seconds. Experimental data show that the system of knowledge-answering questions of engineering of intelligent construction based on deep learning has good horizontal scalability.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Evaluating search features of Google Knowledge Graph and Bing Satori

2015 ◽  
Vol 39 (2) ◽  
pp. 197-213 ◽  
Author(s):  
Ahmet Uyar ◽  
Farouk Musa Aliyu
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Search Engines ◽  
Web Search ◽  
Semantic Search ◽  
Knowledge Graph ◽  
Content Type ◽  
Query Interfaces ◽  
Natural Language Query ◽  
Web Search Engines

Purpose – The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces. Design/methodology/approach – The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography. Findings – The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms. Research limitations/implications – Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research. Practical implications – The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces. Originality/value – As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.

scholarly journals Identifikasi Chatbot dalam Meningkatkan Pelayanan Online Menggunakan Metode Natural Language Processing

2021 ◽  
pp. 142-147
Author(s):  
M Muliyono ◽  
S Sumijan
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Messages ◽  
Processing Stage ◽  
Good Level ◽  
Question And Answer ◽  
West Sumatra ◽  
Answering Questions

Chatbot is a software with artificial intelligence that can imitate human conversations through text messages or voice messages. This chatbot can convey information, according to the knowledge that has been given previously. Helping the limitations of the academic section in answering questions posed by students. The method in this study was sourced from a questionnaire distributed to students at the Muhammadiyah University of West Sumatra. Based on the analysis of the questionnaire, there are 40 questions that are often asked by students to the academic section. Then it is processed using Natural Language Processing (NLP). Natural Language Processing is a branch of science from artificial intelligence that is able to study communication between humans and computers through natural language. The processing stage is to identify the intent, process the input and display the results according to the input. The results of the test using a questionnaire addressed to 227 students got a score of 3,55 with a very good predicate. Then do the test using 40 question and answer data. So, obtained 37 appropriate answers and 3 answers that are not in accordance with the percentage of answer accuracy generated from the chatbot is 92.5 percent. The results of this test have been able to respond to the questions asked by students. This chatbot can make it easier for students to get information with a very good level of accuracy

Graph and Neural Network-Based Intelligent Conversation System

2019 ◽  
pp. 339-357 ◽  
Author(s):  
Anuja Arora ◽  
Aman Srivastava ◽  
Shivam Bansal
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
System Approach ◽  
Domain Knowledge ◽  
Research Work ◽  
Graph Model ◽  
Machine Intelligence ◽  
Knowledge Graph ◽  
Feature Engineering ◽  
Graph Based Model

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.

Developing a Vietnamese Tourism Question Answering System Using Knowledge Graph and Deep Learning

2021 ◽  
Vol 20 (5) ◽  
pp. 1-18
Author(s):  
Phuc Do ◽  
Truong H. V. Phan ◽  
Brij B. Gupta
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
Question Answering ◽  
Knowledge Graph ◽  
Test Set ◽  
Tourist Attractions ◽  
Question Answering System ◽  
Tourism Websites

In recent years, Question Answering (QA) systems have increasingly become very popular in many sectors. This study aims to use a knowledge graph and deep learning to develop a QA system for tourism in Vietnam. First, the QA system replies to a user's question about a place in Vietnam. Then, the QA describes it in detail such as when the place was discovered, why the place's name was called like that, and so on. Finally, the system recommends some related tourist attractions to users. Meanwhile, deep learning is used to solve a simple natural language answer, and a knowledge graph is used to infer a natural language answering list related to entities in the question. The study experiments on a manual dataset collected from Vietnamese tourism websites. As a result, the QA system combining the two above approaches provides more information than other systems have done before. Besides that, the system gets 0.83 F1, 0.87 precision on the test set.

A System for Natural Language Interaction With the Heterogeneous Information Network

2019 ◽  
pp. 271-301 ◽  
Author(s):  
Phuc Do
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Training Data ◽  
Knowledge Graph ◽  
Data Set ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Natural Language Query ◽  
Dependency Parser ◽  
Natural Language Interaction

In this chapter, the authors present their system, which can use natural language query to interact with heterogeneous information networks (HIN). This chapter proposes a solution combining the GraphFrames, recurrent neural network (RNN) long short-term memory (LSTM), and dependency relation of question for generating, training, understanding the question-answer pairs and selecting the best match answer for this question. The RNN-LSTM is used to generate the answer from the facts of knowledge graph. The authors need to build a training data set of question-answer pairs from a very large knowledge graph by using GraphFrames for big graph processing. To improve the performance of GraphFrames, they repartition the GraphFrames. For complicated query, they use the Stanford dependency parser to analyze the question and build the motif pattern for searching GraphFrames. They also develop a chatbot that can interact with the knowledge graph by using the natural language query. They conduct their system with question-answer generated from DBLP to prove the performance of our proposed system.

Natural Language To NoSQL Query Conversion using Deep Learning

2019 ◽  
Author(s):  
Pradeep T ◽  
Rafeeque P C ◽  
Reena Murali
Keyword(s):  
Deep Learning ◽  
Natural Language
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