CN113139041A - Modularized intelligent question-answering method and system - Google Patents
Modularized intelligent question-answering method and system Download PDFInfo
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- CN113139041A CN113139041A CN202110423169.8A CN202110423169A CN113139041A CN 113139041 A CN113139041 A CN 113139041A CN 202110423169 A CN202110423169 A CN 202110423169A CN 113139041 A CN113139041 A CN 113139041A
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Abstract
The invention provides a modular intelligent question answering method and system. The intelligent question answering module is applied to the intelligent robot and provides an intelligent question answering module for the intelligent robot. The method and the system comprise a text input module, an intention recognition module, an AIML chatting module, a knowledge graph module and an intelligent retrieval module. The text input module receives user input, and can manually input text or convert the text into characters for voice; the intention identification module classifies the problems based on semantic analysis and identifies task-type and non-task-type problems; the AIML chatting module presets chatting questions based on AIML; the knowledge graph module queries problems based on wikidata data; and the intelligent retrieval module is used as the last module to call the hundred-degree retrieval result and return the result. The intelligent question and answer system integrates the chatting module, the knowledge map module and the intelligent search module, provides a rich knowledge base for the household intelligent robot, and can conveniently expand and disassemble each module as an independent service, thereby meeting the intelligent question and answer requirement of the household robot.
Description
Technical Field
The invention relates to a modular intelligent question answering method and system, and belongs to the technical field of knowledge maps and natural language understanding.
Background
With the rapid development of artificial intelligence technology, intelligent question-answering robots are widely applied in different fields and scenes, but domestic intelligent question-answering robots are not yet popularized, the existing robots usually ask questions and answer questions in specific fields, the scenes are single, and the application to domestic intelligent robots is not very good in universality.
In order to solve the problems, an intelligent question and answer method which integrates chatting, knowledge maps and an intelligent search module, provides a rich knowledge base for a household intelligent robot, can be conveniently expanded and disassembled as independent services and can meet the intelligent question and answer requirements of the household robot is urgently needed.
Disclosure of Invention
The invention aims to provide a modular intelligent question-answering method and a modular intelligent question-answering system, which not only provide a rich knowledge base for a household intelligent robot, but also can conveniently expand and disassemble each module as an independent service.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a modularized intelligent question-answering system comprises a text input module, an intention identification module, an AIML chatting module, a knowledge graph module and an intelligent retrieval module;
the text input module receives user input, and can manually input text or convert the text into characters for voice;
the intention identification module classifies the problems based on semantic analysis and identifies task-type and non-task-type problems;
the AIML chatting module is used for presetting chatting problems based on AIML and can be conveniently expanded;
the knowledge graph module queries the data in the Blazegraph database;
and the intelligent retrieval module is used as the last module to call the third-party Baidu interface query result and return the third-party Baidu interface query result.
Preferably, the knowledge graph module performs question queries on data in the blackegraph database using SPARQL statements based on wikidata data.
A modular intelligent question answering method comprises the following steps:
step 1: a user inputs a sentence of natural language through a text input module;
step 2: after receiving text input, the intention identification module analyzes the intention by using a semantic analysis and natural language processing method, identifies task type question and non-task type question, if the question is task type, other task type question processing modules are called, and if the question is non-task type question, a chatting module is called;
and step 3: the chatting module presets chatting corpora in advance based on the AIML and can extend the corpora at any time; if the answer is not matched in the chatting module, entering a knowledge graph-based module;
and 4, step 4: after entering a knowledge graph module, extracting entities and attributes through a semantic analysis module, generating a SPARQL statement, calling wikitata to execute SPARQL query, and if the knowledge graph module does not match answers, entering an intelligent retrieval module;
and 5: and after entering the intelligent retrieval module, calling a third-party Baidu interface for searching, acquiring a question answer and returning the question answer to the intelligent robot.
The invention has the advantages that: the intelligent question and answer system integrates the chatting module, the knowledge map module and the intelligent search module, provides a rich knowledge base for the household intelligent robot, and can conveniently expand and disassemble each module as an independent service, thereby meeting the intelligent question and answer requirement of the household robot.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic view of the flow structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A modularized intelligent question-answering system comprises a text input module, an intention identification module, an AIML chatting module, a knowledge graph module and an intelligent retrieval module; the text input module receives user input, and can manually input text or convert the text into characters for voice; the intention identification module classifies the problems based on semantic analysis and identifies task-type and non-task-type problems; the AIML chatting module is used for presetting chatting problems based on AIML and can be conveniently expanded; the knowledge graph module queries the data in the Blazegraph database; and the intelligent retrieval module is used as the last module to call the third-party Baidu interface query result and return the third-party Baidu interface query result. And the knowledge map module performs problem query on data in the Blazegraph database by using a SPARQL statement based on wikidata data.
A modular intelligent question answering method comprises the following steps:
step 1: a user inputs a sentence of natural language through a text input module;
step 2: after receiving text input, the intention identification module analyzes the intention by using a semantic analysis and natural language processing method, identifies task type question and non-task type question, if the question is task type, other task type question processing modules are called, and if the question is non-task type question, a chatting module is called;
and step 3: the chatting module presets chatting corpora in advance based on the AIML and can extend the corpora at any time; if the answer is not matched in the chatting module, entering a knowledge graph-based module;
and 4, step 4: after entering a knowledge graph module, extracting entities and attributes through a semantic analysis module, generating a SPARQL statement, calling wikitata to execute SPARQL query, and if the knowledge graph module does not match answers, entering an intelligent retrieval module;
and 5: and after entering the intelligent retrieval module, calling a third-party Baidu interface for searching, acquiring a question answer and returning the question answer to the intelligent robot.
Claims (3)
1. A modularized intelligent question-answering system is characterized by comprising a text input module, an intention recognition module, an AIML chatting module, a knowledge graph module and an intelligent retrieval module;
the text input module receives user input, and can manually input text or convert the text into characters for voice;
the intention identification module classifies the problems based on semantic analysis and identifies task-type and non-task-type problems;
the AIML chatting module is used for presetting chatting problems based on AIML and can be conveniently expanded;
the knowledge graph module queries the data in the Blazegraph database;
and the intelligent retrieval module is used as the last module to call the third-party Baidu interface query result and return the third-party Baidu interface query result.
2. The modular intelligent question-answering method according to claim 1, wherein the knowledge graph module performs question queries on data in the bluzegraph database using SPARQL sentences based on wikidata data.
3. A modular intelligent question-answering method using claim 1, characterized by comprising the steps of:
step 1: a user inputs a sentence of natural language through a text input module;
step 2: after receiving text input, the intention identification module analyzes the intention by using a semantic analysis and natural language processing method, identifies task type question and non-task type question, if the question is task type, other task type question processing modules are called, and if the question is non-task type question, a chatting module is called;
and step 3: the chatting module presets chatting corpora in advance based on the AIML and can extend the corpora at any time; if the answer is not matched in the chatting module, entering a knowledge graph-based module;
and 4, step 4: after entering a knowledge graph module, extracting entities and attributes through a semantic analysis module, generating a SPARQL statement, calling wikitata to execute SPARQL query, and if the knowledge graph module does not match answers, entering an intelligent retrieval module;
and 5: and after entering the intelligent retrieval module, calling a third-party Baidu interface for searching, acquiring a question answer and returning the question answer to the intelligent robot.
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| CN113821621A (en) * | 2021-09-28 | 2021-12-21 | 中电万维信息技术有限责任公司 | Open intelligent customer service system based on deep learning |
| CN115146066A (en) * | 2022-09-05 | 2022-10-04 | 深圳市华付信息技术有限公司 | Man-machine interaction method, device, equipment and storage medium |
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