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GrandhooGuanZhong ITAI Intelligent Question Answering Solution
Overview
Based on knowledge graph, natural language processing, speech recognition and other artificial intelligence technologies, the intelligent question answering system is equipped in the dialogue robot in the service scene to assist or replace the artificial dialogue, and empower the whole process of dialogue to achieve cost reduction and efficiency increase. Widely used in government services, online customer service, marketing and enterprise information services Based on knowledge graph, natural language processing, speech recognition and other artificial intelligence technologies, the intelligent question answering system is carried in the dialogue robot in the service scene, assisting or replacing the artificial dialogue, and enabling the whole process of dialogue to achieve cost reduction and efficiency increase. It is widely used in government services, online customer service, marketing and enterprise information services.
GuanZhong is based on a database with completely independent intellectual property rights, combined with the fully self-developed knowledge graph engine and intelligent distribution engine. It applies advanced technologies such as data analysis, machine learning and knowledge graph to provide users with instant and correctness guaranteed consultation responses, and intelligently distributes complex questions. The system provides intelligent question answering, intelligent recommendation, multi-document reading comprehension, knowledge graph analysis, knowledge data governance, intelligent distribution, multi-source knowledge base and other functions to build an intelligent question answering robot supporting deep semantic recognition.
Challenges
Industry and business knowledge is difficult to precipitate
The current robotic systems are hindered by the diversity of knowledge data modalities and poor data quality, which prevents the rapid and intelligent transformation of data into knowledge in knowledge engineering. It requires manual governance of data quality and the extraction of relevant and effective knowledge from dialogues and other content. The vast amount of knowledge and the difficulty in categorizing and storing it result in low entry efficiency. The timeliness cannot meet user demands, and it is challenging to accumulate industry and business Q&A knowledge automatically and precisely.
Irrelevance to the question asked makes it difficult to enhance user satisfaction
Users seek more accurate responses, but often encounter irrelevant or no answers during the conversation. Therefore, the accumulation of corpora data, semantic decomposition, semantic recognition, and precise question-answering matching are core competencies of intelligent Q&A robots.
Inability to quickly adapt and match business scenarios
Market demands change at a certain speed, and the content of Q&A has its timeliness. Q&A scenarios need to respond promptly, swiftly adapt to adjustments in Q&A content, match business upgrades, and eliminate outdated Q&A.
Architecture
Benefits
Intelligent knowledge processing with accuracy assurance
Based on original theories such as Big Data Quality Assurance Model and Method, Explainable AI by Combining Statistical and Logical Methods, enabling salespeople to quickly identify potential errors in raw data, combined with downstream task processing under LLM, to rapidly form various knowledge data from texts, tables, and other multi-source data.
Cross-modal knowledge data retrieval
Based on Cross-Modal Fusion Computation and the vector retrieval capability in the original YashanDB operating system, it achieves unified cross-modal querying of relational data and graph data. This lets robots analyze multiple types of knowledge bases simultaneously when dealing with complex questions, enhancing the accuracy of robot responses.
In-depth knowledge association recommendation
Leveraging YashanDB's approximate query processing and adaptive asynchronous parallel capabilities to realize efficient parallel computing of the knowledge graph engine. This enables responses based on the knowledge graph to perform deep analysis and recommendation of multi-hop knowledge within the graph.
Multi-text reading comprehension
By testing and calibrating the understanding and reasoning abilities of LLM, as well as its capability to answer specific questions in the text, it realizes the selection or generation of the most appropriate answer from a text containing paragraph(s) or multiple related knowledge based on the content of the question and related context.
Answer traceability
Based on multi-text reading comprehension and knowledge graph analysis capabilities, it realizes answer traceability to the original text content through text extraction. The system deals with various questions and original texts, not just specific types, making it generally powerful. Users can trace back knowledge to ensure that the answers are based on factual and original text information
Fully self-researched industry-controllable large model
Relying on capabilities such as natural language processing, graph knowledge fusion reasoning, text vector retrieval, and integrating industry knowledge data, this fully self-researched controllable language Q&A large model has self-learning, self-training, high scalability, and strong contextual understanding capabilities.