Building a literature knowledge base towards transparent biomedical AI
Yuanhao HuangZhaowei HanXin LuoXuteng LuoYijia GaoMeiqi ZhaoFeitong TangYiqun WangJiyu ChenChengfan LiXinyu LuJiahao QiuF. DengTiancheng JiaoD XueFan FengThi Hong Ha VuLingxiao GuanJean‐Philippe CartaillerMichael L. StitzelShuibing ChenMarcela BriššováStephen C. J. ParkerJie Liu
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Knowledge graphs have recently emerged as a powerful data structure to organize biomedical knowledge with explicit representation of nodes and edges. The knowledge representation is in a machine-learning ready format and supports explainable AI models. However, PubMed, the largest and richest biomedical knowledge repository, exists as free text, limiting its utility for advanced machine learning tasks. To address the limitation, we present LiteralGraph, a computational framework that rigorously extracts biomedical terms and relationships from PubMed literature. Using this framework, we established Genomic Literature Knowledge Base (GLKB), a knowledge graph that consolidates 263,714,413 biomedical terms, 14,634,427 biomedical relationships, and 10,667,370 genomic events from 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that make it accessible to researchers for various usages, including machine learning using the semantic knowledge in PubMed articles, reducing hallucination of large language models (LLM), and helping experimental scientists explore their data using vast PubMed evidence.Keywords:
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This passage introduces the relevance betereen the knowledge, knowledge base and knowledge base management system on the basic of knowledge base and knowledge base management system in the expert system,and dissolves then into the way of database management.
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Knowledge base system is the core of the intelligent design system ,this paper studies the idea of knowledge base system pragmatism and the characteristic of the sugarcane harvester knowledge, it constructs the general structure of knowledge base system, discusses every main module function of the knowledge base system, it puts forward the knowledge representation, and with the development of knowledge base system adopting different knowledge acquirement method. On the basis of that accomplished knowledge base system of the sugarcane harvester with VC + + for programming language.
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In this paper the notation of base-mesocompactness is introduced and the following results are mainly obtained:(1) Let X be base-mesocompact and X' an F σ subset of X.If X is normal,then X' is base-mesocompact relative to X.(2) Let f:X → Y be a base-mesocompact mapping,ω(X) be a regular cardinality of X and ω(X) ≥ ω(Y).If Y is base-mesocompact and regular,then X is base-mesocompact.(3) Let f:X → Y be a closed lindelof mapping with regular domain and regular range.If Y is base-mesocompact,then X is base-mesocompact.(4) Let X be base-mesocompact.If Y is locally compact and base-mesocompact,then X × Y is base-mesocompact.
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The views of knowledge base may be divided logically two types that is base and base view.The external base view has been reflected,questioned and falsificated.The specific type of internal base view contains base view of common sense,logic and discipline.They are also in question.The existing base view has been failure,which makes production of educational knowledge in trouble.The suitable base is educational classic;therefore,the urgent work will be interoperating of educational classic.
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Knowledge base validation and knowledge base refinement aim to help the expert to improve an existing knowledge base. They deal with the final knowledge acquisition phase and rely on a quality measurement of an existing knowledge base. We present our approach to knowledge base refinement, which is based on results in the domain of knowledge base validation. Our approach is based on a general consistency definition of a knowledge base and on a study of causes of knowledge base inconsistency. Our approach relies significantly on a differentiation of sure and expert knowledge in the knowledge base. We have implemented a system that has two phases: one computational phase decides on the consistency of a knowledge base, and, if necessary, a second phase helps the expert to interactively update the knowledge base. We present some related work in the domain. We illustrate the use of our system with an example.
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Mechanical design requires high-experience knowledge, and an expert system is effective in such a situation. In case knowledge is inputted into this expert system, it is desirable for an expert to directly input knowledge. In this research, a system to which the knowledge input to an expert system is made as for anyone is proposed. Therefore, an expert system structure is described first, and the knowledge base built by the production system is described berow. The Expert system is made as an experiment by them, and the mechanical design expert system and knowledge base has validity checked.
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In the first report, an attemp was done to develop an expert system with the knowledge base that describes the relations among many components in the system for the sequential control of a marine diesel engine plant.The system was proved to work well in such a complicated operation.In the present report the idea of the knowledge base with hierarchical structure is introduced. Then, the present knowledge base contains two different parts.The first is a kind of common knowledge base that describes the input-output relations of every component included usually in these systems and the hierarchical procedure is employed in the descriptions. The second is used for connecting the knowledge base of the components into a system, being based on the database that describes the component connections in the individual engine plant.Therefore the knowledge base can be easily built up for an individual plant by writing only the database of the component connections.It is finally pointed out that the new knowledge base system can be constructed at ease and work successfully.
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Knowledge base is a very important database for knowledge management, which is very useful for Question Answering, Query Expansion and other AI tasks. However, due to the fast-growing knowledge on the web and not all common knowledge expressed in the text is explicit, the knowledge base always suffers from incompleteness. Recently many researchers are trying to solve the problem as link prediction, only using the existing knowledge base, however, it is just knowledge base completion without adding new entities, which emerges from unstructured text not in existing knowledge base. In this paper, we propose a multimodal deep neural network framework that trying to learn new entities from unstructured text and to extend the knowledge base. Experiments demonstrate the excellent performance.
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