Structure Of Knowledge Base Research Articles

Learning Sentiment over Network Embedding for Recommendation System

With the rapid development of Internet, various unstructured information, such as user-generated content, textual reviews, and implicit or explicit feedbacks have grown continuously. Though structured knowledge bases (KBs) which consist of a large number of triples exhibit great advantages in recommendation field recently. In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding method with the sentiment of user reviews. Specifically, we use the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and attractions and apply the user weight scoring which is the combination of user review ratings and textual reviews to get similar attractions among users. Experimental results on real-world dataset verified the superior recommendation performance on precision, recall, and F-measure of our approach compared with state-of-the-art baselines.

РАЗРАБОТКА ИНСТРУМЕНТАЛЬНО-МЕТОДИЧЕСКОГО ОБЕСПЕЧЕНИЯ ДЛЯ ОЦЕНИВАНИЯ ЗНАНИЙ УЧАЩИХСЯ ПО МАТЕМАТИКЕ В РАМКАХ ГИБРИДНОЙ ИНТЕЛЛЕКТУАЛЬНОЙ ОБУЧАЮЩЕЙ СРЕДЫ

The article is devoted to the development of an integrated complex of knowledge base and artifi-cial intelligence tools for assessing students' knowledge in mathematics within the framework of a hybrid intellectual learning environment. In accordance with the structural diagrams of mod-els of intellectual assessment of knowledge and creative potential of schoolchildren in mathe-matics, the structure of the corresponding knowledge base is formalized. The knowledge base is developed within the framework of data mining methods to create a module for monitoring and assessing knowledge in mathematics. As a methodological basis for the development of a knowledge base, the idea of V.P. Bespalko about the representation of the possible structure of human activity in the form of suc-cessive levels of assimilation, reflecting the development of the student's experience in a given subject in the learning process. The levels of assimilation (student, typical, heuristic, research) are determined through the ability to solve problems of the corresponding type. A formalized in-dicator of a student's achievement of a particular level of assimilation of educational material (level of training) is the coefficient of assimilation; assessment of the assimilation of material on a specific topic or section is provided for both at each of the above levels, and in general. The examples of the formation of a knowledge base for assessing the skills and abilities of schoolchildren in algebra are considered. To model the educational process, we propose methods for solving clustering, dimen-sionality reduction and classification problems based on machine learning and artificial neural networks. The foundations for the development of integrated complexes of knowledge bases and artificial intelligence tools to ensure the educational process are presented. The results obtained are aimed at creating methods that provide the processes of operational learning, control and assessment of knowledge, competencies and procedures, the level of formation of subject and professional competencies of students in the information and educational intellectual environment.

A Knowledge-Enhanced Dialogue Model Based on Multi-Hop Information with Graph Attention

With the continuous improvement of the e-commerce ecosystem and the rapid growth of e-commerce data, in the context of the e-commerce ecosystem, consumers ask hundreds of millions of questions every day. In order to improve the timeliness of customer service responses, many systems have begun to use customer service robots to respond to consumer questions, but the current customer service robots tend to respond to specific questions. For many questions that lack background knowledge, they can generate only responses that are biased towards generality and repetitiveness. To better promote the understanding of dialogue and generate more meaningful responses, this paper introduces knowledge information into the research of question answering system by using a knowledge graph. The unique structured knowledge base of the knowledge graph is convenient for knowledge query, can acquire knowledge faster, and improves the background information needed for answering questions. To avoid the lack of information in the dialogue process, this paper proposes the Multi-hop Knowledge Information Enhanced Dialogue-Graph Attention (MKIED-GA) model. The model first retrieves the problem subgraph directly related to the input information from the entire knowledge base and then uses the graph neural network as the knowledge inference module on the subgraph to encode the subgraph. The graph attention mechanism is used to determine the one-hop and two-hop entities that are more relevant to the problem to achieve the aggregation of highly relevant neighbor information. This further enriches the semantic information to provide a better understanding of the meaning of the input question and generate appropriate response information. In the process of generating a response, a multi-attention flow mechanism is used to focus on different information to promote the generation of better responses. Experiments have proved that the model presented in this article can generate more meaningful responses than other models.

Exploring the Microbiota-Gut-Brain Axis for Mental Disorders with Knowledge Graphs

Gut microbiota has a significant influence on brain-related diseases through the communication routes of the gut-brain axis. Many species of gut microbiota produce a variety of neurotransmitters. In essence, the neurotransmitters are chemicals that influence mood, cognition, and behavior of the host. The relationships between gut microbiota and neurotransmitters has received much attention in medical and biomedical research. However, the integration of the various proposed neurotransmitter signal routes that underpin these relationships has not yet been studied well. To unlock the influence of gut microbiota on mental health via neurotransmitters, the microbiota-gut-brain (MGB) axis, we gather the decentralized results in the existing studies into a structured knowledge base. In this paper, we therefore propose a novel Microbiota Knowledge Graph based on a newly constructed knowledge graph for uncovering the potential associations among gut microbiota, neurotransmitters, and mental disorders which we refer to as MiKG. It includes many interfaces that link to well-known biomedical ontologies, e.g. UMLS, MeSH, KEGG, and SNOMED CT, and is extendable by linking to future ontologies to further exploit the relationships between gut microbiota and neurotransmitters. This paper present MiKG, an effective knowledge graph, that can be used to investigate the MGB axis using the relationships among gut microbiota, neurotransmitters, and mental disorders.

Multi-goal multi-agent learning for task-oriented dialogue with bidirectional teacher–student learning

In this work, we propose a multi-goal multi-agent learning (MGMA) framework for task-oriented dialogue generation, which aims to retrieve accurate entities from knowledge base (KB) and generate human-like responses simultaneously. Specifically, MGMA consists of a KB-oriented teacher agent for inquiring KB, a context-oriented teacher agent for extracting dialogue patterns, and a student agent that tries to not only retrieve accurate entities from KB but also generate human-like responses. A “two-to-one” teacher–student learning method is proposed to coordinate these three networks, training the student network to integrate the expert knowledge from the two teacher networks and achieve comprehensive performance in task-oriented dialogue generation. In addition, we also update the two teachers based on the output of the student network, since the space of possible responses is large and the teachers should adapt to the conversation style of the student. In this way, we can obtain more empathetic teachers with better performance. Moreover, in order to build each task-oriented dialogue system effectively, we employ a dialogue memory network to dynamically filter the irrelevant dialogue history and memorize important newly coming information. Another KB memory network, which shares the structural KB tuples throughout the whole conversation, is adopted to dynamically extract KB information with a memory pointer at each utterance. Extensive experiments on three benchmark datasets (i.e., CamRest, In-Car Assistant and Multi-WOZ 2.1) demonstrate that MGMA significantly outperforms baseline methods in terms of both automatic and human evaluation.

Predicting the relationships between gut microbiota and mental disorders with knowledge graphs

Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as ‘matchmaker’ between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.

Designing Organizational Entrepreneurship Structural Equation Modeling with Emphasis on Knowledge Management and Empowerment of Employees

Objective: The purpose of this research was to design an Organizational Entrepreneurship Structural Equation Modeling with emphasis on knowledge management and empowerment of employees. The statistical population consisted of the personnel of IranKhodro Company, totaling 1000 people. Methodology: Using the Cochran equation, the sample size was 278 people. Data were collected by Thomson Enterprise Standard Entrepreneurship Questionnaire (2002), Knowledge Management, Proust et al. (2000), and Empowerment of Speakers (1995), whose face and content validity was confirmed by 10 experts and its reliability was in a preliminary test. With 30 subjects, all of the variables evaluated were above 0.7, which has acceptable limit. Results: Descriptive and inferential statistical methods of structural equation analysis using Smart PLS software and regression test in SPSS software were used to analyze the data. Findings showed that knowledge management and its components affect organizational entrepreneurship. On the other hand, employee empowerment and its components also have a mutual positive and significant effect on organizational entrepreneurship. Conclusion: Therefore, according to the results of this study, it can be said that designing a knowledge-based structure at all levels of the country's organizations will be effective in improving the entrepreneurial status of these organizations, and in this regard, managers will play an important role as producers and national knowledge funds. Therefore, it is necessary that the top managers of IranKhodro Company be more concerned with the training and empowerment of their employees and one of the first activities to be considered by the managers of this organization and emphasized in different sections. Educational needs based on the acquisition of knowledge as one of the components of knowledge management and the establishment of continuing education programs for empowerment in different parts of IranKhodro Company, in order to empower the employee, will develop organizational entrepreneurship in this organization.

Ontological methods and tools for semantic extension of the media WIKI technology

Practical aspects of ontological approach to organization of intelligent Wiki-based information resources (IR) are considered. We analyze the main features, capabilities and limitations of MediaWiki as a technological platform for development of the Web-based information resource and suggest main directions of its refinement. We propose an abstract model of MediaWiki architecture that formalizes relations between the main components of this software environment and analyze the ways of its semantic extensions based on ontological representation of domain knowledge. An original algorithm of semantic Wiki pages matching with domain ontology is developed. We propose an ontological model of IR that formalizes its knowledge base structure and explicitly performs main features of typical information objects (TIO) of this IR. Such TIOs depend on domain specifics and purposes of IR, therefore their development has to involve domain experts and knowledge engineers. Use of ontology corresponding to the set of Wiki pages (either with semantic markup or without it) provides new IR functions associated with semantic search and navigation. Other important aspect of intelligent Wiki resource development deals with adaptation of user interface to the specifics of IR: enabling various tools of navigation, visualization and content analysis by processing of TIO features enriches IR functionality, reduces access time to information and makes usage of IR more efficient. Developing additional MediaWiki functionality with new requests to the MediaWiki API using TIO templates, extends data analysis and integration capabilities, and offers different, user-focused, IR content views expands the possibilities of data integration and proposes various user-oriented representations of IR content. Wiki resource semantization allows the use knowledge acquired from such IR by external application, or example, by search engines for intelligent Web retrieval. Domain ontologies based on various subsets of the Wiki pages and generated by them thesauri can be used by various Semantic Web applications, both independently or in general technological chain for personified retrieval focused on individual users and their tasks. Approbation of this approach is demonstrated by MAIPS retrieval system. We consider the use semantic similarity of concepts represented by Wiki-pages of IR as an additional way of intelligent navigation between these pages. Such approach allows to group Wiki pages according to user interests by different aspects of their content and structure. Wiki ontologies are considered as the basis for estimation of semantic similarity between domain concepts pertinent to user task. Such elements of Wiki ontology as classes, property values of class instances and relations between them are used as parameters for the quantitative assessment of semantic similarity of Wiki pages. We propose to use local similarity and generate the sets of semantically similar concepts (SSC) that takes into account some subset of page properties and categories defined by user needs. Such sets of SSCs can be considered as user task thesauri for other applications. In addition, we propose to enrich the basic tools of MediaWiki used for access management to the IR content with specialized software code that performs content classification that take into consideration separate namespaces, categories, templates and semantic properties of TIO acquired from Wiki markup. We demonstrate the software implementation of proposed solutions by developing of portal version of the Great Ukrainian Encyclopedia (e-VUE) that contains heterogeneous multimedia content with complex structure. We analyze the specifics of e-VUE knowledge system and develop its formalized TIO representation based on Semantic Web technologies and ontological analysis. Ontological model of e-VUE and original methods of its processing used for this project extend the functionality of the portal in the area of search, navigation, integration and protection of content based on background domain knowledge. In addition, original user interface of e-VUE is developed with an allowance for Encyclopedia knowledge specifics, substantially differs from the standard Wiki, meets the requirements, goals and objectives of this IR and provides a lot of additional features.

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level

BackgroundPrecision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications.ResultsHere, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one.ConclusionsOncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com.

Mapping continuity and change in the intellectual structure of the knowledge base on problem‐based learning, 1974–2019: A systematic review

Problem‐based learning (PBL) emerged during the 1970s in response to demands for active learning methods capable of developing transferable knowledge and skills in the training of doctors. Over succeeding decades, PBL was gradually adopted in other fields of education. This systematic review aimed to identify key streams of theory and empirical research that have emerged over time in PBL research and practice. The review sourced 12,036 Scopus‐indexed documents published between 1974 and 2019. Science mapping was used to reveal the ‘intellectual structure’ or key research themes that have evolved in this literature over the past 45 years. The science mapping tool used in this review was author co‐citation analysis conducted in VOSviewer software. Author co‐citation analysis identified three schools of thought that together describe the intellectual structure of the PBL knowledge base: Design of PBL Curriculum and Instruction; PBL Effectiveness; Theory and Practice in Active Learning. In addition to portraying the intellectual structure of the literature as a whole, the review also conducted longitudinal analyses aimed at highlighting structural changes in this field over time. These analyses found that although the size and impact of schools of thought associated with Design of PBL Curriculum and Instruction and PBL Effectiveness increased over time, they remained stable in terms of theoretical foci. However, the Active Learning school evolved from a small school of authors associated with Cognitive Learning Theories prior to 2000 into the largest school of thought during the most recent decades. These findings both reaffirm the theoretical underpinnings of the PBL knowledge base and highlight its increasing integration with other forms of active learning.

The My Cancer Genome clinical trial data model and trial curation workflow.

ObjectiveAs clinical trials evolve in complexity, clinical trial data models that can capture relevant trial data in meaningful, structured annotations and computable forms are needed to support accrual.Material and MethodsWe have developed a clinical trial information model, curation information system, and a standard operating procedure for consistent and accurate annotation of cancer clinical trials. Clinical trial documents are pulled into the curation system from publicly available sources. Using a web-based interface, a curator creates structured assertions related to disease-biomarker eligibility criteria, therapeutic context, and treatment cohorts by leveraging our data model features. These structured assertions are published on the My Cancer Genome (MCG) website.ResultsTo date, over 5000 oncology trials have been manually curated. All trial assertion data are available for public view on the MCG website. Querying our structured knowledge base, we performed a landscape analysis to assess the top diseases, biomarker alterations, and drugs featured across all cancer trials.DiscussionBeyond curating commonly captured elements, such as disease and biomarker eligibility criteria, we have expanded our model to support the curation of trial interventions and therapeutic context (ie, neoadjuvant, metastatic, etc.), and the respective biomarker-disease treatment cohorts. To the best of our knowledge, this is the first effort to capture these fields in a structured format.ConclusionThis paper makes a significant contribution to the field of biomedical informatics and knowledge dissemination for precision oncology via the MCG website.Key wordsknowledge representation, My Cancer Genome, precision oncology, knowledge curation, cancer informatics, clinical trial data model

Open Information Extraction for Waste Incineration NIMBY based on Bert Network in China

Unstructured data makes it difficult to build the emergency knowledge base. Information extraction can help people get structured information faster and more accurately. In this paper, a method of information extraction based on Bert neural network is proposed, which can extract information from the Chinese open text of Waste Incineration NIMBY. It provides a scientific scheme for the construction of a structured knowledge base of Waste Incineration NIMBY, so as to provide auxiliary decision-making for solving the conflict of Waste Incineration NIMBY. Experiments show that the precision rate, recall rate and F-value of the information extraction can reach 91% by using the Bert network, which basically meets the requirements of building a structured knowledge base of Waste Incineration NIMBY.

Impact of Knowledge Search Practices on the Originality of Inventions in the Oil & Gas Industry

The paper suggests a new taxonomy of knowledge search modes to describe the creative process of new invention design, in particular how firms combine knowledge components from their own knowledge base—taking into account both the components and the structures of knowledge bases—with those from newly acquired or newly internally developed. Using network theory techniques, we defined four knowledge search modes: (1) refinement, (2) clustering, (3) absorption and (4) recomposition. We conducted an exploratory study on the oil & gas industry, reviewing 50,776 utility patents filed by 16 major firms between 1989 and 2016. The results showed, first, that firms relied to varying extents on different knowledge search modes in their invention design processes. Second, reviewing the technological originality of the designed inventions showed that simply absorbing new knowledge components, without major changes in knowledge base structure, was associated with low technological originality, but constituted one of the main knowledge search modes used by the analyzed firms. In contrast, major changes in knowledge base structure favored technological originality, with or without new knowledge components, but were nevertheless the least used mode. Understanding organizational learning practices associated with the phenomena described here can foster innovation performance in firms.

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. Specifically, we extract evidence from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain texts. We construct graphs for both sources to obtain the relational structures of evidence. Based on these graphs, we propose a graph-based approach consisting of a graph-based contextual word representation learning module and a graph-based inference module. The first module utilizes graph structural information to re-define the distance between words for learning better contextual word representations. The second module adopts graph convolutional network to encode neighbor information into the representations of nodes, and aggregates evidence with graph attention mechanism for predicting the final answer. Experimental results on CommonsenseQA dataset illustrate that our graph-based approach over both knowledge sources brings improvement over strong baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the CommonsenseQA dataset.

New modeling of reconfigurable microstrip antenna using hybrid structure of simulation driven and knowledge based artificial neural networks

Knowledge-based modeling has a critical role to embed existing knowledge to improve modeling performance. Since reconfigurable antenna can provide more operational frequencies than the classical antennas, a knowledge-based hybrid structure is used in this work to obtain efficient model and producing optimum new models for a reconfigurable microstrip antenna. The hybrid structure consists of two phases. The first phase generates initial knowledge which is used in knowledge-based modeling structure to obtain design parameters. Artificial neural network based multilayer perceptron can generate necessary knowledge for a knowledge-based model after the training process. Knowledge-based modeling improves the accuracy of the initial model to determine design parameters corresponding to the design target. Source difference, prior knowledge Input and prior knowledge input with difference can be applied to realize an efficient knowledge-based strategy. 3D-EM simulation generates the new model in terms of the design parameters of the proposed application. It has three switching states for operating, which are organized by two resistor circuits representing ON/OFF states. Switch positions and geometrical parameters can be used for satisfying design targets between 1 GHz and 6 GHz for the efficient antenna design.

Research on Knowledge Representation and Automatic Recognition of Dynamic Words for Chinese Automatic Syntactic Analysis

There are many temporarily constructed dynamic words in Chinese sentences. Dynamic words are sentence building units that are not included in the general lexicon and are not suitable for further syntactic analysis. Automatic recognition and analysis of dynamic words in sentences play an important role in improving the efficiency and accuracy of Chinese automatic syntactic analysis. The existing researches on dynamic words mainly focus on the qualitative description of concepts and categories. There is no overall algorithm design and experimental exploration on automatic recognition of dynamic words. In the practice of automatic syntactic analysis, dynamic words are generally segmented, and the components are analyzed according to syntax, while the automatic recognition and analysis of dynamic words as a whole are ignored. In this study, the dynamic word is separated from syntactic analysis as the content of lexical analysis and recognized and analyzed as a whole. This paper uses the method of knowledge engineering to research and analyze dynamic words for Chinese automatic syntactic analysis based on sentence pattern structure, initially designs a knowledge representation method of dynamic words, secondly constructs the dynamic word structural mode knowledge base by annotating the dynamic words in the corpus of a certain scale of international Chinese textbooks, and finally explores the automatic recognition methods of dynamic words based on regular expressions, semantic category combinations and machine learning classification algorithms. The experimental results show that the three algorithms can cover the recognition of all types of dynamic words, and achieve relatively ideal accuracy and recall rate.

Automatic Generation of Questions from DBpedia

The production of Educational quizzes is a time-consuming task that can be automated by taking advantage of existing knowledge bases available on the Web of Linked Open Data (LOD) such as DBpedia. For these quizzes to be useful to learners, their questions must be generated in compliance with the knowledge and skills necessary to master for each subject and school year according to the official educational standards. We present an approach that exploits structured knowledge bases that contain the knowledge and skills, from which it selects a set of DBpedia resources relevant to a specific school subject and year. This set of resources is enriched with additional related resources, through the NEFCE heuristic. Finally, question generation strategies are applied to the graph generated with this set of resulting resources. Likewise, we provide an evaluation of two knowledge bases and of the proposed NEFCE heuristics.

Розробка методів керування доступом до інформації у WIKI-ресурсах

The prospects and scope of Wiki technologies are considered, the structure of the content re presentation and MediaWiki software are analyzed. Considerable attention is given to architecture of MediaWiki and the components of this architecture. The problem deal with the need to access management to the content of Wiki-resources in accordance with the specifics of the information contained in such resources is identified. The results of the analysis show that the basic tools of MediaWiki are not capable to allow a satisfactory solution to this problem. Therefore, we need to create specialized software that is based on the content classification with the use of separate namespaces, categories, templates and semantic properties extracted from MediaWiki. Such software has to be independent of the MediaWiki core and be based on programming of content analysis at the skin level. We test the proposed solution in the process of development of the portal version of the Great Ukrainian Encyclopedia (e-vue) with use of knowledge regarding the knowledge base structure of this portal. Prom ble s in programming 2020; 1: 33-46

Health Assessments of Common Bottlenose Dolphins (Tursiops truncatus): Past, Present, and Potential Conservation Applications

The common bottlenose dolphin (Tursiops truncatus) is a global marine mammal species for which some populations, due to their coastal accessibility, have been monitored diligently by scientists for decades. Health assessment examinations have developed a comprehensive knowledge base of dolphin biology, population structure, and environmental or anthropogenic stressors affecting their dynamics. Bottlenose dolphin health assessments initially started as stock assessments prior to acquisition. Over the last four decades, health assessments have evolved into essential conservation management tools of free-ranging dolphin populations. Baseline data enable comparison of stressors between geographic locations and associated changes in individual and population health status. In addition, long-term monitoring provides opportunities for insights into population shifts over time, with retrospective application of novel diagnostic tests on archived samples. Expanding scientific knowledge enables effective long-term conservation management strategies by facilitating informed decision making and improving social understanding of the anthropogenic effects. The ability to use bottlenose dolphins as a model for studying marine mammal health has been pivotal in our understanding of anthropogenic effects on multiple marine mammal species. Future studies aim to build on current knowledge to influence management decisions and species conservation. This paper reviews the historical approaches to dolphin health assessments, present day achievements, and development of future conservation goals.

Discuss on the innovation of core concepts in the teaching of physiology

Medical education reform mainly focused on the reform of the form and method of teaching but neglected the reform of the teaching concept that has truly penetrated into all aspects of teaching. With reference to the reflection on the teaching goal of physiology and the need of the cultivation of post competency, the core concepts of physiological teaching are summarized into the following four aspects: Three Outlooks must be positive to help students establish a balanced view, a dialectical view, and a holistic view; Three Fundamentals should be solid to help students master basic knowledge structure system, cultivate basic knowledge acquisition ability, and improve basic operation skills; students should attach importance to learning and use; humanistic quality should be strengthened. The above core concepts have been widely recognized by students in teaching practice, and clinical medical students have significantly better evaluation of the core concepts than nursing students. Future direction of the subsequent teaching reform should be how to refine the contents of lectures under the guidance of the teaching concepts to adapt to the increasingly detailed specialities. Key words: Physiology; Teaching reform; Teaching principle