Partial Order Structure Diagram Research Articles

Incremental concept cognitive learning in dynamic formal contexts based on attribute partial order structure diagram

Incremental concept cognitive learning in dynamic formal contexts based on attribute partial order structure diagram

Incremental concept cognitive learning in dynamic formal contexts based on attribute partial order structure diagram

Incremental concept cognitive learning in dynamic formal contexts based on attribute partial order structure diagram

Graph representation learning method based on three-way partial order structure

In the era of big data, handling massive datasets to extract valuable information has become increasingly critical. Knowledge representation emerges as a pivotal method to address this challenge. In the domain of knowledge representation, there exist two primary approaches: symbolic representation and vector representation. The integration of symbolic and vector representations to harness their respective strengths has become the cutting-edge approach to address challenges in the field of knowledge representation. This paper proposes a method that integrates a partial order formal structure analysis (POFSA) with graph representation learning. Specifically, we initially construct three-way partial order structure graphs, then create an attribute graph based on this structure, which can be processed by the graph representation learning methods. Finally, we utilize the graph representation learning to construct embeddings for three-way attribute partial order structure diagram (APOSD). We comprehensively assesse these embeddings across eight different datasets and present the results. The experiments indicate the feasibility of our proposed approach which is proven a novel approach of combining symbolic and vector representations for handling complex data and implicit knowledge.

The construction of fuzzy linguistic attribute partial ordered structure diagram

The construction of fuzzy linguistic attribute partial ordered structure diagram

Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams

So far, no research has used the partial order algorithm for the mining of hospital medical technology. This paper proposed a novel knowledge discovery method of hospital medical technology based on partial ordered structure diagrams, constructed attribute partial ordered structure diagram and object partial ordered structure diagram for the formal context constructed by hospital set and medical technology set, and finally analyzed them using the knowledge discovery method. The experiments show that the partial ordered structure diagram can effectively visualize the structural relationships between hospital sets and medical technology sets, and the distribution characteristics of medical technology sets in hospital sets and the rules of medical technology sets owned by hospital sets can be obtained based on the node, branch, and group structure relationships of the partial ordered structure diagram.

A Filter-APOSD approach for feature selection and linguistic knowledge discovery

The development of natural language processing promotes the progress of general linguistic studies. Based on the selected features and the extracted rules for word sense disambiguation (WSD), some valuable knowledge of the relations between linguistic features and word sense classes may be discovered, which may provide theoretical and practical evidence and references for lexical semantic study and natural language processing. However, many available approaches of feature selection for WSD are in the end to end operation, they can only select the optimal features for WSD, but not provide the rules for WSD, which makes knowledge discovery impossible. Therefore, a new Filter-Attribute partial ordered structure diagram (Filter-APOSD) approach is proposed in this article to fulfill both feature selection and knowledge discovery. The new approach is a combination of a Filter approach and an Attribute Partial Ordered Structure Diagram (APOSD) approach. The Filter approach is designed and used for filtering the simplest rules for WSD, and the APOSD approach is used to provide the complementary rules for WSD and visualize the structure of the datasets for knowledge discovery. The features occurring in the final rule set are selected as the optimal features. The proposed approach is verified by the benchmark data set from the SemEval-2007 preposition sense disambiguation corpus with around as the target word for WSD. The test result shows that the accuracy of WSD of around is greatly improved comparing with the one by the state of the art, and 17 out of 22 features are finally selected and ranked according to their contribution to the WSD, and some knowledge on the relations between the word senses and the selected features is discovered.

Rule acquisition based on attribute partial order structure diagram

Rule acquisition based on attribute partial order structure diagram

Rule acquisition based on attribute partial order structure diagram

Rule acquisition based on attribute partial order structure diagram

Incremental concept cognitive learning based on three-way partial order structure

With the vigorous development of the information technology industry, the information data available to mankind has shown an explosive growth trend. Dynamic concept learning is an approach that can effectively process the acquired massive data and extract valuable information from them. Concept cognitive learning (CCL) is a very active research direction in the field of dynamic concept learning, while partial order formal structure analysis (POFSA) is a concrete and practical model of CCL. However, the existing CCL algorithms in POFSA face some challenges when processing constantly changing data. Therefore, this paper is devoted to explore an incremental CCL algorithm based on three-way object partial order structure diagram (OPOSD) in POFSA with the incorporation of the thoughts of incremental learning. The features of five object categories are considered, and their incremental influences on three-way OPOSD are analyzed and their incremental CCL algorithms in three-way OPOSD are established. Based on some real famous formal contexts, this paper conducts numerical experiments, and the results show that the incremental CCL algorithm based on three-way OPOSD is consistent with human cognitive principles, and can improve the CCL performance of POFSA as well.

Study on Medication Regularity of Sijunzi Decoction and Its Categorized Prescriptions Based on Attribute Partial Order Structure Diagram

Sijunzi decoction is a famous prescription in traditional Chinese medicine (TCM) for the treatment of spleen deficiency syndrome. Spleen deficiency syndrome is manifested in a variety of symptoms. Herbs can be added or replaced on the basis of Sijunzi decoction according to different symptoms, forming Sijunzi categorized prescriptions. In this paper, the medication regularity of Sijunzi decoction and its categorized prescriptions was studied based on attribute partial order structure diagram (APOSD). According to prescription-herb APOSD generated from Sijunzi decoction categorized prescriptions screened from comprehensive TCM database, it can be seen that three herbs are used in all prescriptions, i.e. Renshen, Baizhu and Fuling. Different forms of Sijunzi decoction mainly depend on whether the forth herb is Zhigancao, Chenpi-Gancao, or Gancao. The efficacy of common herbs in the prescriptions for different symptoms can be presented by prescription-herb APOSD and prescription-symptom APOSD generated from different prescription subgroups. The experimental results show that APOSD can effectively reveal the medication regularity of Sijunzi decoction and its categorized prescriptions, and it is an useful tool for theoretical study of TCM.

A novel classification tree based on local minimum Gini index and attribute partial order structure diagram

A novel classification tree based on local minimum Gini index and attribute partial order structure diagram

A novel classification tree based on local minimum Gini index and attribute partial order structure diagram

Decision tree is not only an important machine learning method, but also the basis of ensemble learning methods such as random forest and deep forest. Based on the theory of Formal Concept Analysis (FCA) and Attribute Partial Order Structure Diagram (APOSD), a new decision tree for classification is proposed in this paper. Firstly, the local minimum of Gini index is used to complete the data granulation, and the Formal Decision Mode Information Table (FDMIT) is constructed. Then, the Attribute Partial Order Classification Tree (APOCT) is generated based on APOSD to complete the pattern recognition and rule extraction. The method of APOCT separates the process of granulation and visualisation, and the granulation process is easy to parallelise and efficient. The experimental results show that APOCT is effective.

Knowledge discovery for spleen yang deficiency syndrome based on attribute partial order structure diagram

Syndromes and medications in Traditional Chinese Medicine (TCM) have been studied by advanced information technology in the modernisation of TCM, promoting the development of knowledge discovery in TCM. In this paper, based on the method of Attribute Partial Order Structure Diagram (APOSD), syndrome-symptom APOSD and prescription-herb APOSD are constructed for spleen yang deficiency syndrome, which is a common syndrome in TCM. The common symptoms and specific symptoms of spleen yang deficiency syndrome are extracted from the syndrome-symptom APOSD. The association rules among herbs in the prescriptions for treating spleen yang deficiency syndrome are visualised on the prescription-herb APOSD, and herb pairs and herb groups are extracted. Compared with Apriori algorithm, APOSD not only obtains important association rules, but also shows the compositions of prescriptions in the diagram. APOSD provides a new scientific research method for TCM.

Knowledge discovery for spleen yang deficiency syndrome based on attribute partial order structure diagram

Syndromes and medications in Traditional Chinese Medicine (TCM) have been studied by advanced information technology in the modernisation of TCM, promoting the development of knowledge discovery in TCM. In this paper, based on the method of Attribute Partial Order Structure Diagram (APOSD), syndrome-symptom APOSD and prescription-herb APOSD are constructed for spleen yang deficiency syndrome, which is a common syndrome in TCM. The common symptoms and specific symptoms of spleen yang deficiency syndrome are extracted from the syndrome-symptom APOSD. The association rules among herbs in the prescriptions for treating spleen yang deficiency syndrome are visualised on the prescription-herb APOSD, and herb pairs and herb groups are extracted. Compared with Apriori algorithm, APOSD not only obtains important association rules, but also shows the compositions of prescriptions in the diagram. APOSD provides a new scientific research method for TCM.

Formal concept attribute partial-order structure diagram and applications

Formal-Concept Attribute Partial-Order Structure Diagram (FC-APOSD) is proposed on the basis of formal concept analysis and Attribute Partial-Order Structure Diagram (APOSD) theory, which inherits the advantage of good visualisation of APOSD and can reveal relationships among formal concepts at the same time. This paper focuses on the mathematical description of FC-APOSD, how to use FC-APOSD to explore knowledge from a formal context and the applications of FC-APOSD. Specifically, our work mainly includes the following points: 1. FC-APOSD is described in mathematical formal language; 2. After giving a necessary and sufficient condition for an attribute set of a node to be the intension of a formal concept, a method to find pseudo-intensions using FC-APOSD is provided; 3. The relationship among objects is discussed using groups and subgroups in FC-APOSD; 4. A method to find exclusive attributes from a formal context is provided by using FC-APOSD; 5. With the help of classical Chinese medicinal formulae related to emotional diseases from 'Treatise on Febrile and Miscellaneous Diseases', the syndrome-treatment pattern of emotional diseases is explored using the FC-APOSD method.

Formal concept attribute partial-order structure diagram and applications

Formal-Concept Attribute Partial-Order Structure Diagram (FC-APOSD) is proposed on the basis of formal concept analysis and Attribute Partial-Order Structure Diagram (APOSD) theory, which inherits the advantage of good visualisation of APOSD and can reveal relationships among formal concepts at the same time. This paper focuses on the mathematical description of FC-APOSD, how to use FC-APOSD to explore knowledge from a formal context and the applications of FC-APOSD. Specifically, our work mainly includes the following points: 1. FC-APOSD is described in mathematical formal language; 2. After giving a necessary and sufficient condition for an attribute set of a node to be the intension of a formal concept, a method to find pseudo-intensions using FC-APOSD is provided; 3. The relationship among objects is discussed using groups and subgroups in FC-APOSD; 4. A method to find exclusive attributes from a formal context is provided by using FC-APOSD; 5. With the help of classical Chinese medicinal formulae related to emotional diseases from 'Treatise on Febrile and Miscellaneous Diseases', the syndrome-treatment pattern of emotional diseases is explored using the FC-APOSD method.

Comparison of support vector machine, back propagation neural network and extreme learning machine for syndrome element differentiation

Artificial intelligence is a discipline which focuses on the study of simulating and extending human intelligence, while machine learning (ML) is one of the most rapidly developing subfields of AI research. The research of ML in the field of traditional Chinese medicine (TCM), has a bright prospect, as well as a profound significance. To explore the feasibility of using ML methods to approximate the diagnosis of TCM, support vector machine (SVM) is introduced and investigated for syndrome element differentiation of TCM in this paper. Based on 670 medical records, SVM was used to approximate the mapping relations between inputs (set of clinical manifestations) and outputs (set of syndrome elements). The value orders of syndrome elements were adopted to evaluate the approximation results, while attribute partial order structure diagram was employed to discover and visualize the knowledge of the records. Back propagation neural network (BPNN) and extreme learning machine (ELM), as comparative methods, were also employed to deal with the medical data. The value order’s matching results between real and predicted results shows that, for SVM, the matched degree of each record is no less than 65%, while there are at least 88% records whose matched degree is more than 80%; and for BPNN and ELM, the highest proportion of records whose matched degree is more than 80% is only 45%. Using methods of ML to approximate the diagnosis of TCM should be feasible, and more relevant research can be conducted in the future.

Symptom-herb knowledge discovery based on attribute partial ordered structure diagrams

As a medical system with a long history, traditional Chinese medicine (TCM) owns extremely rich and valuable experience and knowledge. In this paper, the granulation-based attribute partial ordered structure diagram (APOSD) is proposed to achieve knowledge discovery for spleen Yang deficiency syndrome in TCM. APOSD is discussed with the perspective of granular computing. Each layer of APOSD is viewed as a level of granularity and the nodes in APOSD layers are viewed as information granules. Granulation based generation method of APOSD reduces the complexity of the algorithm and can extract more comprehensive information from multi-level and multi-scale. The proposed method is testified through analysis of prescriptions of spleen Yang deficiency syndrome screening from the comprehensive database of TCM. Symptom patterns, import herbs and their relationship are extracted from different layers and clusters in generated APOSD. Compared with commonly used Apriori association rule analysis algorithm, the method proposed in this paper can not only extract herbs with high confidence but also extract herbs with specific effect, and the association relationships can also be shown on the generated APOSD.

A research on syndrome element differentiation based on phenomenology and mathematical method

BackgroundAs an empirical medical system independent of conventional Western medicine (CWM), over thousands of years, traditional Chinese medicine (TCM) has established its own unique method of diagnosis and treatment. The perspective of holism and system in TCM is essentially different from the view of Reductionism in CWM. With the development of modern science and technology, the restriction of reductionism is more and more prominent, and researchers begin to pay more attention to holistic thinking in TCM. Confronted with the above situation, there is an urgent need to explore the diagnosis of TCM by the techniques of modern science.MethodsTo explore the feasibility of using modern science to describe and realize the diagnosis of TCM, in this paper, a method of syndrome element differentiation based on phenomenology is proposed. The proposed method is implemented by mathematical mapping, and then it is testified through analysis of 670 medical records: Based on the original mapping data between two data sets (set of syndrome elements and set of clinical manifestations), new mapping data is generated, and thus the corresponding quantitative diagnostic results are calculated and evaluated. Finally, knowledge discovery of the diagnosis results based on attribute partial-ordered structure diagram is conducted.ResultsThe value order’s matching results between original and new results show that the matched degree of each record is no less than 65%, while there are at least 87% records whose matched degree is more than 80%. In addition, the knowledge discoveries of new results are basically identical with the ones of original results as well.ConclusionUsing phenomenology to describe syndrome differentiation should be feasible, and further research on mapping relations between various sets (symptoms, formulas, drugs) of TCM should be conducted and evaluated through clinical trials in future.

A new approach of attribute partial order structure diagram for word sense disambiguation of English prepositions

To improve the accuracy of word sense disambiguation (WSD) has been a significant issue, and to visualize the structure of a dataset to discover knowledge has been an urgent demand in natural language processing. In order to fulfill these two tasks simultaneously, a new approach of attribute partial order structure diagram is proposed. The principle of attribute partial order and the approach of attribute partial order structure diagram are described. The proposed approach is testified by the WSD of the English preposition over, using the dataset from SemEval corpus. Two well-accepted sense inventories for fine-grained WSD of the English prepositions are adopted. The formal contexts for the fine-grained WSD of the English preposition over are established and the corresponding attribute partial order structure diagrams are generated and used as the models of WSD. The tested results show that the accuracies of WSD of over by the proposed approach are significantly higher than the ones by the state of the art system. Moreover, the proposed approach can visualize the attribute partial order structure of the dataset, which can be used for knowledge discovery.