A subject topic identification method and system integrating network structure characteristics

Abstract

The present invention discloses a method and system for identifying subject topics by integrating network structure features. The method comprises: extracting topics from a number of text data in a class of subjects to generate a subject data set; selecting a number of highly influential topics from the subject data set as an experimental set, and randomly selecting a number of topics as a control set; connecting related topics with topics as nodes to construct a subject network; calculating multi-dimensional features of the subject network of the experimental set and the subject network of the control set respectively; obtaining a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of the topic; dividing the subject data in the experimental set and the control set into a training set and a test set, respectively training a machine learning model to learn the features of the highly influential topics, and comparing the results output by the machine learning model with the results obtained by the influence ranking of the topics in the subject network to obtain the performance of the machine learning model. The present invention can improve the accuracy of subject identification.

Description

Technical Field

[0001] The present invention relates to the field of machine learning technology, and in particular to a subject topic identification method and system integrating network structure features. Background Art

[0002] Identifying emerging disciplinary topics is an effective method for timely identifying trends in scientific and technological innovation and monitoring disciplinary development trends. Currently, methods for identifying emerging disciplinary topics have formed a relatively stable research paradigm, forming a system of emerging topic identification methods based primarily on scientometric analysis and knowledge network analysis. Scientometric analysis primarily analyzes the metadata underlying disciplinary topics and calculates their emerging index by integrating multidimensional indicator characteristics. Knowledge network analysis primarily relies on citation networks based on scientific literature and co-word networks based on keywords to discover emerging document clusters and emerging keyword communities.

[0003] However, scientometric analysis methods require addressing the integration of multidimensional indicators, specifically determining the weights of each indicator. Currently, there is a lack of effective methods for characterizing the interactions between these indicators. Knowledge network analysis methods often use clustering to generate network communities to identify disciplinary themes, then identify emerging themes based on novelty and impact. However, this approach overlooks the position and structure of disciplinary themes within the knowledge network, factors that often reflect their inherent characteristics. Summary of the Invention

[0004] The present invention provides a subject topic identification method and system integrating network structural features, which can integrate the position and structural features of subject topics in a subject network, thereby improving the accuracy of subject identification.

[0005] According to one aspect of the present invention, a method for identifying subject topics by integrating network structure features is provided, comprising:

[0006] Acquire a number of text data of a subject, extract themes from the text data, and generate a theme data set;

[0007] Selecting several highly influential topics from the topic dataset as an experimental set, and randomly selecting several topics from the topic dataset as a control set, wherein the start and end time of the publication of the text data corresponding to the topics in the control set and the topics in the experimental set are the same;

[0008] Taking topics as nodes, connecting related topics, and constructing topic networks for the experimental set and the control set within a preset time period;

[0009] Calculating the multi-dimensional features of the experimental set topic network and the control set topic network respectively;

[0010] Obtaining a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of the topics in the topic network of the experimental set and the influence ranking of the topics in the control set;

[0011] The topic data in the experimental set and the control set are divided into a training set and a test set, which are used to train machine learning models respectively to learn the characteristics of high-influence topics. The results output by the machine learning model are compared with the results obtained by ranking the topic influence in the topic network to obtain the performance of the machine learning model.

[0012] Optionally, the step of selecting several highly influential topics from the topic dataset as an experimental set and randomly selecting several topics from the topic dataset as a control set, wherein the start and end time of publication of the text data corresponding to the topics in the control set and the topics in the experimental set are the same, includes:

[0013] Selecting several highly influential topics from the topic dataset as experimental sets;

[0014] A random matching algorithm is used to generate topics with the same start and end time of the text data publication as the topics in the experimental set as a control set.

[0015] Optionally, the step of obtaining a plurality of text data of a subject and extracting topics from the text data includes:

[0016] A plurality of text data of a subject are obtained, and an edit distance algorithm is used to perform topic coding on knowledge entities in the text data to obtain a plurality of coded topics.

[0017] Optionally, taking topics as nodes, related topics are connected to construct topic networks of the experimental set and the control set within a preset time period, including:

[0018] Taking topics as nodes and the mean of the probabilities of any two topics appearing in the same text data as the length of the edge connecting the two topics, two related topics are connected to construct a topic network of the topic data in the experimental set and the control set within a preset time period.

[0019] Optionally, the multi-dimensional features include at least the number of topic-related papers published, the influence of authors focusing on the topic, the frequency of citations of the topic, the influence of journals carrying the topic, network centrality, network centrality change rate, network PageRank, network PageRank change rate and the degree of network structure mutation.

[0020] Optionally, the degree of network structure mutation is calculated by the WL subtree kernel algorithm to calculate the similarity between subtrees in the topic network structure diagram, thereby measuring the degree of mutation of the topic. The calculation formula is as follows:

[0021] S i (tn,t)=k WL (G i,t-n ,G i,t )

[0022] Where t represents time, n represents time span; S i (tn,t) represents the mutation degree of the topic network from time tn to time t; k WL (G i,t-n ,G i,t ) is the WL subtree kernel algorithm used to calculate the topic network G i,t-n and G i,t structural similarity.

[0023] Optionally, obtaining a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of topics in the topic network of the experimental set and the influence ranking of topics in the control set includes:

[0024] At least the TOPSIS method, the grey correlation method, the entropy weight method, and the rank sum ratio method are used to process the first multi-dimensional features and the second multi-dimensional features, thereby obtaining a comprehensive score for each topic;

[0025] The topic samples were ranked by influence based on the comprehensive scores, and the proportion of high-impact topics was obtained based on the Top-n ranking.

[0026] Optionally, dividing the topic data in the experimental set and the control set into a training set and a test set, respectively training a machine learning model to learn the characteristics of high-influence topics, and comparing the results output by the machine learning model with the results obtained by ranking the topics in the topic network to obtain the performance of the machine learning model, includes:

[0027] The topic samples in the experimental set are marked as 1, and the topic samples in the control set are marked as 0, and a five-fold cross-validation strategy is used to train multiple machine learning models respectively to learn the characteristics of high-impact topics;

[0028] A variety of classification model performance evaluation indicators are used to evaluate the classification results of multiple machine learning models, and the evaluation results are compared with the results obtained by ranking the topic influence in the topic network to obtain the performance of each machine learning model, and the optimal machine learning model is selected as the subject topic recognition model.

[0029] Optionally, the machine learning model includes at least logistic regression, support vector machine, decision tree, K-nearest neighbor, random forest and gradient boosting, multi-layer perceptron and TabNet model.

[0030] According to another aspect of the present invention, a subject topic identification system integrating network structure features is provided, comprising:

[0031] A topic extraction module is used to obtain a number of text data of a certain subject, extract the topics in the text data, and generate a topic dataset;

[0032] A topic selection module is used to select several highly influential topics from the topic dataset as an experimental set, and randomly select several topics from the topic dataset as a control set, wherein the start and end time of the publication of the text data corresponding to the topics in the control set and the topics in the experimental set are the same;

[0033] A network construction module is used to connect related topics using topics as nodes, and respectively construct a topic network of the experimental set and the control set within a preset time period;

[0034] A feature calculation module, used to calculate the multi-dimensional features of the experimental set topic network and the control set topic network respectively;

[0035] An influence calculation module, configured to obtain a comprehensive score for each topic based on the multi-dimensional features, so as to determine the influence ranking of the topics in the topic network of the experimental set and the influence ranking of the topics in the control set;

[0036] A training module is used to divide the topic data in the experimental set and the control set into a training set and a test set, and to train a machine learning model to learn the characteristics of high-influence topics, and to compare the results output by the machine learning model with the results obtained by ranking the topic influence in the topic network to obtain the accuracy of the machine learning model.

[0037] According to another aspect of the present invention, an electronic device is provided, comprising:

[0038] at least one processor; and

[0039] a memory communicatively connected to the at least one processor; wherein,

[0040] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the subject topic identification method integrating network structure features described in any embodiment of the present invention.

[0041] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the subject topic identification method integrating network structure features described in any embodiment of the present invention when executed.

[0042] The technical solution of the embodiment of the present invention constructs a topic network according to the correlation between topics to ensure that the connection strength between topics in the network is based on the actual co-occurrence of documents; calculates the multi-dimensional characteristics of the topic network, and adopts multiple methods to obtain the comprehensive score of each topic based on the multi-dimensional characteristics to determine the influence ranking of the topic; and trains a machine learning model to learn the characteristics of high-influence topics, compares the results output by the machine learning model with the results obtained by the influence ranking of topics in the topic network to obtain the accuracy of the machine learning model, and then selects the best machine learning model to identify subject topics. The present invention can integrate the position and structural characteristics of subject topics in the topic network, thereby improving the accuracy of topic identification.

[0043] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a flow chart of a subject topic identification method integrating network structure features provided in accordance with the first embodiment of the present invention;

[0046] Figure 2 This is a flow chart of a subject topic identification method integrating network structure features provided in accordance with the second embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of a subject topic identification method integrating network structure features provided in accordance with the second embodiment of the present invention;

[0048] Figure 4 Schematic diagram of the proportion of high-influence topics obtained by ranking topics by influence using four ranking methods in Example 2 of the present invention;

[0049] Figure 5 This is a system architecture diagram of a subject topic identification system integrating network structure features provided in the third embodiment of the present invention;

[0050] Figure 6It is a structural diagram of an electronic device for implementing a subject topic identification method integrating network structure features according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0053] Example 1

[0054] Figure 1 A flowchart of a subject topic identification method integrating network structure features is provided for the first embodiment of the present invention. Figure 1 As shown, the method includes:

[0055] S101. Acquire a number of text data of a subject, extract themes from the text data, and generate a theme data set.

[0056] A category of disciplines may include fields such as medicine, semiconductors, communications, and chemical materials; text data may include literature data or patent data. In this embodiment, several text data from a category of disciplines can be randomly selected as experimental samples, and several disciplinary themes can be extracted from the text data. The themes can be currently popular or general topics, such as tumor immunity, targeted therapy, and immunotherapy. The extracted themes are used as the experimental subject data set.

[0057] S101. Select several highly influential topics from the topic data set as an experimental set, and randomly select several topics from the topic data set as a control set, wherein the text data corresponding to the topics in the control set and the topics in the experimental set have the same publication start and end time.

[0058] Based on the current popular topics, several popular topics can be selected from the topic data set as high-influence topics to form an experimental set; and several topics can be randomly selected from the topic data set as a control set. It should be noted that the publication start and end time of the text data of all topics in the control set should be the same as the publication start and end time of the text data of the topics in the experimental set, ensuring that the experimental set and the control set are the same in time, so that the multi-dimensional features of the experimental set obtained subsequently are comparable with the multi-dimensional features of the control set, thereby verifying the accuracy and effectiveness of the topic identification algorithm of this scheme.

[0059] S102 , using topics as nodes, connecting related topics to construct topic networks for the experimental set and the control set within a preset time period.

[0060] For the experimental set and the control set, topic networks for the experimental set and the control set can be constructed using topic data, respectively. In this embodiment, the topics in the experimental set can be used as nodes, and the related topics can be connected pairwise based on the associations between the topics, thereby forming the topic network for the experimental set. Similarly, the control set is constructed using the same method as the experimental set topic network.

[0061] Since the degree of association between two topics in the experimental set or the control set is different, the length of the connecting line between the two topics can be determined according to the degree of association, so that the lengths of the connecting lines between the topics in the obtained topic network are different.

[0062] It should be noted that the preset time period can be one year, two years, or other time settings. In this embodiment, the topic network of the experimental set or the control set can be obtained according to the preset time period. For example, a topic network of the topic structure corresponding to the text data from 2000 to 2010 in the experimental set and the control set can be obtained with a preset time period of 10 years, and the topic network of the topic structure corresponding to the text data from 2010 to 2020 in the experimental set and the control set can be obtained respectively, so as to be used for subsequent comparison of the multi-dimensional features of the topic network of the experimental set and the control set in the same time period.

[0063] S103 , respectively calculating the multi-dimensional features of the experimental set topic network and the control set topic network.

[0064] Multidimensional features can include external attribute features of a topic and topic network features. External features can include features related to the influence of the text data corresponding to the topic; topic network features can include the position and structural features of the topic in the topic network; the combination of the external features of the topic and the multidimensional features composed of the topic network features can reflect the influence of the topic. In this embodiment, the influence of the topic can be quantified. For example, the mean and standard deviation of the multidimensional features can be calculated separately, and the mean and standard deviation can be used to reflect the influence of the topic.

[0065] S104. Obtain a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of the topics in the topic network of the experimental set and the influence ranking of the topics in the control set.

[0066] In this embodiment, a preset method can be used to calculate the quantitative results of the multi-dimensional features of each topic in the experimental set and the control set respectively to rank the topics, thereby obtaining a ranking of the influence of the topics in the experimental set and the control set. For example, a ranking method such as the TOPSIS method, the grey correlation method, the entropy weight method, and the rank sum ratio method can be used.

[0067] S105. Divide the topic data in the experimental set and the control set into a training set and a test set, respectively for training machine learning models to learn the characteristics of high-influence topics, and compare the results output by the machine learning model with the results obtained by ranking the topic influence in the topic network to obtain the performance of the machine learning model.

[0068] The experimental set data and the control set data in step S101 can be used as data sets to train the machine learning model, thereby ensuring that the ranking result in step S104 is comparable with the result output by the machine learning model.

[0069] Among them, the method of dividing the subject data in the experimental set and the control set into a training set and a test set can be to mix the experimental set and the control set as a sample set, and make different labels on the subject data in the experimental set and the subject data in the control set respectively, randomly extract a certain proportion of the subject data from the sample set as a training set, and randomly extract another proportion of the subject data from the sample set as a test set.

[0070] By comparing the topic results output by the machine learning model with the topic ranking results obtained by ranking the topic influence in the topic network, the accuracy of the identification results of high-influence topics can be determined.

[0071] The technical solution of the embodiment of the present invention constructs a topic network according to the correlation between topics to ensure that the connection strength between topics in the network is based on the actual co-occurrence of documents; calculates the multi-dimensional characteristics of the topic network, and adopts multiple methods to obtain the comprehensive score of each topic based on the multi-dimensional characteristics to determine the influence ranking of the topic; and trains a machine learning model to learn the characteristics of high-influence topics, compares the results output by the machine learning model with the results obtained by the influence ranking of topics in the topic network to obtain the accuracy of the machine learning model, and then selects the best machine learning model to identify subject topics. The present invention can integrate the position and structural characteristics of subject topics in the topic network, thereby improving the accuracy of topic identification.

[0072] Example 2

[0073] Figure 2 A subject topic identification method integrating network structure features is provided in the second embodiment of the present invention. Figure 2 Included are:

[0074] S201. Acquire a plurality of text data of a subject, and use an edit distance algorithm to subject-code knowledge entities in the text data to obtain a plurality of coded subjects.

[0075] In this embodiment, the biomedical discipline can be used as the experimental scenario, and the metadata of the entire PubMed (medical database) library can be parsed. Mainly based on the high-impact documents of Nature and Science, as well as the Nobel Prize-winning achievements in the field of biomedicine, the text data of 165 highly influential journal articles are finally obtained. The domain knowledge descriptors in the MeSH (Medical Subject Headings) knowledge base can be used to calculate the similarity between knowledge entities in the text data through the edit distance algorithm, and then the 14,741,948 knowledge entities in the highly influential documents are subject-coded. The metadata of the entire PubMed library is parsed to obtain highly influential topics. The purpose of this parsing is to identify topics that have not yet been included in MeSH, and to support subsequent thematic network analysis, and ultimately obtain effective thematic coding and network structure data. Among them, the edit distance algorithm is a method for measuring the similarity between two character strings, defining the minimum number of single-character editing operations required to convert from one string to another, which include inserting, deleting or replacing a character.

[0076] Mesh Medical Subject Headings (MeSH) is an authoritative subject heading list and the most commonly used standard medical subject heading list in the biomedical field.

[0077] S202. Select several highly influential topics from the topic data set as an experimental set; and use a random matching algorithm to generate topics with the same publication start and end time as the text data corresponding to the topics in the experimental set as a control set.

[0078] In this embodiment, 165 high-quality biomedical research results were screened and 231 high-quality research topics were finally selected as the experimental set. In order to verify the identification method of emerging disciplinary topics, another control set was generated by a random matching method to keep the start and end time of the text data corresponding to the topic the same. The random matching process is to combine a control group with the same time range from different literature libraries based on the starting time of the experimental set and the same relevance standard, thereby ensuring the comparability of the experimental group and the control group in terms of time and topic characteristics, and then verifying the accuracy and effectiveness of the identification method.

[0079] S203. Taking topics as nodes and the mean of the probabilities of any two topics appearing in the same text data as the length of the edge connecting the two topics, two related topics are connected, thereby constructing a topic network of the topic data in the experimental set and the control set within a preset time period.

[0080] In this embodiment, the coded topics can be used as nodes, and the co-occurrence relationship between two related topics in the same document can be used as edges to construct a topic co-occurrence network. The specific connection line construction method includes normalizing the co-occurrence edge weights: first, the probability of the tagged topics T1 and T2 appearing in the same text data is calculated, which are respectively recorded as P(T1|T2) and P(T2|T1). These two probabilities represent the ratio of the tagged topics T1 and T2 appearing in the document at the same time. Then, the weight of the edge is expressed by calculating the mean of the two probabilities, which is reflected in the topic network as the length of the two topic edges. The structure of the subject topic network finally obtained can be as follows: Figure 3 The network structure shown in the "Discipline Topic Network" in .

[0081] This approach ensures that the strength of connections between nodes (topics) in the network is based on actual document co-occurrence. Since each topic network corresponds to a different time period, the dynamic topic network displayed in time series can be used to analyze the changes in topic networks over different time periods, thereby quantifying the relationships between topics and their evolutionary trends in the network.

[0082] S204 , respectively calculating the multi-dimensional features of the experimental set topic network and the control set topic network.

[0083] In this embodiment, the multi-dimensional features include at least the number of papers published on the topic, the influence of authors focusing on the topic, the frequency of citations of the topic, the influence of journals carrying the topic, network centrality, network centrality change rate, network PageRank, network PageRank change rate and the degree of network structure mutation.

[0084] Among them, the number of topic-related papers published, the influence of authors focusing on the topic, the frequency of citations of the topic, and the influence of journals carrying the topic are the external attribute characteristics of the topic, which are used to reflect the influence of the literature on the topic; network centrality, network centrality change rate, network PageRank, network PageRank change rate and network structure mutation degree are the topic network characteristics, which are used to reflect the position relationship of the topic in the topic network.

[0085] Specifically, the number of papers published related to a topic reflects the research activity of the topic in the academic literature and represents the extent to which the topic has been studied in the literature. Secondly, the influence of the topic's authors is evaluated based on the number of citations by the lead author and academic influence indicators (such as the h-index). The citation frequency of the topic is calculated by counting the citations of papers related to the topic in other literature to measure the influence and recognition of the topic in the academic community. Furthermore, the influence of the journals underlying the topic is analyzed by analyzing indicators such as the impact factor of the journals in which the relevant papers are published, reflecting the authority and importance of the journals in the academic community. The centrality of the topic in the network is used to assess the importance and connectivity of each topic in the topic co-occurrence network. A higher centrality indicates a more prominent core position of the topic in the network. Secondly, the rate of change of the topic network centrality over different time periods is calculated to reflect the magnitude of the change in the centrality of the topic within a specific period, thereby revealing whether the relative influence of the topic has fluctuated significantly over time. Finally, the PageRank change rate of the topic network is calculated. The PageRank algorithm calculates the influence of a topic, and its degree of change reflects the diffusion rate of the topic's influence. Finally, the degree of structural mutation of the topic network is calculated by comparing the degree of structural evolution of the egocentric network at different times.

[0086] Among them, the degree of network structure mutation is calculated by the WL subtree kernel algorithm to calculate the similarity between each subtree in the topic network structure diagram, and then measure the degree of mutation of the topic. The calculation formula is as follows:

[0087] S i (tn,t)=k WL (G i,t-n ,G i,t )

[0088] Where t represents time, n represents time span; S i(tn,t) represents the mutation degree of the topic network from time tn to time t; k WL (G i,t-n ,G i,t ) is the WL subtree kernel algorithm used to calculate the topic network G i,t-n and G i,t structural similarity.

[0089] Based on the obtained characteristic values ​​of each topic, such as the number of topic-related papers published, the influence of authors focusing on the topic, the frequency of citations of the topic, the influence of the journal carrying the topic, network centrality, network centrality change rate, network PageRank, network PageRank change rate, and the degree of network structure mutation, the mean and standard deviation of each characteristic value for each topic in the experimental and control sets can be calculated. Statistical analysis can also be performed on the mean and standard deviation of the characteristic values ​​for each topic in the experimental and control sets.

[0090] S205. Use at least TOPSIS method, grey correlation method, entropy weight method, and rank sum ratio method to process the first multidimensional features and the second multidimensional features, and then obtain a comprehensive score for each topic.

[0091] Specifically, the TOPSIS method calculates the distance between each topic's multi-dimensional features and the ideal solution and the negative ideal solution to derive the relative closeness of each topic and rank them based on the relative closeness. The grey correlation method calculates the correlation coefficient between each topic and the reference sequence to derive the grey correlation degree and then rank them. The entropy weight method determines the weight by calculating the information entropy of each feature of each topic and ranks the topics based on the weighted comprehensive score. The rank sum ratio method ranks and standardizes each feature of each topic to ultimately calculate the total score of each topic.

[0092] S206. The topic samples are ranked by influence based on the comprehensive scores, and the proportion of high-impact topics is determined based on the Top-n ranking.

[0093] Specifically, through the comprehensive scores of these four methods, the influence of the subject samples in all experimental sets and control sets was ranked, and the proportion of high-impact topics was obtained based on the Top-n ranking. Figure 4 The results of the proportion of high-influence topics obtained by ranking the topics by influence using four ranking methods show that the TOPSIS method and the grey correlation method perform particularly well in identifying high-influence topics, especially at P@5 and P@10 (the accuracy of the top 5 and top 10 items searched), which can identify more than 90% of high-influence topics, while the entropy weight method and the rank sum ratio method are relatively weak in performance.

[0094] S207. Mark the topic samples in the experimental set as 1, mark the topic samples in the control set as 0, and use a five-fold cross-validation strategy to train multiple machine learning models separately to learn the characteristics of high-impact topics.

[0095] It should be noted that the experimental group and control group constructed in step S202 are divided into training sets and test sets, and the subject samples in the control set are marked as negative samples, that is, marked as "0", and the subject samples in the experimental set are marked as positive samples, that is, marked as "1", which can be abstracted as a binary classification task of machine learning.

[0096] In addition, in order to improve the robustness of the model and prevent overfitting, this embodiment can use a 5-fold crossover strategy to divide the training set data into 5 equal parts, use 4 equal parts for model training each time, and use the remaining 1 part as a test set. The same machine learning model is trained 5 times to ensure that each subset is used as a test set. Finally, the performance of the model is evaluated by averaging the results of 5 experiments.

[0097] Among them, machine learning models can use eight classification algorithm models, including logistic regression (Linear), support vector machine (SVM), decision tree (DecisionTree), K-nearest neighbor (KNeighbors), random forest (RandomForest), gradient boosting (GradientBoosting), multi-layer perceptron and TabNet. These models are trained using a training set, and the choice of loss function varies depending on the model. For example, logistic regression and support vector machine models use cross-entropy loss (Cross-Entropy Loss) to minimize classification error, while decision tree and random forest split nodes using the Gini index or information gain. For gradient boosting models, the loss function used is mean squared error (MSE) or log-likelihood loss to improve the prediction effect of the tree.

[0098] S208. Use multiple classification model performance evaluation indicators to evaluate the classification results of multiple machine learning models, compare the evaluation results with the results obtained by ranking the topic influence in the topic network to obtain the performance of each machine learning model, and select the optimal machine learning model as the subject topic recognition model.

[0099] After the model training was completed, multiple classification model performance evaluation indicators were used to evaluate the performance of 8 binary classification models in the high-impact topic identification task. These evaluation indicators include accuracy, precision, recall, and standard F1 value (F1 Score), which comprehensively evaluate the performance of each model in different evaluation dimensions. As shown in Table 1 below, the classification results of the 8 machine learning models under the four indicators show that for the main data of the medical topic selected in this embodiment, the performance indicators under the random forest model are better than those of other recognition models. By comparing these indicators, the best performing model can be effectively identified, providing a more accurate tool for the identification of high-impact topics.

[0100] Table 1 Performance evaluation results of topic identification model

[0101] Classification Model Accuracy (Acc) Precision Recall F1 value Logistic regression 65.17 / 65.11 67.20 / 66.11 49.00 / 49.51 55.95 / 56.18 Support Vector Machine 62.71 / 62.88 63.67 / 67.46 46.99 / 39.58 53.44 / 48.78 Decision Tree 64.79 / 67.92 62.53 / 67.92 58.42 / 59.74 60.34 / 62.90 K adjacent 62.17 / 60.64 60.98 / 59.68 51.63 / 46.94 55.73 / 52.15 Random Forest 68.16 / 68.48 66.73 / 67.67 62.77 / 60.67 64.47 / 63.81 GBoosting 62.92 / 66.97 60.79 / 65.61 57.58 / 61.16 58.82 / 63.14 MLP 64.41 / 65.48 66.70 / 66.60 48.42 / 51.26 55.66 / 57.48 TabNet 62.56 / 61.57 60.82 / 58.25 58.13 / 59.22 58.86 / 58.76

[0102] Example 3

[0103] Figure 5 This is a structural diagram of a subject topic identification system that integrates network structure features provided by the third embodiment of the present invention. Figure 5 As shown, the device includes:

[0104] The subject extraction module 501 is used to obtain a number of text data of a subject, extract the subject in the text data, and generate a subject data set;

[0105] A topic selection module 502 is configured to select several highly influential topics from the topic dataset as an experimental set, and randomly select several topics from the topic dataset as a control set, wherein the topics in the control set and the topics in the experimental set have the same publication start and end time for the text data;

[0106] A network construction module 503 is configured to connect related topics using topics as nodes, and to construct a topic network for the experimental set and the control set within a preset time period.

[0107] A feature calculation module 504 is used to calculate the multi-dimensional features of the experimental set topic network and the control set topic network respectively;

[0108] An influence calculation module 505 is configured to obtain a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of the topics in the topic network of the experimental set and the influence ranking of the topics in the control set;

[0109] The training module 506 is used to divide the topic data in the experimental set and the control set into a training set and a test set, and to train the machine learning model to learn the characteristics of high-influence topics, and to compare the results output by the machine learning model with the results obtained by ranking the topic influence in the topic network to obtain the accuracy of the machine learning model.

[0110] The message data processing resource scheduling device provided by the embodiment of the present invention can execute the message data processing resource scheduling device method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0111] Example 4

[0112] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0113] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0114] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0115] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a subject topic identification method that integrates network structure features.

[0116] In some embodiments, a method for identifying a subject matter by integrating network structural features may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for identifying a subject matter by integrating network structural features described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute a method for identifying a subject matter by integrating network structural features in any other appropriate manner (e.g., by means of firmware).

[0117] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0118] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0119] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0121] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0122] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0123] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0124] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A subject topic identification method integrating network structure features, characterized in that: include: Acquire a number of text data of a subject, extract themes from the text data, and generate a theme data set; Selecting several highly influential topics from the topic dataset as an experimental set, and randomly selecting several topics from the topic dataset as a control set, wherein the start and end time of the publication of the text data corresponding to the topics in the control set and the topics in the experimental set are the same; Taking topics as nodes, connecting related topics, and constructing topic networks for the experimental set and the control set within a preset time period; Calculating the multi-dimensional features of the experimental set topic network and the control set topic network respectively; The experimental set topic network corresponds to the first multi-dimensional feature; The control set topic network corresponds to the second multi-dimensional feature; Obtaining a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of the topics in the topic network of the experimental set and the influence ranking of the topics in the control set; The topic data in the experimental set and the control set are divided into a training set and a test set, which are used to train machine learning models respectively to learn the characteristics of high-influence topics. The results output by the machine learning model are compared with the results obtained by ranking the topic influence in the topic network to obtain the performance of the machine learning model.

2. The subject identification method integrating network structure features according to claim 1 is characterized in that: The method comprises selecting several highly influential topics from the topic dataset as an experimental set, randomly selecting several topics from the topic dataset as a control set, wherein the start and end time of publication of the text data corresponding to the topics in the control set and the topics in the experimental set are the same, including: Selecting several highly influential topics from the topic dataset as experimental sets; A random matching algorithm is used to generate topics with the same start and end time of the text data publication as the topics in the experimental set as a control set.

3. The subject identification method integrating network structure features according to claim 1 is characterized in that: The step of obtaining a plurality of text data of a subject and extracting topics from the text data includes: A plurality of text data of a subject are obtained, and an edit distance algorithm is used to perform topic coding on knowledge entities in the text data to obtain a plurality of coded topics.

4. The subject identification method integrating network structure features according to claim 1 is characterized in that: Taking topics as nodes, related topics are connected to construct topic networks of the experimental set and the control set within a preset time period, including: Taking topics as nodes and the mean of the probabilities of any two topics appearing in the same text data as the length of the edge connecting the two topics, two related topics are connected to construct a topic network of the topic data in the experimental set and the control set within a preset time period.

5. The subject identification method integrating network structure features according to claim 1 is characterized in that: The multi-dimensional features include at least the number of papers published on the topic, the influence of authors focusing on the topic, the frequency of citations of the topic, the influence of journals carrying the topic, network centrality, network centrality change rate, network PageRank, network PageRank change rate and the degree of network structure mutation.

6. The subject identification method integrating network structure features according to claim 5 is characterized in that: The degree of network structure mutation is calculated by the WL subtree kernel algorithm to calculate the similarity between the subtrees in the topic network structure diagram, and then measure the degree of mutation of the topic. The calculation formula is as follows: Among them, t represents time and n represents time span; Indicates the degree of mutation of the topic network from time tn to time t; To calculate the topic network using the WL subtree kernel algorithm and structural similarity.

7. The subject identification method integrating network structure features according to claim 1 is characterized in that: Obtaining a comprehensive score for each topic based on the multi-dimensional features to determine the influence ranking of topics in the topic network of the experimental set and the influence ranking of topics in the control set includes: At least the TOPSIS method, the grey correlation method, the entropy weight method, and the rank sum ratio method are used to process the first multi-dimensional features and the second multi-dimensional features, thereby obtaining a comprehensive score for each topic; The topic samples were ranked by influence based on the comprehensive scores, and the proportion of high-impact topics was obtained based on the Top-n ranking.

8. The subject identification method integrating network structure features according to claim 1 is characterized in that: The subject data in the experimental set and the control set are divided into a training set and a test set, respectively used to train a machine learning model to learn the characteristics of high-influence topics, and the results output by the machine learning model are compared with the results obtained by ranking the topics in the topic network to obtain the performance of the machine learning model, including: The topic samples in the experimental set are marked as 1, and the topic samples in the control set are marked as 0, and a five-fold cross-validation strategy is used to train multiple machine learning models respectively to learn the characteristics of high-impact topics; A variety of classification model performance evaluation indicators are used to evaluate the classification results of multiple machine learning models, and the evaluation results are compared with the results obtained by ranking the topic influence in the topic network to obtain the performance of each machine learning model, and the optimal machine learning model is selected as the subject topic recognition model.

9. The subject identification method integrating network structure features according to claim 1 is characterized in that: The machine learning models include at least logistic regression, support vector machine, decision tree, K-nearest neighbor, random forest and gradient boosting, multi-layer perceptron and TabNet models.

10. A subject topic identification system integrating network structure features, characterized in that: include: A topic extraction module is used to obtain a number of text data of a certain subject, extract the topics in the text data, and generate a topic dataset; A topic selection module is used to select several highly influential topics from the topic dataset as an experimental set, and randomly select several topics from the topic dataset as a control set, wherein the start and end time of the publication of the text data corresponding to the topics in the control set and the topics in the experimental set are the same; A network construction module is used to connect related topics using topics as nodes, and respectively construct a topic network of the experimental set and the control set within a preset time period; A feature calculation module, used to calculate the multi-dimensional features of the experimental set topic network and the control set topic network respectively; The experimental set topic network corresponds to the first multi-dimensional feature; The control set topic network corresponds to the second multi-dimensional feature; An influence calculation module, configured to obtain a comprehensive score for each topic based on the multi-dimensional features, so as to determine the influence ranking of the topics in the topic network of the experimental set and the influence ranking of the topics in the control set; A training module is used to divide the topic data in the experimental set and the control set into a training set and a test set, and to train a machine learning model to learn the characteristics of high-influence topics, and to compare the results output by the machine learning model with the results obtained by ranking the topic influence in the topic network to obtain the accuracy of the machine learning model.

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