Semantic Similarity Algorithm Research Articles

Role Analysis of the Construction and Application of Folk Dance Knowledge Graph in Cultural Transmission

Abstract This paper first introduces ontology construction, knowledge extraction, and graph database in the process of knowledge graph construction based on folk dance. Secondly, the semantic similarity algorithm is used as the basis to construct the conceptual similarity (MD4) model, and the five types of similarity algorithms in the MD4 model are introduced. After that, based on this model, we will conduct a comparative study on the number of folk dance publications, keywords, and hotspots in China and abroad. Finally, suggestions are made for creating a knowledge graph that is based on folk dance. The annual volume of folk dance research published in China is generally increasing. In contrast, the amount of foreign publications on folk dance is generally low, and its maximum number of publications is only 67. In China, 1021 keywords finally got 156 nodes, and the total link strength of the total number of occurrences was 3942. 1352 keywords finally got 137 nodes, and the total link strength of the total number of occurrences was 1983. Among all the keywords in China, the top three co-occurrence-ranked keywords and hot words were folk dance, value, and development status, with a number of 817, 633, and 607, respectively. The foreign countries include folk dance (453), value (233), and performance (146). The cultural inheritance of folk dance can be greatly impacted by the knowledge map’s ability to summarize the development of folk dance.

Semantic Network Analysis and Optimization Path Research on Medical English Terminology

Abstract Specialized terms are often longer words in medical English, and how to improve the effective memorization of specialized terms has become a research hotspot in medical English learning. The article proposes a method for creating a semantic network model for medical English terms using CRFs and the ATT-BiLSTM model. Firstly, on the basis of combing the characteristics and methods of medical English terminology composition, the CRF model is introduced to recognize the named entities of medical English terms and combined with the Viterbi decoding algorithm to obtain the lexical annotation result sequence of medical English terms. Secondly, an entity-relationship extraction model for medical English terms is established by combining the attention mechanism with the BiLSTM model, and ELMo vectors are added to improve the extraction efficiency of medical English terms. Finally, a semantic network model of medical English terms was constructed based on the Neo4j database by combining the named entity recognition and entity relationship extraction model, and the semantic network analysis was carried out by the semantic similarity algorithm based on information content. The results show that the CRFs model only needs a 14.2MB training corpus to co-represent the contextual information of medical English terms, and its F1 value is maximally improved by 10.22% on average after adding ELMo vectors to the ATT-BiLSTM model. The mean semantic similarity of medical English terms in the semantic network model is 1.391, and the result of the semantic similarity assessment is 0.803. Relying on the semantic network model can help learners understand the differences between medical English terms, find more similar terms to elucidate the standard terminology, and improve learners’ comprehension of medical English terms.

Reform and Practice of Civic Teaching in Design Curriculum by Integrating Local Excellent Culture

Abstract To scientifically constitute the reform of Civics teaching in design course, the support structure of Civics network resources in design course is subdivided into Civics theory and network technology. This paper mainly utilizes fuzzy set-based knowledge fusion algorithm and semantic similarity algorithm to realize the knowledge fusion of local cultural resources course ideology and politics, updates local artistic resources by combining the results of fusion of knowledge elements, establishes implicit connection between different knowledge elements, and realizes the knowledge fusion of local cultural resources and design studies course ideology and politics. Taking the design majors of six universities in Guangxi as an example, the information access channels of local excellent cultural resources are discussed in depth, in which the proportion of search engine and social network channels respectively reaches more than 70%, presenting a “strong” dependence level. Based on the data of practical teaching reform, the construction of local excellent culture courses of design majors in Guangxi colleges and universities needs to be further improved to deepen students’ understanding of local excellent culture and encourage them to actively participate in relevant practical activities.

The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunologyand cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

MA-STS-Based Social Intimacy Analysis Algorithm Using Real Campus Network Data

In recent years, the widespread availability of Wi-Fi in various settings, including universities, enterprises, and large shopping centers, has become increasingly prevalent. The user’s time and location information embedded in wireless network systems can reveal individual and group social relationships, which indirectly reflect each person’s psychological well-being. However, due to challenges in obtaining complete data, the high complexity of related data, and the absence of suitable data analysis models, few studies have analyzed student social behavior using data from university campus networks. This paper employs real-world data from a renowned Chinese university’s wireless campus network for in-depth analysis and introduces a novel multiangle semantic trajectory similarity (MA-STS) algorithm to infer the intimacy and relationship types (such as teacher-student, friends, classmates, or romantic partners) between users. The experiments demonstrate that the proposed algorithm achieves an accuracy of over 95%.

The System of the Dissemination Characteristics of Internet Public Opinion Big Data Based on Artificial Intelligence

In the era we live in today, the network is often used to analyze a large number of complex systems. With the development of the information society, there are more and more ways to disseminate public information through social networks. Public opinion dissemination refers to the process of disseminating public opinion information through social networks. Because the dissemination of public opinion is the basis for the exchange of ideas among multiple communicators of public opinion, the network community will certainly have an impact on the dissemination and development of public opinion. This article is based on artificial intelligence to study the network public opinion big data dissemination characteristic analysis system, introduces the network public opinion analysis system based on the characteristics of the network public opinion, introduces in detail multiple methods and clustering algorithms for extracting the text information of Internet public opinion, and proposes the Kmeans + Canopy + semantic similarity algorithm, and uses the A event to compare the parameters of the network clustering coefficient, the correlation measure and the degree centrality measure, and the performance of the Kmeans + Canopy algorithm and the Kmeans + Canopy + semantic similarity algorithm. The results of the experiment found that the clustering coefficient of “People’s Daily” in the network dissemination of A event was 0.038, which was the highest among all nodes. It shows that 3.8% of the nodes established by the “People’s Daily” can interact one-to-one to deliver information and intelligence resources. Although the complexity of the algorithm has increased and the time consumed by the system has increased, the accuracy of clustering has been improved, especially for cultural articles, the accuracy rate has been as high as 75%, and entertainment articles can reach up to 70%, and stabilize at around 70%.

Feature extraction of search product based on multi-feature fusion-oriented to Chinese online reviews

The increasing Chinese online reviews contain rich product demand information, especially for search products. This study suggests a product feature extraction model from online reviews based on multi-feature fusion named PFEMF (products features extraction based on multi-feature fusion) model. Combining sentence and word characteristics of Chinese online reviews, the model explores the lexical features, frequency features, span features, and semantic similarity features of words. And then, they are fused to identify the features that customers are concerned about most by sequential relationship analysis. The identified product feature provides direction for product innovation and facilitates the product selection for customers. Finally, the study takes iPad Air as an example to prove this model. The results show that the extraction performance of the PFEMF model is superior to the traditional term frequency-inverse document frequency (tf-idf) algorithm, word span algorithm, and semantic similarity algorithm.

Risk Cost Measurement of Value for Money Evaluation Based on Case-Based Reasoning and Ontology: A Case Study of the Urban Rail Transit Public-Private Partnership Projects in China

Risk is demonstrated as one of the most crucial drivers of value for money (VFM) in public–private partnerships (PPP), but in previous studies, the risk cost estimation of the quantitative evaluation of VFM was still a dilemma that strongly depended on specialist discretion or had low methodological operability. This paper establishes a prediction model for estimating the risk cost in the phase of VFM evaluation through a combination of case-based reasoning (CBR) and ontology technology. PPP information ontology was established to provide the technical basis of knowledge representation for the CBR cycle. Then, according to whether the information data were quantitative or qualitative, similarity calculation methods were used for the retrieval of similar cases. The conceptual semantic similarity algorithm based on the ontology tree structure was well implemented to compare abstract information. After the most similar cases were extracted, a revision mechanism was followed when there were deviations in the similar cases. Finally, the risk costs of the target case were obtained by weighting the extracted similar cases based on the similarity. An empirical analysis was performed with 18 historical projects from the China Public–Private Partnerships Center. The results showed that the relative errors between the estimated and actual costs of total risk and retained risk were 11.05% and 2.41%, respectively. This indicates that the estimation model could achieve a better risk cost prediction with small errors, which validates the availability of the model. Based on the proposed model, this research establishes an extensible PPP information ontology model. It promotes the integration and interoperability of information knowledge in the PPP domain, which can be further expanded according to the requirements. Coherent accuracy is provided by the whole CBR-based measurement process, which has offered a systematic and objective method for the risk costs measurement of PPP projects.

An Effective Resource Matching Scheme Based on a Novel Unified Descriptive Model for Modern Manufacturing Industry Systems

In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and modularized ontology-based resource-unified descriptive model, according to the characteristics of design/manufacturing/service resources. We analyze all kinds of properties of the resources, design a specific descriptive model of ontology, function, and service, ensure the consistency and independence of resource descriptions, and use the OWL (Web Ontology Language) ontology descriptive language and Protégé tools to verify. Then, based on the unified descriptive model, a resource matching method based on multi-level tags is proposed, which matches the task request with the resources in the resource library, selects the resources that meet the task request, and guarantees the resource sharing across subjects and stages. The resource matching work first performs task description and decomposition, and uses information entropy and rough set theory to sort the importance of subtasks, then uses the semantic similarity algorithm to complete multi-level tags’ matching. Finally, two examples are used to prove the feasibility and effectiveness of the experiment.

Assessing Lexical Psychological Properties in Second Language Production: A Dynamic Semantic Similarity Approach.

Previous studies of the lexical psycholinguistic properties (LPPs) in second language (L2) production have assessed the degree of an LPP dimension of an L2 corpus by computing the mean ratings of unique content words in the corpus for that dimension, without considering the possibility that learners at different proficiency levels may perceive the degree of that dimension of the same words differently. This study extended a dynamic semantic similarity algorithm to estimate the degree of five different LPP dimensions of several sub-corpora of the Education First-Cambridge Open Language Database representing L2 English learners at different proficiency levels. Our findings provide initial evidence for the validity of the algorithm for assessing the LPPs in L2 production and contribute useful insights into between-proficiency relationships and cross-proficiency differences in the LPPs in L2 production as well as the relationships among different LPP dimensions.

Event Detection through Lexical Chain Based Semantic Similarity Algorithm

Twitter is a platform where millions of people tend to tweet about new events happening in their lives. Celebrities tweeting about the new product endorsement, Politicians tweeting about their views towards a policy or people, Natural calamities occurring are tweeted instantly. Studying this data can provide us with useful information. In this paper, we have proposed an Event detection using a lexical chain based semantic similarity algorithm, for detecting Events from Twitter streams. Lexical chains have been used to preserve the Lexical cohesion in a text. The Twitter data set was collected using “tweepy” API, then pre-processing was done, steps like tokenization, stop word removal, and stemming is carried out and stored the tweets in a text file. Then lexical chains were built using the tweets in the file. The formation of the key graph, with each node as a lexical chain, was carried out. Then the clustering algorithm ‘SCAN’ was used to cluster the lexical chains, the formed clusters represent the keywords of an event. The last summarization step was carried out for each cluster representing an event.

Improvement and Analysis of Semantic Similarity Algorithm Based on Linguistic Concept Structure

With the rapid development of information age, various social groups and corresponding institutions are producing a large amount of information data every day. For such huge data storage and identification, in order to manage such data more efficiently and reasonably, traditional semantic similarity algorithm emerges. However, the accuracy of the traditional semantic similarity algorithm is relatively low, and the convergence of corresponding algorithm is poor. Based on this problem, this paper starts with the conceptual structure of language, analyzes the depth of language structure and the distance between nodes, and analyzes the two levels as the starting point. For the information of a specific data resource description frame type, the weight of interconnected edges is used for impact analysis so as to realize the semantic similarity impact analysis of all information data. Based on the above improvements, this paper also systematically establishes the data information modeling process based on language conceptual structure and establishes the corresponding model. In the experimental part, the improved algorithm is simulated and analyzed. The simulation results show that compared with the traditional algorithm, the algorithm has obvious accuracy improvement.

AWN-similarity: Towards developing free open-source frameworks for measuring Arabic semantic similarity under Windows / Linux operating systems

Arabic is a highly systematic language where its words exhibit elegant and rigorous logic. The field of Arabic word semantic similarity becomes more challenging due to its higher complexity and subtlety. This research is concerned with investigating the development of free open-source frameworks containing packages to calculate the semantic similarity between two Arabic words or concepts. These packages are known as AWN-ConceptSimilarity and AWN-WordSimilarity. The developed packages implement seven semantic similarity algorithms. One of these algorithms was proposed for Arabic and the rest were proposed for English where successfully adapted to Arabic using an Arabic lexical database, Arabic wordnet. The functionality of the developed packages is validated using two-word similarity benchmarks datasets previously produced for Arabic. The results of the validation process indicate that the developed frameworks represent an important contribution to the Arabic semantic similarity field. Moreover, the developed packages are reliable to use and embed them with Arabic researchers' projects for improving or comparing their methodologies.

AWN-similarity: Towards developing free open-source frameworks for measuring Arabic semantic similarity under Windows / Linux operating systems

Arabic is a highly systematic language where its words exhibit elegant and rigorous logic. The field of Arabic word semantic similarity becomes more challenging due to its higher complexity and subtlety. This research is concerned with investigating the development of free open-source frameworks containing packages to calculate the semantic similarity between two Arabic words or concepts. These packages are known as AWN-ConceptSimilarity and AWN-WordSimilarity. The developed packages implement seven semantic similarity algorithms. One of these algorithms was proposed for Arabic and the rest were proposed for English where successfully adapted to Arabic using an Arabic lexical database, Arabic wordnet. The functionality of the developed packages is validated using two-word similarity benchmarks datasets previously produced for Arabic. The results of the validation process indicate that the developed frameworks represent an important contribution to the Arabic semantic similarity field. Moreover, the developed packages are reliable to use and embed them with Arabic researchers' projects for improving or comparing their methodologies.

Clinical Context-Aware Biomedical Text Summarization Using Deep Neural Network: Model Development and Validation.

BackgroundAutomatic text summarization (ATS) enables users to retrieve meaningful evidence from big data of biomedical repositories to make complex clinical decisions. Deep neural and recurrent networks outperform traditional machine-learning techniques in areas of natural language processing and computer vision; however, they are yet to be explored in the ATS domain, particularly for medical text summarization.ObjectiveTraditional approaches in ATS for biomedical text suffer from fundamental issues such as an inability to capture clinical context, quality of evidence, and purpose-driven selection of passages for the summary. We aimed to circumvent these limitations through achieving precise, succinct, and coherent information extraction from credible published biomedical resources, and to construct a simplified summary containing the most informative content that can offer a review particular to clinical needs.MethodsIn our proposed approach, we introduce a novel framework, termed Biomed-Summarizer, that provides quality-aware Patient/Problem, Intervention, Comparison, and Outcome (PICO)-based intelligent and context-enabled summarization of biomedical text. Biomed-Summarizer integrates the prognosis quality recognition model with a clinical context–aware model to locate text sequences in the body of a biomedical article for use in the final summary. First, we developed a deep neural network binary classifier for quality recognition to acquire scientifically sound studies and filter out others. Second, we developed a bidirectional long-short term memory recurrent neural network as a clinical context–aware classifier, which was trained on semantically enriched features generated using a word-embedding tokenizer for identification of meaningful sentences representing PICO text sequences. Third, we calculated the similarity between query and PICO text sequences using Jaccard similarity with semantic enrichments, where the semantic enrichments are obtained using medical ontologies. Last, we generated a representative summary from the high-scoring PICO sequences aggregated by study type, publication credibility, and freshness score.ResultsEvaluation of the prognosis quality recognition model using a large dataset of biomedical literature related to intracranial aneurysm showed an accuracy of 95.41% (2562/2686) in terms of recognizing quality articles. The clinical context–aware multiclass classifier outperformed the traditional machine-learning algorithms, including support vector machine, gradient boosted tree, linear regression, K-nearest neighbor, and naïve Bayes, by achieving 93% (16127/17341) accuracy for classifying five categories: aim, population, intervention, results, and outcome. The semantic similarity algorithm achieved a significant Pearson correlation coefficient of 0.61 (0-1 scale) on a well-known BIOSSES dataset (with 100 pair sentences) after semantic enrichment, representing an improvement of 8.9% over baseline Jaccard similarity. Finally, we found a highly positive correlation among the evaluations performed by three domain experts concerning different metrics, suggesting that the automated summarization is satisfactory.ConclusionsBy employing the proposed method Biomed-Summarizer, high accuracy in ATS was achieved, enabling seamless curation of research evidence from the biomedical literature to use for clinical decision-making.

Application of an Improved Synthetical Semantic Similarity Method in Water Knowledge Graph

In order to integrate different kinds of information in the water affairs field, this paper proposes an improved comprehensive semantic similarity algorithm and applies it to the construction of water affairs ontology. In this article, the algorithm is to give the concept instance similarity algorithm and concept definition similarity algorithm appropriate weight. First, we apply the three algorithms to the concept of the local ontology of smart water, and then calculate the similarity between the concepts to judge the similarity between the concepts to merge the smart water data, and finally compare the similarities of the three algorithms Degree value and calculation and comparison of three similarity variance values prove the effectiveness of the method. It can be seen from the experimental results that the variance of the comprehensive similarity calculation method is smaller than that of the other two methods, and it is easier to determine the similarity between two concepts.

An improved semantic similarity algorithm based on HowNet and CiLin

This paper explores an improved method for the semantic similarity calculation of words combined with HowNet and CiLin. Firstly, we designing the algorithm based on HowNet’s sememe similarity improvement calculation, comprehensively considering the influence of each part of sememe on the overall meaning, and improving the calculation of word similarity based on HowNet by changing the specific calculation method of each part of sememe. At the same time, we adopt different strategies for the different results obtained in the similarity calculation of CiLin. The experimental RG data set proves that the modified Pearson coefficient of the method reaches 0.87.

More Agility to Semantic Similarities Algorithm Implementations.

Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of humans, animals, and plants. While the focus of the research is on the design and improvement of GO semantic similarity algorithms, there is still a need for implementation of such algorithms before they can be used to solve actual biological problems. This can be challenging given that the potential users usually come from a biology background and they are not programmers. A number of implementations exist for some well-established algorithms but these implementations are not generic enough to support any algorithm other than the ones they are designed for. The aim of this paper is to shift the focus away from implementation, allowing researchers to focus on algorithm’s design and execution rather than implementation. This is achieved by an implementation approach capable of understanding and executing user defined GO semantic similarity algorithms. Questions and answers were used for the definition of the user defined algorithm. Additionally, this approach understands any direct acyclic digraph in an Open Biomedical Ontologies (OBO)-like format and its annotations. On the other hand, software developers of similar applications can also benefit by using this as a template for their applications.

Classification of Semantic Similarity Technique between Word Pairs using Word Net

The concept of relevancy is a most blazing topic in information regaining process. In the last few years there is a drastically increase the digital data so there is a need to increase the accuracy of information regaining process .Semantic Similarity measure the similarity between word-pair by using WordNet as ontology.We have analyzed the different category of semantic similarity algorithm to compute semantic closeness between word-pair and evaluate its value by using WordNet.We have compared various algorithms on Miller- Charles data set of 30 word-pair is used to rank them category wise.

An ontology learning based approach for focused web crawling using combined normalized pointwise mutual information and Resnik algorithm

The precedence of unexplored Uniform Resource Locators (URLs) is calculated in many existing works based on a linear combination of similarities of different texts of the web_page and the specified topic along with their associated weights. These weights, however, are chosen based on various methodologies like Term Frequency-Inverse Document Frequency (TF-IDF), so these weights can immediately create severe deviations from the priorities of unvisited web pages and also it will calulate the similarity only if the word occurs in the web page. It won’t consider the semantic similarity of the word in the web page. To overcome the troubles mentioned above, this article presents a new focused web crawler based on combined Normalized Pointwise Mutual Information (NPMI) and Resnik based semantic similarity algorithm, called as P-crawler. In the P-crawler, the records of an unexplored web page are made up of web page text, anchor text, title text, bold text and heading text of the web page. The experimental findings show that the suggested algorithm increases focused on crawler efficiency. In conclusion, the above technique is efficient and promising for focused web crawlers.