Robust multi-source geographic entities matching by maximizing geometric and semantic similarity

With the increasing complexity of scientific and technological project management, traditional project management systems are faced with the challenges of massive data processing and semantic understanding. The study first discusses the computer-implemented technology of semantic similarity evaluation, analyzes the application of word embedding and deep learning algorithms, and emphasizes the importance of these technologies in natural language processing (NLP). A novel hybrid semantic similarity evaluation framework has been developed and implemented, incorporating an optimized algorithm-based semantic feature extraction module alongside multi-source data fusion and processing techniques modeled on semantic relationships. The framework’s primary objective is to enable precise analysis of multilingual project data by deeply exploring contextual information. Experimental validation highlights the framework’s performance and effectiveness, with particular emphasis on its accuracy in semantic similarity calculations, data processing efficiency, and responsiveness in large-scale environments. During testing, the framework achieved a semantic similarity calculation accuracy of 67.37%, based on a test set comprising 73 samples. An analysis of 74,723 project records revealed that the system’s average response time for multi-source data fusion was 2.3 seconds. Under complex semantic analysis conditions, the framework processed data at a rate of 348.15 items per second. Multiple test results further demonstrated that the error margin in semantic evaluation remained within 2.51 percentage points. These findings confirm that the system optimization successfully meets the anticipated goals in terms of both accuracy and efficiency.

The sharing and interoperation of geographical information has always been a hot topic in the field of geographical information science, and the semantic similarity of the concept of geographic information is also an important factor affecting geographic information sharing and interoperability. The semantic similarity of concept calculation is a natural language processing, an important component of the study, but also applications of artificial intelligence problems to be solved. Ontology, which is an explicit and formal specification of sharing information, becoming an effective method for solve the problem of semantic similarity. Considering the special nature of the concept of geographic information, this paper takes an integrated multi-point semantic similarity calculation method in this paper. We made an investigation on the distance-based semantic similarity calculation model, the attribute-based semantic similarity calculation model, the relationship between concepts-based semantic similarity calculation models. And we analyzed all aspects of the concept of geographic information. Then, considering the impact of the distance, the attributes, the relationship between concepts for the semantic similarity of the concept of geographic information, this paper established an integrated and multi-angle model for the semantic similarity calculation of geographic information concept. And we added some adjustable parameters to adapt the flexible application. Make sure the calculation results more accurate and objective. By precise experimental analysis, this integrated model can improve the calculation accuracy.

In natural language processing (NLP) tasks, computing semantic textual similarity (STS) is crucial for capturing nuanced semantic differences in text. Traditional word vector methods, such as Word2Vec and GloVe, as well as deep learning models like BERT, face limitations in handling context dependency and polysemy and present challenges in computational resources and real-time processing. To address these issues, this paper introduces two novel methods. First, a sentence embedding generation method based on Kullback–Leibler Divergence (KLD) optimization is proposed, which enhances semantic differentiation between sentence vectors, thereby improving the accuracy of textual similarity computation. Second, this study proposes a framework incorporating RoBERTa knowledge distillation, which integrates the deep semantic insights of the RoBERTa model with prior methodologies to enhance sentence embeddings while preserving computational efficiency. Additionally, the study extends its contributions to sentiment analysis tasks by leveraging the enhanced embeddings for classification. The sentiment analysis experiments, conducted using a Stochastic Gradient Descent (SGD) classifier on the ACL IMDB dataset, demonstrate the effectiveness of the proposed methods, achieving high precision, recall, and F1 score metrics. To further augment model accuracy and efficacy, a feature selection approach is introduced, specifically through the Dynamic Principal Component Selection (DPCS) algorithm. The DPCS method autonomously identifies and prioritizes critical features, thus enriching the expressive capacity of sentence vectors and significantly advancing the accuracy of similarity computations. Experimental results demonstrate that our method outperforms existing methods in semantic similarity computation on the SemEval-2016 dataset. When evaluated using cosine similarity of average vectors, our model achieved a Pearson correlation coefficient (τ) of 0.470, a Spearman correlation coefficient (ρ) of 0.481, and a mean absolute error (MAE) of 2.100. Compared to traditional methods such as Word2Vec, GloVe, and FastText, our method significantly enhances similarity computation accuracy. Using TF-IDF-weighted cosine similarity evaluation, our model achieved a τ of 0.528, ρ of 0.518, and an MAE of 1.343. Additionally, in the cosine similarity assessment leveraging the Dynamic Principal Component Smoothing (DPCS) algorithm, our model achieved a τ of 0.530, ρ of 0.518, and an MAE of 1.320, further demonstrating the method’s effectiveness and precision in handling semantic similarity. These results indicate that our proposed method has high relevance and low error in semantic textual similarity tasks, thereby better capturing subtle semantic differences between texts.

A semantic similarity computation method for virtual resources in cloud manufacturing environment based on information content

Wikipedia bi-linear link (WBLM) model: A new approach for measuring semantic similarity and relatedness between linguistic concepts using Wikipedia link structure

Entity matching (EM) recognizes records in one or different databases that represent the same entity in real-world. It is an essential part of data cleaning and data integration. Most existing studies determine whether two records match by calculating the similarity of the corresponding attribute values in the two records. Then series of similarity metrics are proposed. But according to our investigation, we found that no one has considered combining semantic level similarity with string level similarity. In some data tables, some attributes values are long text, such as product descriptions, and they are more semantically similar in different records that refer to a same entity. Other numeric or noun attributes, such as price and person name, are more suitable for string level similarity calculations. Therefore, we propose a model to calculate the similarity of the two types of attributes using modules of different similarity levels, and assign them different weights. Learning by labeled data, we get a model that can effectively solve the entity matching task.

Human-inspired semantic similarity between sentences

A key aspect of Knowledge fusion is Entity Matching. The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity. In recent years, some representative works have used deep learning methods for entity matching, and these methods have achieved good results. However, the common limitation of these methods is that they assume that different attribute columns of the same entity are independent, and inputting the model in the form of paired entity records will cause repeated calculations. In fact, there are often potential relations between different attribute columns of different entities. These relations can help us improve the effect of entity matching, and can perform feature extraction on a single entity record to avoid repeated calculations. To use attribute relations to assist entity matching, this paper proposes the Relation-aware Entity Matching method, which embeds attribute relations into the original entity description to form sentences, so that entity matching is transformed into a sentence-level similarity determination task, based on Sentence-BERT completes sentence similarity calculation. We have conducted experiments on structured, dirty, and textual data, and compared them with baselines in recent years. Experimental results show that the use of relational embedding is helpful for entity matching on structured and dirty data. Our method has good results on most data sets for entity matching and reduces repeated calculations.

In the field of biology, researchers need to compare genes or gene products using semantic similarity measures (SSM). Continuous data growth and diversity in data characteristics comprise what is called big data; current biological SSMs cannot handle big data. Therefore, these measures need the ability to control the size of big data. We used parallel and distributed processing by splitting data into multiple partitions and applied SSM measures to each partition; this approach helped manage big data scalability and computational problems. Our solution involves three steps: split gene ontology (GO), data clustering, and semantic similarity calculation. To test this method, split GO and data clustering algorithms were defined and assessed for performance in the first two steps. Three of the best SSMs in biology [Resnik, Shortest Semantic Differentiation Distance (SSDD), and SORA] are enhanced by introducing threaded parallel processing, which is used in the third step. Our results demonstrate that introducing threads in SSMs reduced the time of calculating semantic similarity between gene pairs and improved performance of the three SSMs. Average time was reduced by 24.51% for Resnik, 22.93%, for SSDD, and 33.68% for SORA. Total time was reduced by 8.88% for Resnik, 23.14% for SSDD, and 39.27% for SORA. Using these threaded measures in the distributed system, combined with using split GO and data clustering algorithms to split input data based on their similarity, reduced the average time more than did the approach of equally dividing input data. Time reduction increased with increasing number of splits. Time reduction percentage was 24.1%, 39.2%, and 66.6% for Threaded SSDD; 33.0%, 78.2%, and 93.1% for Threaded SORA in the case of 2, 3, and 4 slaves, respectively; and 92.04% for Threaded Resnik in the case of four slaves.

Document summarization provides an instrument for faster understanding the collection of text documents and has a number of real life applications. Semantic similarity and clustering can be utilized efficiently for generating effective summary of large text collections. Summarizing large volume of text is a challenging and time consuming problem particularly while considering the semantic similarity computation in summarization process. Summarization of text collection involves intensive text processing and computations to generate the summary. MapReduce is proven state of art technology for handling Big Data. In this paper, a novel framework based on MapReduce technology is proposed for summarizing large text collection. The proposed technique is designed using semantic similarity based clustering and topic modeling using Latent Dirichlet Allocation (LDA) for summarizing the large text collection over MapReduce framework. The summarization task is performed in four stages and provides a modular implementation of multiple documents summarization. The presented technique is evaluated in terms of scalability and various text summarization parameters namely, compression ratio, retention ratio, ROUGE and Pyramid score are also measured. The advantages of MapReduce framework are clearly visible from the experiments and it is also demonstrated that MapReduce provides a faster implementation of summarizing large text collections and is a powerful tool in Big Text Data analysis.

Point of interest (POI) matching finds POI pairs that refer to the same real-world entity, which is the core issue in geospatial data integration. To address the low accuracy of geospatial entity matching using a single feature attribute, this study proposes a method that combines the D–S (Dempster–Shafer) evidence theory and a multiattribute matching strategy. During POI data preprocessing, this method calculates the spatial similarity, name similarity, address similarity, and category similarity between pairs from different geospatial datasets, using the multiattribute matching strategy. The similarity calculation results of these four types of feature attributes were used as independent evidence to construct the basic probability distribution. A multiattribute model was separately constructed using the improved combination rule of the D–S evidence theory, and a series of decision thresholds were set to give the final entity matching results. We tested our method with a dataset containing Baidu POIs and Gaode POIs from Beijing. The results showed the following—(1) the multiattribute matching model based on improved DS evidence theory had good performance in terms of precision, recall, and F1 for entity-matching from different datasets; (2) among all models, the model combining the spatial, name, and category (SNC) attributes obtained the best performance in the POI entity matching process; and (3) the method could effectively address the low precision of entity matching using a single feature attribute.

Fire plays a prominent role in the construction and destruction of ecosystems, and the accurate estimation of the burned area (BA) after a fire occurrence is of great significance to protect ecosystems and save people’s lives and property. This study evaluated the performances of three publicly available BA satellite products (GFED4, MCD64CMQ, and FireCCI5.1) in detecting Chinese forest fire burning from 2001 to 2016 across different time scales (yearly, monthly, and seasonally) and spatial scales (regional and provincial). The reference data were derived from the monthly China Forestry Statistical Yearbook (CFSY), and they were mainly used to evaluate the detection ability of each of the three BA products in the three major forest fire areas of China consisting of the Northeast (NE), Southwest (SW), and Southeast (SE) regions. The main results are as follows: (1) A significant declining BA trend was demonstrated in the whole study area and in the NE and SE subregions. Specifically, the slopes for the whole area ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, the slopes for the NE region ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, and the slopes for the SE region ranged from −594.24 ha/year for GFED4 to −3162.1 ha/year for the CFSY. The BA in China was mainly dominated by forest fires in the NE region, especially in 2003 and 2006 when this region accounted for 90% and 87% of occurrences, respectively. (2) Compared with the CFSY, GFED4 had the best performance at the yearly scale with an RMSE of 23.9 × 104 ha/year and CC of 0.83. Similarly, at the monthly scale, GFED4 also had the best performance for the three regions, with the lowest RMSE ranging from 0.33 × 104 to 5.4 × 104 ha/month—far lower than that of FireCC5.1 which ranged from 1.16 × 104 to 8.56 × 104 ha/month (except for the SE region where it was slightly worse than MCD64CMQ). At the seasonal scale, GFFD4 had the best performance in spring and winter. It was also noted that the fewer BAs in summer made the differences among the products insignificant. (3) Spatially, GFED4 had the best performance in RMSEs for all the provinces of the three regions, in CCs for the provinces of the SW and SE regions, and in MEs for the provinces of the SE region. (4) All three products had stronger detection abilities for severe and disaster fires than for common fires. Additionally, GFED4 had a more consistent number of months with the CFSY than the other products in the NE region. Moreover, the conclusion that GFED4 had the best performance in the China region was also proved using other validated BA datasets. These results will help us to understand the BA detection abilities of the satellite products in China and promote the further development of multi-source satellite fire data fusion.

Sentence similarity computation is a key part of the Chinese FAQ system and the computation accuracy determine the quality of the system. Combining with question classification index, a new Chinese sentence similarity computing method based on Content Forward Network is proposed. The method combined concept semantic matching and the overall semantic question sentence similarity computation with question automatic classification to deal with the sentence similarity computing. Comparing with the traditional keyword-based and simple semantic question similarity computation method, Chinese frame semantic information makes the Chinese sentence similarity calculation accuracy improved obviously. The experimental results show that the method can effectively solve the intelligent matching problem in Chinese question answering system. Keywords—Sentence similarity; Chinese Frame Network; Frame element; Question classification; Question answering system

Natural language processing (NLP) is a critical research direction in artificial intelligence, where the calculation of vocabulary semantic similarity is the foundation and core work. However, existing calculation methods are faced with problems, e.g., the inability to extract important semantic information. Failure to address this issue can compromise the accuracy of semantic similarity measures in NLP applications. To this end, in this paper, a vocabulary semantic similarity calculation model based on word vectors and convolutional neural networks (CNNs) was proposed. The word vector model was improved using long short-term memory (LSTM) networks, and important semantics were extracted using convolutional layers and ensured semantic order through bidirectional Gated Recurrent Unit. The structure of the Siamese neural network was used to ensure consistency in text encoding. The experimental findings have shown that the proposed model has the highest F1 value in different datasets. In the original Chinese natural language inference (OCNLI) dataset, the Pearson correlation coefficient of the model was 0.021 and 0.018 higher than that of the LSTM network and CNNs, respectively. The accuracy of the similarity calculation in the two datasets was 92.4% and 96.5%, respectively. According to these results, the semantic similarity prediction value of the proposed model can be closer to the true value, and the prediction performance of the model is more excellent.

BackgroundIn recent years, with increases in the amount of information available and the importance of information screening, increased attention has been paid to the calculation of textual semantic similarity. In the field of medicine, electronic medical records and medical research documents have become important data resources for clinical research. Medical textual semantic similarity calculation has become an urgent problem to be solved.ObjectiveThis research aims to solve 2 problems—(1) when the size of medical data sets is small, leading to insufficient learning with understanding of the models and (2) when information is lost in the process of long-distance propagation, causing the models to be unable to grasp key information.MethodsThis paper combines a text data augmentation method and a self-ensemble ALBERT model under semisupervised learning to perform clinical textual semantic similarity calculations.ResultsCompared with the methods in the 2019 National Natural Language Processing Clinical Challenges Open Health Natural Language Processing shared task Track on Clinical Semantic Textual Similarity, our method surpasses the best result by 2 percentage points and achieves a Pearson correlation coefficient of 0.92.ConclusionsWhen the size of medical data set is small, data augmentation can increase the size of the data set and improved semisupervised learning can boost the learning efficiency of the model. Additionally, self-ensemble methods improve the model performance. Our method had excellent performance and has great potential to improve related medical problems.