Semantic Representation Method Research Articles

Comparing semantic representation methods for keyword analysis in bibliometric research

Semantic representation methods play a crucial role in text mining tasks. Although numerous approaches have been proposed and compared in text mining research, the comparison of semantic representation methods specifically for publication keywords in bibliometric studies has received limited attention. This lack of practical evidence makes it challenging for researchers to select suitable methods to obtain keyword vectors for downstream bibliometric tasks, potentially hindering the achievement of optimal results. To address this gap, this study conducts an experimental comparison of various typical semantic representation methods for keywords, aiming to provide quantitative evidence for bibliometric studies. The experiment focuses on keyword clustering as the fundamental task and evaluates 22 variations of five typical methods across four scientific domains. The methods compared are co-word matrix, co-word network, word embedding, network embedding, and “semantic + structure” integration. The comparison is based on fitting the clustering results of these methods with the “evaluation standard” specific to each domain. The empirical findings demonstrate that the co-word matrix exhibits subpar performance, whereas the co-word network and word embedding techniques display satisfactory performance. Among the five network embedding algorithms, LINE and Node2Vec outperform DeepWalk, Struc2Vec, and SDNE. Remarkably, both the “pre-training and fine-tuning” model and the “semantic + structure” model yield unsatisfactory results in terms of performance. Nevertheless, even with variations in the performance of these methods, no singular approach stands out as universally superior. When selecting methods in practical applications, comprehensive consideration of factors such as corpus size and semantic cohesion of domain keywords is crucial. This study advances our understanding of semantic representation methods for keyword analysis and contributes to the advancement of bibliometric analysis by providing valuable recommendations for researchers in selecting appropriate methods.

Improving Semantic Analysis in Visualization with Meta Network Representation and Parsing Algorithm

This article aims to advance semantic analysis models, particularly in visualization, by proposing a novel semantic representation method utilizing the semantic Meta Network (MNet). MNet is a complex framework comprising semantic elements, internal and external relationships, and feature attributes, defined hierarchically through recursive processes, aiming to depict the comprehensive semantic space from phrase-level components to complete texts. The methodology involves the development of a general construction algorithm for MNet, encompassing meta relationships, tree structures, and network structures, and a Parsing method for specific semantic analysis problems, including a bottom-up specification-based MNet semantic dependency tree construction algorithm and a network construction algorithm tailored for natural language interface parsing. Empirical experiments confirm the effectiveness of these algorithms, particularly in parsing natural language control interface instructions in Supervisory Control and Data Acquisition (SCADA) systems, bridging specific semantic analysis problems with the general construction and parsing processes of MNet, accounting for internal semantics concerning language unit structures and foreign language meanings in the linguistic context, thereby contributing significantly to the field of natural language semantic analysis.

HMFVC: A Human-Machine Friendly Video Compression Scheme

Recent studies on Video Coding for Machine (VCM) have achieved remarkable results. However, in practical visual-analytic applications, except for transmitting the visual feature for machine analysis, video textures are also mandatory for human monitoring and decision-making. To this end, this paper proposes a human-machine friendly video compression scheme (HMFVC) which can satisfy both human viewing and machine analysis well. First, we propose a learned semantic representation (LSR) method to extract semantic information between temporal neighboring frames. LSR could be utilized in signal reconstruction for human viewing and visual analysis for machine understanding. Second, given the proposed LSR, we design an end-to-end optimized video compression framework to jointly optimize the visual quality for human perception, analysis accuracy for machines, and compression efficiency as well. Finally, an HMFVC codec is developed, which can achieve higher action recognition accuracy and better reconstruction quality than the traditional codecs and learned video compression approaches. Specifically, HMFVC saves 77% bitrate to achieve the same analysis performance with the original videos compared to x265. To our knowledge, HMFVC is the first end-to-end optimized video compression scheme to serve both humans and machines. It is a promising framework for human-machine friendly video compression approaches.

Multi-Level Text Clustering in Subject Knowledge Library and its Visualization

The large-scale and complex data generated in the teaching field of business administration poses challenges for decision-makers and managers of companies, and how to effectively extract and manage the useful information contained in these data has become a problem to be solved. Currently available methods of subject knowledge library clustering and visualization struggle to handle the complexity and multi-hierarchies of such subject data effectively or meet users’ requirements for advanced semantic understanding and retrieval. In view of these matters, this study aims to probe deeper into the problem of multi-level text clustering in the subject knowledge library and its visualization. Firstly, an innovative strategy-based subject semantic representation method for knowledge libraries was proposed to better interpret and represent the semantic information of subject data. Secondly, a subject clustering model of the knowledge library was constructed based on an improved hierarchical Dirichlet polynomial distribution, enabling efficient and accurate clustering of subject data. Lastly, visualization technology was employed to display the cluster results, allowing users to gain a clear understanding of the internal relationships and structure of the subject data. The research findings of this study could provide valuable new tools and methods for solving the problem of subject knowledge library management and utilization, analyzing the subject data, and supporting decision-making. As a result, they hold both theoretical and practical significance.

A Semantic Representation Method of Building Codes Applied to Compliance Checking

Compliance checking is a very important step in engineering construction. With the development of information technology, automated compliance checking (ACC) has been paid more and more attention by researchers. One of the most important steps in automated compliance checking is the representation of the code information. However, the relationship constraint is often ignored in the code information and spatial geometric relationship is challenging to represent. The general code representation method does not have enough ability to identify the situation that does not meet the checking conditions because it is easy to cause semantic ambiguity in the checking results. This paper proposes a code representation method, and the building code information is represented in five parts. Relationships in the engineering domain and spatial relationships can be represented in constraint mode; different spatial relationship constraint-checking methods are also explicated. Constraint subject and constraint item can distinguish checking conditions and requirements, which supports semantic checking results. The mapping between the building information ontology and the code concepts is established, which can be used to automatically generate reasoning rules for compliance checking. Finally, the proposed method is verified by the representation of the China Metro Design Code and the application of the actual Metro model.

A semantic data-driven knowledge base construction method to assist designers in design inspiration based on traditional motifs

Directing at the problem of inaccurate expression of tacit knowledge and low heuristic of retrieval results in the design inspiration drawing process of designers based on traditional motifs, a semantic data representation method of traditional motifs knowledge is put forward, and an effective knowledge base construction and knowledge retrieval method for traditional motifs are described. First, the connotation of traditional motifs is deeply explored based on the theory of symbol semantics, realizing the interpretation and transfer of the semantic features in traditional motifs. Second, shape grammar is adopted for fractal traditional motifs, the hierarchical structure of traditional motifs is built, and the parametric coding of semantic features of traditional motifs is completed, providing the semantic data representation method of traditional motifs knowledge. Third, based on the clustering algorithm, the construction method of the traditional motifs knowledge base is explained, and a traditional motifs knowledge retrieval method taking into account the user's perceptual preference is proposed. Finally, setting the traditional Chinese motifs as an example, with a tea set as the designed product, the construction process of a small traditional motif knowledge base is shown in detail, proving the feasibility of this paper’s theory. Compared with the traditional theory, the semantic data representation method of traditional pattern knowledge is greatly advantageous in semantic differences elimination and computational efficiency.

The Sociology of Female Characters in the Novels of Western Armenian Female Authors in the Late 19th Century

The aim of the research is to reveal the sociology of building women characters (their vocabulary) in the novels of two Armenian female authors, namely novelist-publicist Srbuhi Tyusab and novelist-poet Sipil, who lived and worked in the Ottoman Empire, Constantinople in particular, in the second half of the 19th century. The problem of the study is the literary-sociological and linguistic generalizations of female characters’ vocabulary in Srbuhi Tyusab’s novel “Araksia or the Governess” and Sipil’s “A Girl’s Heart” from the perspectives of comparative analysis of linguistic features, vocabulary and semantic commonalities of the texts. A comparative analysis of female characters in the novels “Araksia or the Governess” (1887) by Tyusab and “A Girl’s Heart” (1891) by Sipil, has been attempted from the perspectives of literary and sociological studies. The actuality of the study lies in its interdisciplinarity, according to which the material was analyzed in the context of mutual connections between literary studies, linguistics, social and computer sciences with the application of the methods of literary and linguistic comparison, sociological statistics, as well as the graph-based semantic representation method. This kind of study has been attempted for the first time. It is important and actual not only in terms of interdisciplinarity, but also in terms of the analysis of women’s issues in Armenian Studies.

TYPOLOGY OF FEMALE CHARACTERS IN THE NOVELS OF WESTERN ARMENIAN FEMALE AUTHORS

The aim of the research is to explore the typology of female characters in the works of female writers Srbuhi Tyusab, Sipil and Zapel Yesayan in the context of social transformations in the Ottoman Empire of the second half of the 19th and the beginning of the 20th centuries. A comparative analysis of female characters in the novels Mayta (1881), Siranush (1884), Araksia or the Governess (1887) by Tyusab, A Girl’s Heart (1895) by Sipil, and Gardens of Silihtar (1935) by Zapel Yesayan has been attempted from the perspectives of literary and sociological studies. The data are analysed with the application of the graph-based semantic representation method. The actuality of the research lies in its interdisciplinarity, according to which mutual connections are created between literary studies, sociology and computer science. The selection of these works as research data is accounted for by their volume, variety of characters, simplicity of plots and little branching. All the main characters in all the novels are females. All the novels were written around the same time period (the 1880s-1900s) and bear characteristics of a romantic novel, except Zapel Yesayan’s autobiographical novel Gardens of Silihtar written in 1935. Despite the year the novel was written, in it Yesayan describes the same period (the end of the 19th century) reflected in Tyusab’s and Sipil’s novels.

Detecting Local Opinion Leader in Semantic Social Networks: A Community-Based Approach

Online social networks have been incorporated into people’s work and daily lives as social media and services continue to develop. Opinion leaders are social media activists who forward and filter messages in mass communication. Therefore, competent monitoring of opinion leaders may, to some extent, influence the spread and growth of public opinion. Most traditional opinion leader mining approaches focus solely on the user’s network structure, neglecting the significance and role of semantic information in the generation of opinion leaders. Furthermore, these methods rank the influence of users globally and lack effectiveness in detecting local opinion leaders with low influence. This paper presents a community-based opinion leader mining approach in semantic social networks to address these issues. Firstly, we present a new node semantic feature representation method and community detection algorithm to generate the local public opinion circle. Then, a novel influence calculation method is proposed to find local opinion leaders by combining the global structure of the network and local structure of the public opinion circle. Finally, nodes with high comprehensive influence are identified as opinion leaders. Experiments on real social networks indicate that the proposed method can accurately measure global and local influence in social networks, as well as increase the accuracy of local opinion leader mining.

Comparing Semantic Representation Methods of Keywords in Bibliometric Analysis

Comparing Semantic Representation Methods of Keywords in Bibliometric Analysis

Citation recommendation using semantic representation of cited papers’ relations and content

Citation recommendation can help researchers quickly find supplementary or alternative references in massive academic resources. Current research on citation recommendation mainly focuses on the citing papers, resulting in the enormous cited papers are ignored, including the relations among cited papers and their citation context cited in citing papers. Moreover, cited paper’s content is often denoted with its original title and abstract, which is hard to acquire and rarely considers different citation motivations. Furthermore, the most appropriate method for semantic representation of cited papers’ relations and content is uncertain. Therefore, this paper studies citation recommendation from the perspective of semantic representation of cited papers’ relations and content. Firstly, four forms of citation context are designed and extracted as cited papers’ content considering citation motivations, as well as co-citation relationships are extracted as cited papers’ relations. Secondly, 132 methods are designed for generating semantic vector of cited paper, including four network embedding methods, 16 methods by combining four text representation algorithms with four forms of citation content, and 112 fusion methods. Finally, similarity among cited papers is calculated for citation recommendation and a quantitative evaluation method based on link prediction is designed, to find the most appropriate form of citation content and the optimal method. The result shows that doc2vecC (Document to Vector through Corruption) with the form of CS&SS (Current Sentences and Surrounding Sentences) performs best, in which the AUC (Area Under Curve) and MAP (Macro Average Precision) reach 0.877 and 0.889 and have increased by 0.462 and 0.370 compared with the worst-performing method. This performance is slightly improved by parameters adjustment, and a case study is performed whose results have further proved the effectiveness of this method. In addition, among four forms of cited papers’ content, CS&SS performs best in almost all methods. Furthermore, the fusion methods not always perform better than the single methods, where doc2vecC (CS&SS) performs better than the best fusion method GCN (Graph Convolutional Network). These results not only prove the effectiveness of citation recommendation from the perspective of cited paper, but also provide helpful and useful suggestions for method selection and citation content selection. The data and conclusions can be extended to other text mining-related tasks. Simultaneously, it is a preliminary research which needs to be further studied in other domains using emerging semantic representation methods.

Commodity Image Classification Based on Improved Bag-of-Visual-Words Model

With the increasing scale of e-commerce, the complexity of image content makes commodity image classification face great challenges. Image feature extraction often determines the quality of the final classification results. At present, the image feature extraction part mainly includes the underlying visual feature and the intermediate semantic feature. The intermediate semantics of the image acts as a bridge between the underlying features and the advanced semantics of the image, which can make up for the semantic gap to a certain extent and has strong robustness. As a typical intermediate semantic representation method, the bag-of-visual-words (BoVW) model has received extensive attention in image classification. However, the traditional BoVW model loses the location information of local features, and its local feature descriptors mainly focus on the texture shape information of local regions but lack the expression of color information. Therefore, in this paper, the improved bag-of-visual-words model is presented, which contains three aspects of improvement: (1) multiscale local region extraction; (2) local feature description by speeded up robust features (SURF) and color vector angle histogram (CVAH); and (3) diagonal concentric rectangular pattern. Experimental results show that the three aspects of improvement to the BoVW model are complementary, while compared with the traditional BoVW and the BoVW adopting SURF + SPM, the classification accuracy of the improved BoVW is increased by 3.60% and 2.33%, respectively.

Concept Representation by Learning Explicit and Implicit Concept Couplings

Generating the precise semantic representation of a word or concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually 1) model a word’s semantics from some explicit aspects while ignoring the intrinsic aspects of the word, 2) treat semantic knowledge as a supplement of word embeddings, and 3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between concept co-occurrences and hyperlinks. In human consciousness, a concept is always associated with various couplings that exist within/between descriptive texts and knowledge networks, which inspires us to capture as many concept couplings as possible for building a more informative concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings that are based on concept co-occurrences and hyperlink relations, respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings for better concept representation. Extensive experimental results on six real-world datasets show that CCE significantly outperforms eight state-of-the-art word embeddings and semantic representation methods.

Semantic trajectory representation and retrieval via hierarchical embedding

Trajectory mining has gained growing attention due to its emerging applications, such as location-based services, urban computing, and movement behavior analyses. One critical and fundamental mining task is to retrieve specific locations or trajectories that satisfy particular patterns. However, existing approaches mainly represent the trajectory as a collection of geographic and temporal features, so the latent semantic properties are barely considered. In this paper, we introduce a new semantic trajectory representation method, which considers trajectory structures, temporal information, and domain knowledge to make efficient semantic retrieval possible. Specifically, we first introduce a synchronization-based model to identify multi-resolution regions of interest (ROIs) to extract structures from disordered raw trajectories. Afterward, we proposed a hierarchical embedding model to embed ROIs as well as trajectories on the hierarchical ROI network as continuous vectors by considering multiple kinds of semantic similarity. As a result, users can easily retrieve desirable ROIs or trajectories by computing the similarity among embedded vectors. Experiments show that our approach excels both classical trajectory metric-based models and state-of-the-art deep network embedding models in terms of retrieving interpretable ROIs and trajectories.

A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development.

BackgroundKnowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs.ObjectiveWe aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function.MethodsWe performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task.ResultsCompared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank.ConclusionsThe proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.

A Hierarchical Clustering Approach to Fuzzy Semantic Representation of Rare Words in Neural Machine Translation

Rare words are usually replaced with a single $ $unk$ $>$ token in the current encoder–decoder style of neural machine translation, challenging the translation modeling by an obscured context. In this article, we propose to build a fuzzy semantic representation (FSR) method for rare words through a hierarchical clustering method to group rare words together, and integrate it into the encoder–decoder framework. This hierarchical structure can compensate for the semantic information in both source and target sides, and providing fuzzy context information to capture the semantic of rare words. The introduced FSR can also alleviate the data sparseness, which is the bottleneck in dealing with rare words in neural machine translation. In particular, our method is easily extended to the transformer-based neural machine translation model and learns the FSRs of all in-vocabulary words to enhance the sentence representations in addition to rare words. Our experiments on Chinese-to-English translation tasks confirm a significant improvement in the translation quality brought by the proposed method.

CSDA: A novel attention-based LSTM approach for code search

Previous studies have proposed semantic-based approaches for code search over large-scale codebases, which has bridged the gap in understanding the semantics between natural language and source-code language. However, these studies either failed to determine an effective method for semantic representations or lacked the distinction of semantic features. In this study, we propose a novel attention-based LSTM neural network known as CSDA (Code Search based on Description Attention), which can effectively improve the code search performance. The proposed model can focus on different parts of a semantic feature when numerous aspects of a source code snippet are used as input. As opposed to assigning the same weight to different parts of the semantic vector, CSDA takes the semantics of natural language descriptions into account, so that the subtle differences hidden in the code snippet can be discriminated and associated with the corresponding queries. We compare CSDA with the existing state-of-the-art approach CODEnn, which uses a jointly embedding technique for code search. Our experimental evaluation demonstrates that CSDA outperforms previous methods and achieves superior code search performance to CODEnn, with higher success rates and mean reciprocal ranks. This study provides significant insights into the use of semantic representation methods in deep learning-based code search approaches.

Knowledge-Based Semantic Relatedness measure using Semantic features

Measuring semantic relatedness has received much attention for uses in many fields such as information retrieval and natural language processing. For handling synonymous problem in distributional-based measures, many researchers are investigating how to exploit semantic features in lexical sources to form knowledge-based measures. In the knowledge-based measures, a hierarchy model is used to measure the relatedness between words based on only the taxonomical features extracted from a provided lexical source. In this paper, a new knowledge feature-based measure is proposed to build the semantic vector of a word construct on taxonomical and non-taxonomical feature of relation words. The proposed measure utilised the topological parameters that weight the importance of each element in the semantic vector. One of the gold dataset used to assess the proposed model and compare the findings with other related works. The results demonstrated the effectiveness of the proposed model on measuring semantic relatedness between words. In this paper, the research framework is identified based on the observations made on the previous related works that have been conducted for semantic representation and semantic relatedness measures. The required data in this research includes the semantic knowledge-based approach and the evaluation datasets. The semantic knowledge that will be used throughout of this research is extracted from English WordNet 3.1. On the other hand, the evaluation datasets covers the gold standard benchmarks which have been used for evaluating the semantic relatedness measurements and text mining tasks. Finally, the evaluation is preform to evaluate the proposed method (PM) based on approach in this research, in which obtained the result have been analyzed, to discuss and compare based on different performance measure and finding the strength and weakness in this paper, to alternative the semantic representation correlated to this research, to designing and develop the topical-based on the semantic representation method for text mining from Social media.

Common Semantic Representation Method Based on Object Attention and Adversarial Learning for Cross-Modal Data in IoV

With the significant development of the Internet of Vehicles (IoV), various modal data, such as image and text, are emerging, which provide data support for good vehicle networking services. In order to make full use of the cross-modal data, we need to establish a common semantic representation to achieve effective measurement and comparison of different modal data. However, due to the heterogeneous distributions of cross-modal data, there exists a semantic gap between them. Although some deep neural network (DNN) based methods have been proposed to deal with this problem, there still exist several challenges: the qualities of the modality-specific features, the structure of the DNN, and the components of the loss function. In this paper, for representing cross-modal data in IoV, we propose a common semantic representation method based on object attention and adversarial learning (OAAL). To acquire high-quality modality-specific feature, in OAAL, we design an object attention mechanism, which links the cross-modal features effectively. To further alleviate the heterogeneous semantic gap, we construct a cross-modal generative adversarial network, which contains two parts: a generative model and a discriminative model. Besides, we also design a comprehensive loss function for the generative model to produce high-quality features. With a minimax game between the two models, we can construct a shared semantic space and generate the unified representations for cross-modal data. Finally, we apply our OAAL on retrieval task, and the results of the experiments have verified its effectiveness.

Learning to detect and understand drug discontinuation events from clinical narratives.

Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article, we assessed the ability of natural language processing (NLP) systems to unlock DDC information from clinical narratives automatically. We collected 1867 de-identified providers' notes from the University of Massachusetts Medical School hospital electronic health record system. Then 2 human experts chart reviewed those clinical notes to annotate DDC events and their reasons. Using the annotated data, we developed and evaluated NLP systems to automatically identify drug discontinuations and reasons at the sentence level using a novel semantic enrichment-based vector representation (SEVR) method for enhanced feature representation. Our SEVR-based NLP system achieved the best performance of 0.785 (AUC-ROC) for detecting discontinuation events and 0.745 (AUC-ROC) for identifying reasons when testing this highly imbalanced data, outperforming 2 state-of-the-art non-SEVR-based models. Compared with a rule-based baseline system for discontinuation detection, our system improved the sensitivity significantly (57.75% vs 18.31%, absolute value) while retaining a high specificity of 99.25%, leading to a significant improvement in AUC-ROC by 32.83% (absolute value). Experiments have shown that a high-performance NLP system can be developed to automatically identify DDCs and their reasons from providers' notes. The SEVR model effectively improved the system performance showing better generalization and robustness on unseen test data. Our work is an important step toward identifying reasons for drug discontinuation that will inform drug safety surveillance and pharmacovigilance.