Implicit Semantics Research Articles

Research on personalised knowledge graph recommendation algorithm based on self-supervised learning

A recommendation algorithm that integrates generative graph self-supervised learning and contrastive graph self-supervised learning is proposed in this paper to address the issues of noisy data and graph incompleteness in knowledge graphs. Firstly, implicit semantics are captured by the self-supervised generative learning method through the masking and reconstruction of the edges of knowledge graph triples. Secondly, a multi-level contrastive learning method is proposed, which includes strategies for edge deletion and feature dropout in the graph structure and embedding layers, respectively.Finally, a contrastive loss function is introduced for contrastive learning to improve the quality of the embedded representations, and the BPR loss is incorporated and combined with the loss from the graph self-supervised learning module to optimize the overall performance of the recommender system. The results demonstrate that on the Last.fm, MovieLens, and Yelp2018 datasets, the model proposed in this paper significantly improves Recall and NDCG evaluation metrics compared to other traditional methods.

A dynamic graph structural framework for implicit sentiment identification based on complementary semantic and structural information

Implicit sentiment identification has become the classic challenge in text mining due to its lack of sentiment words. Recently, graph neural network (GNN) has made great progress in natural language processing (NLP) because of its powerful feature capture ability, but there are still two problems with the current method. On the one hand, the graph structure constructed for implicit sentiment text is relatively single, without comprehensively considering the information of the text, and it is more difficult to understand the semantics. On the other hand, the constructed initial static graph structure is more dependent on human labor and domain expertise, and the introduced errors cannot be corrected. To solve these problems, we introduce a dynamic graph structure framework (SIF) based on the complementarity of semantic and structural information. Specifically, for the first problem, SIF integrates the semantic and structural information of the text, and constructs two graph structures, structural information graph and semantic information graph, respectively, based on specialized knowledge, which complements the information between the two graph structures, provides rich semantic features for the downstream identification task, and helps to understanding of the contextual information between implicit sentiment semantics. To deal with the second issue, SIF dynamically learns the initial static graph structure to eliminate the noise information in the graph structure, preventing noise accumulation that affects the performance of the downstream identification task. We compare SIF with mainstream natural language processing methods in three publicly available datasets, all of which outperform the benchmark model. The accuracy on the Puns of day dataset, SemEval-2021 task 7 dataset, and Reddit dataset reaches 95.73%, 85.37%, and 65.36%, respectively. The experimental results demonstrate a good application scenario for our proposed method on implicit sentiment identification tasks.

Self-Supervised pre-training model based on Multi-view for MOOC Recommendation

Recommendation strategies based on concepts of knowledge are gradually applied to personalized course recommendation to promote model learning from implicit feedback data. However, existing approaches typically overlook the prerequisite dependency between concepts, which is the significant basis for connecting courses, and they fail to effectively model the relationship between items and attributes of courses, leading to inadequate capturing of associations between data and ineffective integration of implicit semantics into sequence representations. In this paper, we propose Self-Supervised pre-training model based on Multi-view for MOOC Recommendation (SSM4MR) that exploits non-explicit but inherently correlated features to guide the representation learning of users’ course preferences. In particular, to keep the model from relying solely on course prediction loss and overmphasising on the final performance, we treat the concepts of knowledge, course items and learning paths as different views, then sufficiently model the intrinsic relevance among multi-view through formulating multiple specific self-supervised objectives. As such, our model enhances the sequence representation and ultimately achieves high-performance course recommendation. All the extensive experiments and analyses provide persuasive support for the superiority of the model design and the recommendation results.

Label-Aware Calibration and Relation-Preserving in Visual Intention Understanding.

Visual intention understanding is a challenging task that explores the hidden intention behind the images of publishers in social media. Visual intention represents implicit semantics, whose ambiguous definition inevitably leads to label shifting and label blemish. The former indicates that the same image delivers intention discrepancies under different data augmentations, while the latter represents that the label of intention data is susceptible to errors or omissions during the annotation process. This paper proposes a novel method, called Label-aware Calibration and Relation-preserving (LabCR) to alleviate the above two problems from both intra-sample and inter-sample views. First, we disentangle the multiple intentions into a single intention for explicit distribution calibration in terms of the overall and the individual. Calibrating the class probability distributions in augmented instance pairs provides consistent inferred intention to address label shifting. Second, we utilize the intention similarity to establish correlations among samples, which offers additional supervision signals to form correlation alignments in instance pairs. This strategy alleviates the effect of label blemish. Extensive experiments have validated the superiority of the proposed method LabCR in visual intention understanding and pedestrian attribute recognition. Code is available at https://github.com/ShiQingHongYa/LabCR.

Kontrollierte Vokabularien und historische Semantiken

Abstract Controlled vocabularies are a common component in applications of linked data in order to connect digital resources. There are applications in the Humanities and digital editions, too. Two scenarios are described in some detail. The first concerns the analysis of annotations on manuscripts of Aristotle’s De interpretatione. In conjunction with transcriptions, the usage of authority files in metadata as well of domain specific vocabularies helps in this example to find stemmatological relations between glosses and diagrams in some of the manuscripts. The second example is not about a text-critical application but about the creation of the thesaurus for a commentary on Michael Maier’s Atalanta fugiens, that is a multimodal, figurative and dense written book by today’s standards. The question is raised as to what extent a controlled vocabulary with its concepts and the implicit semantics of the relations between them can be of a heuristic value for the reconstruction of this historical, alchemical concepts in its dynamics and alterity.

Targeted Adversarial Attack Against Deep Cross-Modal Hashing Retrieval

Deep cross-modal hashing has achieved excellent retrieval performance with the powerful representation capability of deep neural networks. Regrettably, current methods are inevitably vulnerable to adversarial attacks, especially well-designed subtle perturbations that can easily fool deep cross-modal hashing models into returning irrelevant or the attacker’s specified results. Although adversarial attacks have attracted increasing attention, there are few studies on specialized attacks against deep cross-modal hashing. To solve these issues, we propose a targeted adversarial attack method against deep cross-modal hashing retrieval in this paper. To the best of our knowledge, this is the first work in this research field. Concretely, we first build a progressive fusion module to extract fine-grained target semantics through a progressive attention mechanism. Meanwhile, we design a semantic adaptation network to generate the target prototype code and reconstruct the category label, thus realizing the semantic interaction between the target semantics and the implicit semantics of the attacked model. To bridge modality gaps and preserve local example details, a semantic translator seamlessly translates the target semantics and then embeds them into benign examples in collaboration with a U-Net framework. Moreover, we construct a discriminator for adversarial training, which enhances the visual realism and category discrimination of adversarial examples, thus improving their targeted attack performance. Extensive experiments on widely tested cross-modal retrieval datasets demonstrate the superiority of our proposed method. Also, transferable attacks show that our generated adversarial examples have well generalization capability on targeted attacks. The source codes and datasets are available at https://github.com/tswang0116/TA-DCH.

Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through ( k , 𝒫)-Core based Embedding

Expert finding is crucial for a wealth of applications in both academia and industry. Given a user query and trove of academic papers, expert finding aims at retrieving the most relevant experts for the query, from the academic papers. Existing studies focus on embedding-based solutions that consider academic papers’ textual semantic similarities to a query via document representation and extract the top- n experts from the most similar papers. Beyond implicit textual semantics, however, papers’ explicit relationships (e.g., co-authorship) in a heterogeneous graph (e.g., DBLP) are critical for expert finding, because they help improve the representation quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this article, we study expert finding on heterogeneous graphs by considering both the explicit relationships and implicit textual semantics of papers in one model. Specifically, we define the cohesive ( k , 𝒫)-core community of papers w.r.t. a meta-path 𝒫 (i.e., relationship) and propose a ( k , 𝒫)-core based document embedding model to enhance the representation quality. Based on this, we design a proximity graph-based index (PG-Index) of papers and present a threshold algorithm (TA)-based method to efficiently extract top- n experts from papers returned by PG-Index. We further optimize our approach in two ways: (1) we boost effectiveness by considering the ( k , 𝒫)-core community of experts and the diversity of experts’ research interests, to achieve high-quality expert representation from paper representation; and (2) we streamline expert finding, going from “extract top- n experts from top- m ( m> n ) semantically similar papers” to “directly return top- n experts”. The process of returning a large number of top- m papers as intermediate data is avoided, thereby improving the efficiency. Extensive experiments using real-world datasets demonstrate our approach’s superiority.

Imitation Learning-Based Implicit Semantic-Aware Communication Networks: Multi-Layer Representation and Collaborative Reasoning

Semantic communication has recently attracted significant interest from both industry and academia due to its potential to transform the existing data-focused communication architecture towards a more generally intelligent and goal-oriented semantic-aware networking system. Despite its promising potential, semantic communications and semantic-aware networking are still in their infancy. Most existing works focus on transporting and delivering the explicit semantic information, e.g., labels or features of objects, that can be directly identified from the source signal. The original definition of semantics as well as recent results in cognitive neuroscience suggest that it is the implicit semantic information, in particular the hidden relations connecting different concepts and feature items that play the fundamental role in recognizing, communicating, and delivering the real semantic meanings of messages. Motivated by this observation, we propose a novel reasoning-based implicit semantic-aware communication network architecture that allows destination users to directly learn a reasoning mechanism that can automatically generate complex implicit semantic information based on a limited clue information sent by the source users. Our proposed architecture can be implemented in a multi-tier cloud/edge computing networks in which multiple tiers of cloud data center (CDC) and edge servers can collaborate and support efficient semantic encoding, decoding, and implicit semantic interpretation for multiple end-users. We introduce a new multi-layer representation of semantic information taking into consideration both the hierarchical structure of implicit semantics as well as the personalized inference preference of individual users. We model the semantic reasoning process as a reinforcement learning process and then propose an imitation-based semantic reasoning mechanism learning (iRML) solution to learning a reasoning policy that imitates the inference behavior of the source user. A federated graph convolutional network (GCN)-based collaborative reasoning solution is proposed to allow multiple edge servers to jointly construct a shared semantic interpretation model based on decentralized semantic message samples. Extensive experiments have been conducted based on real-world datasets to evaluate the performance of our proposed architecture. Numerical results confirm that iRML offers up to 25.8 dB improvement on the semantic symbol error rate, compared to the semantic-irrelevant communication solutions.

Research on the Automatic Subject-Indexing Method of Academic Papers Based on Climate Change Domain Ontology

It is important to classify academic papers in a fine-grained manner to uncover deeper implicit themes and semantics in papers for better semantic retrieval, paper recommendation, research trend prediction, topic analysis, and a series of other functions. Based on the ontology of the climate change domain, this study used an unsupervised approach to combine two methods, syntactic structure and semantic modeling, to build a framework of subject-indexing techniques for academic papers in the climate change domain. The framework automatically indexes a set of conceptual terms as research topics from the domain ontology by inputting the titles, abstracts and keywords of the papers using natural language processing techniques such as syntactic dependencies, text similarity calculation, pre-trained language models, semantic similarity calculation, and weighting factors such as word frequency statistics and graph path calculation. Finally, we evaluated the proposed method using the gold standard of manually annotated articles and demonstrated significant improvements over the other five alternative methods in terms of precision, recall and F1-score. Overall, the method proposed in this study is able to identify the research topics of academic papers more accurately, and also provides useful references for the application of domain ontologies and unsupervised data annotation.

A Lightweight Sentiment Analysis Framework for a Micro-Intelligent Terminal

Sentiment analysis aims to mine polarity features in the text, which can empower intelligent terminals to recognize opinions and further enhance interaction capabilities with customers. Considerable progress has been made using recurrent neural networks or pre-trained models to learn semantic representations. However, recently published models with complex structures require increasing computational resources to reach state-of-the-art (SOTA) performance. It is still a significant challenge to deploy these models to run on micro-intelligent terminals with limited computing power and memory. This paper proposes a lightweight and efficient framework based on hybrid multi-grained embedding on sentiment analysis (MC-GGRU). The gated recurrent unit model is designed to incorporate a global attention structure that allows contextual representations to be learned from unstructured text using word tokens. In addition, a multi-grained feature layer can further enrich sentence representation features with implicit semantics from characters. Through hybrid multi-grained representation, MC-GGRU achieves high inference performance with a shallow structure. The experimental results of five public datasets show that our method achieves SOTA for sentiment classification with a trade-off between accuracy and speed.

Retranslating Skovoroda’s Conversation on Happiness into English: Language and Cultural Challenges

The article focuses on Hryhorii Skovoroda’s philosophical dialogue dedicated to the nature of human and happiness as a bright example of a harmonious fusion of philosophical ideas and individual style. A comparative analysis based on a hermeneutic approach helped to assess the equivalency in representing the lexical, syntactical, and emotional levels of the reconstructed Ukrainian version of Skovoroda’s dialogue via English translation, and thus contribute to clarifying the reliable strategies of translating a chronologically remote text of philosophical discourse. The research stresses the importance for a translator of philosophical works of Hryhorii Skovoroda to thoroughly consider the contextual meaning, inseparability of the concepts and style, and unique syntactic and emotive patterns of the source text conflating explicit and implicit semantics. In addition to deep understanding by translators of key philosophical terms, charged with contextual connotations, an equivalent translation of philosophic texts also demands sufficient knowledge of the related historical, cultural, and linguistic background to ensure a comprehensive perception of the intended senses and vectors of the mental activity by a reader.

Cross-Modal Semantic Communications

It is well known that multi-modal services, including video, audio, and haptic signals, aim to provide immersive experience with low latency and high reliability. Although multi-modal signals have differences in structure, transmission delay, and jitter, there are potential cross-modal relations among these multi-modal signals that can be explored to realize multi-modal signal processing and streaming. As a revolutionary communication paradigm, semantic communications can not only provide human-oriented services by delivering the intended meanings, but also break the modality barriers through compact semantic information extracted from multi-modal signals, which can help to achieve cross-modal applications. However, due to the polysemy and ambiguity issues, how to guarantee high reliability for semantic communications is a tremendous challenge. To overcome this dilemma, this article designs a cross-modal semantic communication paradigm that involves three modules: The cross-modal knowledge graph (CKG) provides essential background knowledge and signal patches for encoding and decoding respectively; the cross-modal semantic encoder infers potential implicit semantics to reduce encoding polysemy; and the cross-modal semantic decoder ensures consistency between source signals and recovered signals in terms of bit level and semantic level in order to reduce decoding ambiguity. They collaboratively improve the reliability of the communication system, which is verified by the simulation result.

Contribution of Effective Learning in Educational System

Nowadays Learning Management Systems (LMS) are not restricted to distant uses. Nevertheless, the pedagogical expressiveness of designed courses is strongly dependent on the teacher’s knowledge and expertise about the targeted platform. The funded Graphic project aims to help teachers in focusing on the specification of pedagogically sound and technically executable learning designs. To this end, we propose to support teachers by providing an LMS-specific Visual Instructional Design Language (VIDL). This paper proposes a specific LMS- centered approach for raising the pedagogical expressiveness of its implicit learning design semantics. We discussed, in accordance to the activity theory, how the LMS low-level parameterizations could be abstracted in order to build higher- level building blocks. Based on the Moodle application, we present and illustrate our approach by formalizing the abstract syntax of a Moodle-dedicated VIDL.

Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model

In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation.

Проблема согласования критериев действенности суждений в жанре политического интервью

The purpose of the study is to prove that the coordination of criteria for the effectiveness of judgments put forward in the genre of political interview is observed not only between the journalist and the respondent («in the first frame»), but also between the actual participants of the conversation and the target audience («in the second frame»). It is proved that the interviewer and the respondent postulate and ratify the criteria for the effectiveness of the initiated points of view not only among themselves, but also for the third participant of the conversation – the audience, which is regarded as the most important arbiter, accepting or rejecting the propositional content of the dialogical replicas that construct the conversation. Based on Yu. Habermas’ theory of communicative action it is stated that due to the media specifics of the implementation of a political interview, the audience is not able to coordinate the status of the criteria for the truth of the judgments of the interviewer and the respondent directly «in the first frame». The public discuss these criteria among themselves or with other audiences. Taking into account the methods developed in the conversational analysis, the political interview is investigated as a discursive phenomenon with a dual status, namely as a process of publishing relevant information about the current situation by the interviewer and the respondent and the product of formation and decision-making about the situation in the country and the world by representatives of society. Impartial expression of opinions is carried out in a political interview not only based on a system of regulated exchange of remarks, but also as a result of contextual production and interpretation of linguistic phenomena that implement explicit and implicit semantics. It is indicated that the differentiation between surface language forms and deep semantic content, explicit meanings and implicit meanings is relevant. The necessity of using pragmatic principles for the analysis of media discourse based on such concepts as implicatures of meaning, communicative intentionality is substantiated. The ratification of the effectiveness criteria approved for judgments in the context of a political interview is carried out taking into account the communicative strategies of production and interpretation of semantic content.

Parallel Texts and their Implicit Semantics in Baddi Ibno Al-Murabiti's Novel "Valleys of Thirst"

The contemporary Arab novel recorded a striking and competitive presence for the rest of the literary genres in keeping pace with political, cultural and social events and focusing on the depth of intellectual issues that take a human nature in general, and pertain to the Arab countries in particularAfter a number of novelists who renewed and rebelled against the usual and ready-made templates and wrote narrative texts focusing on what the parallel texts add to the narrative texts of hidden meanings, connotations and symbols. The main title of the novel, its cover, the titles of the novel's chapters and the textual thresholds will guide critical readings, as it is absolutely impossible to ignore its interpretive role.
 This can be seen in the novel "Valleys of Thirst" by the Mauritanian novelist "Baddy Ibno Al-Murabiti", where parallel texts with their intellectual, political and mythological dimensions appear to expose the miserable reality and expose the images that the regimes hide in.

Integrative model for discovering linked topics in science and technology

Linked topics in science and technology (LTSTs) can provide new avenues for technological innovation and are a key step in the transition from basic to applied research. This paper proposes a science and technology semantic linkage integration model for discovering LTSTs. Particularly, the integrative model fuses the term co-occurrence networks of basic and applied research, which expands the completeness of topic networks by enhancing the semantic characteristics of these networks. It is found that link prediction can further reinforce the semantic association of topic terms in networks between basic and applied topics. Simple fusion explicitly linked the topic terms, which can be used as automatic seed marking for subsequent link prediction to identify implicit linking of topic terms. Furthermore, an application to the gene-engineered vaccines field depicted that newly predicted implicit relations can effectively identify LTSTs. The results also show that implicit semantic recognition of LTSTs can be enhanced through simple fusion, while the recognition of LTST can be improved through link prediction. Therefore, the proposed model can assist experts to identify LTSTs that cannot be recognized through simple fusion. • The science and technology semantic linkage integration model improves the identification of linked topics in science and technology (LTSTs). • Simple fusion and link prediction form a twofold model to identify topics and implicit semantics. • Term co-occurrence networks of basic and applied research are fused. • The fusion expands topic networks and enhances their semantic associations. • LTST chains identified by connected LTST terms provide micro granularity.

Visualization of medical concepts represented using word embeddings: a scoping review

BackgroundAnalyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language processing (NLP), to learn dense and low-dimensional word representations from large unlabeled corpora that capture the implicit semantics of words. Models like Word2Vec, GloVe or FastText have been broadly applied and reviewed in the bioinformatics and healthcare fields, most often to embed clinical notes or activity and diagnostic codes. Visualization of the learned embeddings has been used in a subset of these works, whether for exploratory or evaluation purposes. However, visualization practices tend to be heterogeneous, and lack overall guidelines.ObjectiveThis scoping review aims to describe the methods and strategies used to visualize medical concepts represented using word embedding methods. We aim to understand the objectives of the visualizations and their limits.MethodsThis scoping review summarizes different methods used to visualize word embeddings in healthcare. We followed the methodology proposed by Arksey and O’Malley (Int J Soc Res Methodol 8:19–32, 2005) and by Levac et al. (Implement Sci 5:69, 2010) to better analyze the data and provide a synthesis of the literature on the matter.ResultsWe first obtained 471 unique articles from a search conducted in PubMed, MedRxiv and arXiv databases. 30 of these were effectively reviewed, based on our inclusion and exclusion criteria. 23 articles were excluded in the full review stage, resulting in the analysis of 7 papers that fully correspond to our inclusion criteria. Included papers pursued a variety of objectives and used distinct methods to evaluate their embeddings and to visualize them. Visualization also served heterogeneous purposes, being alternatively used as a way to explore the embeddings, to evaluate them or to merely illustrate properties otherwise formally assessed.ConclusionsVisualization helps to explore embedding results (further dimensionality reduction, synthetic representation). However, it does not exhaust the information conveyed by the embeddings nor constitute a self-sustaining evaluation method of their pertinence.

Image Sentiment Analysis via Active Sample Refinement and Cluster Correlation Mining

Training an effective image sentiment analysis model using high-quality samples and the implicit cross-modal semantics among heterogeneous features is still challenging. To address this problem, we propose an active sample refinement (ASR) strategy to obtain sufficient high-quality images with definite sentiment semantics. We mine the cluster correlation among the heterogeneous SENet features. Discriminative cross-modal semantics is generated to train an effective but robust image classifier. Ensemble learning is employed to further boost performance. Our method outperforms other competitive baselines, demonstrating its effectiveness and robustness. Meanwhile, the ASR strategy is a useful supplement to the current data augmentation method.

A hybrid query expansion framework for the optimal retrieval of the biomedical literature

With the proliferation of biomedical literature, it is quite challenging for biomedical scientists to keep them updated with the new advancements. In biomedical literature retrieval systems, the keywords in the user-defined queries are often defined with various lexical variants consequently leading to the vocabulary mismatch (VM). One possible way to cope with these issues is to introduce a query expansion (QE) framework to enrich the original queries with the auxiliary semantically similar terms for each keyword mentioned in a query. In this research, we propose a biomedical QE framework to alleviate the VM. The proposed approach combines the clinical diagnosis information (CDI) and word embeddings (WEs) simultaneously to retrieve the relevant biomedical literature. The process of embeddings vocabulary terms as real-valued and low dimensional vectors referred to as word embedding has garnered significant attention by potentially capturing the implicit semantics. We have exploited threefold word embeddings (Domain-Specific, Domain-Agnostic, and Hybrid) and integrated the embeddings outcomes with the CDI to get the best query combination for the efficient retrieval of biomedical literature. Experimental results procured for the Text REtrieval Conference dataset showed that CDI, when used with the hybrid word embeddings surpassed the WEs trained for the domain-specific and domain-agnostic data. The results demonstrate that the utilization of this unique setup of merging two techniques is a valuable addition to the QE process leading to significantly improved precision rate and VM in biomedical literature retrieval. We hope that our approach would assist investigators to use this query combination to retrieve relevant articles.