Fuzzy Semantics Research Articles

A least-squares framework for developing interval type-2 fuzzy semantics

The developing of IT2 fuzzy semantics is critical for computing with words (CWW), but exiting approaches lack flexibility and fail to adapt user’s diversified demands. The study proposes a least-squares framework for designing CWW encoders that construct interval type-2 fuzzy sets (IT2 FSs) to represent the semantic meanings of linguistic words. In the least-squares framework, an CWW encoder is characterized by two elements: an intra-uncertain semantic mapping and an inter-uncertain semantic family. The intra-uncertain semantic mapping transforms data intervals into type-1 fuzzy sets (T1 FSs), then an optimal IT2 FS is derived from the inter-uncertain semantic family using the least-squares method. Furthermore, several intra-uncertain semantic mappings are introduced, and a compatibility measure is defined to facilitate model selection. The least-squares framework benefits from the flexible selection of intra-uncertain semantic mappings and least-squares optimization-based construction of IT2 FSs. In experiments, the least-squares framework is applied to handle real-world online survey data and the large-scale online review data set of a Chinese life service review site, Dianping.com. Compared to the enhanced interval approach and the Hao-Mendel approach, the least-squares framework shows its favorable efficiency in experiments and statistical tests, and adapts to user-defined intra- and inter-uncertain semantic families.

Analyzing fuzzy semantics of reviews for multi-criteria recommendations

Hotel reviews play a vital role in tourism recommender system. They should be analyzed effectively to enhance the accuracy of recommendations which can be generated either from crisp ratings on a fixed scale or real sentiments of reviews. But crisp ratings cannot represent the actual feelings of reviewers. Existing tourism recommender systems mostly recommend hotels on the basis of vague and sparse ratings resulting in inaccurate recommendations or preferences for online users. This paper presents a semantic approach to analyze the online reviews being crawled from tripadvisor.in. It discovers the underlying fuzzy semantics of reviews with respect to the multiple criteria of hotels rather than using the crisp ratings. The crawled reviews are preprocessed via data cleaning such as stopword and punctuation removal, tokenization, lemmatization, pos tagging to understand the semantics efficiently. Nouns representing frequent features of hotels are extracted from pre-processed reviews which are further used to identify opinion phrases. Fuzzy weights are derived from normalized frequency of frequent nouns and combined with sentiment score of all the synonyms of adjectives in the identified opinion phrases. This results in fuzzy semantics which form an ideal representation of reviews for a multi-criteria tourism recommender system. The proposed work is implemented in python by crawling the recent reviews of Jaipur hotels from TripAdvisor and analyzing their semantics. The resultant fuzzy semantics form a manually tagged dataset of reviews tagged with sentiments of identified aspects, respectively. Experimental results show improved sentiment score while considering all the synonyms of adjectives. The results are further used to fine-tune BERT models to form encodings for a query-based recommender system. The proposed approach can help tourism and hospitality service providers to take advantage of such sentiment analysis to examine the negative comments or unpleasant experiences of tourists and making appropriate improvements. Moreover, it will help online users to get better recommendations while planning their trips.

Проблемы нейминга с позиции потенциальных имен собственных

The article discusses the main problems that the nominee has in the naming process. The main attitude is that future specialists, creating their name, primarily seek to express their own perception of onim without thinking about the consumer. The study is based on an analysis of hypothetical (potential) proper names (names) obtained as a result of a questionnaire of future lawyers. Using the example of collected names, it was possible to identify and describe the main errors made by nominees when creating the names of their own enterprises. It has been proven that in addition to originality, transparency of the name in terms of field of activity, memorability, the phenomenon of cognitive dissonance is the main cognitive mechanism for creating exotic names. The analysis of the collected onyms made it possible to identify several directions focused on the relationship between the cognitive-mental characteristics of the nominee and the recipient’s reflection. The following processes were attributed to such areas: standard onimization; word-forming creativity; case studies; foreign language creative naming. As a conclusion, we can state that future lawyers, like many nominees of existing organizations, are not only unfamiliar with the naming criteria, but also explicate absolute indifference towards customers and the consumer. The main violations of naming include: lack of communicative targeting; indifference to the consumer; creating names with fuzzy semantics (abbreviations, foreign-language words), as well as the use of precedent onyms that lead to the creation of cognitive dissonance in the chipper; impaired associative capabilities of onym. The result of the study is the conclusion that the study of the basic requirements and criteria for naming is the implementation of an integrated approach in the study of not only the creative capabilities of linguistics, but also the cultural-specific values of Russia.

A Comparative Study of the Fuzzy Semantics of Chinese and Russian Numerals

A Comparative Study of the Fuzzy Semantics of Chinese and Russian Numerals

FuzzyPPI: Large-Scale Interaction of Human Proteome at Fuzzy Semantic Space

FuzzyPPI: Large-Scale Interaction of Human Proteome at Fuzzy Semantic Space

Fuzzy order-sorted feature logic

Order-Sorted Feature (OSF) logic is a knowledge representation and reasoning language based on function-denoting feature symbols and set-denoting sort symbols ordered in a subsumption lattice. OSF logic allows the construction of record-like terms that represent classes of entities and that are themselves ordered in a subsumption relation. The unification algorithm for such structures provides an efficient calculus of type subsumption, which has been applied in computational linguistics and implemented in constraint logic programming languages such as LOGIN and LIFE and automated reasoners such as CEDAR. This work generalizes OSF logic to a fuzzy setting. We give a flexible definition of a fuzzy subsumption relation which generalizes Zadeh's inclusion between fuzzy sets. Based on this definition we define a fuzzy semantics of OSF logic where sort symbols and OSF terms denote fuzzy sets. We extend the subsumption relation to OSF terms and prove that it constitutes a fuzzy partial order with the property that two OSF terms are subsumed by one another in the crisp sense if and only if their subsumption degree is greater than 0. We show how to find the greatest lower bound of two OSF terms by unifying them and how to compute the subsumption degree between two OSF terms, and we provide the complexity of these operations.

Analysis of Fuzzy Semantic Translation in English Language and Literature Based on Bayesian Nonparametric Model

Abstract The development of machine learning provides a new path for translating fuzzy semantics into English and literature. In this paper, we propose translation points for fuzzy semantics based on the fuzziness of language by studying the linguistic features in English literature. The hierarchical PY process under the Bayesian nonparametric model is adopted to achieve word alignment in English translation, and the clustering process of English words is controlled by introducing discount and intensity parameters. Based on this, the parameter estimation of the word probability model is completed using Gibbs sampling, and the Moses decoder extracts the translation features. Based on monolingual word classification, this model reduces 6.85% in the average time consumed per sentence compared to the Beam algorithm and 9.57% compared to the A* algorithm.

Research on a Hotel Collaborative Filtering Recommendation Algorithm Based on the Probabilistic Language Term Set

In the face of problems such as information overload and the information cocoon resulting from big data, it is a key point of current research to solve the problem of semantic fuzziness of online reviews and improve the accuracy of personalized recommendation algorithms by using online reviews. Based on the advantage of the probabilistic language term set to deal with fuzzy information and the historical data of online hotel reviews, this paper proposes a collaborative filtering recommendation algorithm for hotels. Firstly, the text data of hotel online reviews are crawled by a crawler and processed by jieba and TF-IDF tools. Secondly, the hotel evaluation attribute set is constructed, and the sentiment analysis of the review statements is carried out with the help of the HowNet sentiment dictionary and manual annotation method. The probabilistic language term set is used to classify the data and derive statistics, and the maximum deviation method is used to determine the weight of each attribute. Then, the cosine similarity formula is fused with the modified cosine similarity formula to calculate the similarity and construct the decision matrix. Finally, combined with the historical data of the user’s hotel selection, the hotel recommendation results are generated. This paper collected review data from 10 hotels in Macau from the official “Ctrip” website. The proposed recommendation algorithm model was then applied to process and analyze the data, resulting in the generation of a ranked list of hotel recommendations. To validate the accuracy and effectiveness of this research, the recommendation results were compared with those produced by other algorithms.

Artificial Intelligence Method for Accurate Translation of Fuzzy Semantics in English Language and Literature

In order to address the drawbacks of semantic ambiguity, inaccurate quantifiers, and low translation accuracy in traditional grammar-based translation methods, this paper proposes an artificial intelligence translation method based on semantic analysis for English fuzzy semantics. Firstly, a comprehensive analysis of English language semantics was carried out from different semantic levels such as language, knowledge, and pragmatics, and the key points of fuzzy semantics were identified. Then, key feature quantities for accurate translation of fuzzy semantics in English vocabulary and literature were constructed, and artificial intelligence methods were used to optimize fuzzy semantics. The experimental results show that the proposed method can avoid semantic understanding ambiguity and improve the accuracy of English language translation.

A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning

Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.

Image semantic learning method based on social heterogeneous graph networks1

Image semantic learning techniques are crucial for image understanding and classification. In social networks, image data is widely disseminated thanks to convenient acquisition and intuitive expression characteristics. However, due to the freedom of users to publish information, the image has apparent context dependence and semantic fuzziness, which brings difficulties to image representation learning. Fortunately, social attributes such as hashtags carry rich semantic relations, which can be conducive to understanding the meaning of images. Therefore, this paper proposes a new method named Social Heterogeneous Graph Networks (SHGN) for image semantic learning in social networks. First, a heterogeneous graph is built to expand image semantic relations by social attributes. Then the consistent semantic space is reconstructed through cross-media feature alignment. Finally, an image semantic extended learning network is designed to capture and integrate the social semantics and visual feature, which obtains a rich semantic representation of images from a social context. The experiments demonstrate that SHGN can achieve efficient image representation, and favorably against many baseline algorithms.

Selection and study of fuzzy semantics in machine translation

In daily life, English is increasingly used in various scenarios, and the technology of translation using machines and others is gradually maturing, but there are still cases of inaccurate translation. To enhance translation’s accuracy, this study optimizes the method of fuzzy semantic selection, and then optimizes the method of domain analysis combined with neural networks to improve the accuracy of machine translation in different domains. The accuracy of the optimized neural network tends to be stable when the number of iterations is 15, the accuracy is 0.96, the accuracy of the traditional neural network is 0.91, and RNN is 0.82. Compared with the benchmark system, the bilingual mapping model has increased by 0.67% in the news field and 0.56% in the education field. The precision, recall and F value of machine translation are 93%, 86% and 0.8 respectively. The comprehensive experimental results show that the selection method based on fuzzy semantics, combined with the domain analysis method of network neural, can remarkably enhance the accuracy.

A novel approach for ranking intuitionistic fuzzy numbers and its application to decision making

Ranking intuitionistic fuzzy numbers is an important issue in the practical application of intuitionistic fuzzy sets. Many scholars rank intuitionistic fuzzy numbers by defining different measures. These measures do not comprehensively consider the fuzzy semantics expressed by membership degree, nonmembership degree, and hesitancy degree. As a result, the ranking results are often counterintuitive, such as the indifference problems, the non-robustness problems, etc. In this paper, according to geometrical representation, a novel measure for intuitionistic fuzzy numbers is defined, which is called the ideal measure. After that, a new ranking approach is proposed. It’s proved that the ideal measure satisfies the properties of weak admissibility, membership degree robustness, nonmembership degree robustness, and determinism. A numerical example is applied to illustrate the effectiveness and feasibility of this method. Finally, using the presented approach, the optimal alternative can be acquired in multi-attribute decision-making problems. Comparison analysis shows that the ideal measure is more effective and simple than other existing methods.

Fuzzy Semantics for Arbitrary-Shaped Scene Text Detection.

To robustly detect arbitrary-shaped scene texts, bottom-up methods are widely explored for their flexibility. Due to the highly homogeneous texture and cluttered distribution of scene texts, it is nontrivial for segmentation-based methods to discover the separatrixes between adjacent instances. To effectively separate nearby texts, many methods adopt the seed expansion strategy that segments shrunken text regions as seed areas, and then iteratively expands the seed areas into intact text regions. In seek of a more straightforward way that does not rely on seed area segmentation and avoid possible error accumulation brought by iterative processing, we propose a redundancy removal strategy. In this work, we directly explore two types of fuzzy semantics-text and separatrix-that do not possess specific boundaries, and separate cluttered instances by excluding the separatrix pixels from text regions. To deal with the fuzzy semantic boundaries, we also conduct reliability analysis in both optimization and inference stage to suppress false positive pixels at ambiguous locations. Experiments on benchmark datasets demonstrate the effectiveness of our method.

An Artificial Intelligence Method for Automatic Assessment of Fuzzy Semantics in English Literature

An Artificial Intelligence Method for Automatic Assessment of Fuzzy Semantics in English Literature

MV-Datalog+-: Effective Rule-based Reasoning with Uncertain Observations

AbstractModern applications combine information from a great variety of sources. Oftentimes, some of these sources, like machine-learning systems, are not strictly binary but associated with some degree of (lack of) confidence in the observation. We propose MV-Datalog and $\mathrm{MV-Datalog}^\pm$ as extensions of Datalog and $\mathrm{Datalog}^\pm$ , respectively, to the fuzzy semantics of infinite-valued Łukasiewicz logic $\mathbf{L}$ as languages for effectively reasoning in scenarios where such uncertain observations occur. We show that the semantics of MV-Datalog exhibits similar model theoretic properties as Datalog. In particular, we show that (fuzzy) entailment can be decided via minimal fuzzy models. We show that when they exist, such minimal fuzzy models are unique and can be characterised in terms of a linear optimisation problem over the output of a fixed-point procedure. On the basis of this characterisation, we propose similar many-valued semantics for rules with existential quantification in the head, extending $\mathrm{Datalog}^\pm$ .

Improving the performance of semantic graph-based keyword extraction and text summarization using fuzzy relations in Hindi Wordnet

In this study, a Fuzzy Semantic Graph-based approach is proposed to extract keywords and generate extractive text summaries from Hindi text documents. Hindi Wordnet is used as a knowledge source to construct the semantic graph. As the semantic relations defined in Hindi Wordnet are crisp, they do not capture the semantic relationship as a matter of degree. Due to that, many terms are represented as not being related, while these can share some meaningful relationship as per real-life scenarios. To overcome this curb of Hindi Wordnet, the paper presents several fuzzy semantic associations between such terms by assigning a value ranging from 0 to 1 to such relations. While constructing the semantic graph to represent documents using Hindi Wordnet semantic relations, the terms sharing fuzzy semantic relations are also added to enhance the quality of the graph. The experiments are done to extract potential keywords and to generate a good content summary. It is observed that such semantics generate a more accurate summary and produce prospective keywords for the document. The performance of the proposed approach fuzzy-based semantic graph is compared to semantic graph-based approach for keyword extraction and text summarization. The keywords extracted and the summary generated by the proposed approach is match up to human extracted keywords and human-generated text summary. The proposed approach results are evaluated using precision, recall, and f-measure. Different outcomes of generated text summaries are evaluated using the ROUGE matrix. The results of the proposed approach are pretty encouraging.

Construction of English Translation Model Based on Improved Fuzzy Semantic Optimal Control of GLR Algorithm

Data point overlap exists in the model translation recognition results of generalized maximum likelihood ratio detection (GLR) algorithm. A fuzzy semantic optimal control intelligent recognition model for English translation based on improved GLR algorithm is proposed. This algorithm is used to create a phrase corpus for marking tens of thousands of English and Chinese words, so that phrases can be searched automatically. The algorithm builds a phrase corpus of about 710,000 Chinese and English words. Phrase structure is constructed through phrase centers. Partial speech recognition results can be obtained. According to the syntactic function of analytic linear list, the ambiguity of Chinese and English structures in part of speech recognition results is corrected. Finally get the content of the identifier on the basis of comprehensive evaluation. The recognition accuracy based on the improved algorithm is more than 95%. The overall score was 92.3. This algorithm overcomes the disadvantages of GLR. Compared with statistical algorithm and dynamic memory algorithm, the algorithm improves the operation speed and processing performance and is more suitable for machine translation tasks. It provides a new idea in the field of machine translation.

Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control.

This work addresses four aspects of the English translation model: consistency, model structure, semantic understanding, and knowledge fusion. To solve the problem of lack of personality consistency in the responses generated by neural networks in English translation models, an English translation model with fuzzy semantic optimal control of neural networks is proposed in this study. The model uses a fuzzy semantic optimal control retrieval mechanism to obtain appropriate information from an externally set English information table; to further improve the effectiveness of the model in retrieving correct information, this work adopts a two-stage training method, using ordinary English translation data for model pretraining and then fine-tuning the model using English translation data with optimal control containing fuzzy semantic information. The model consists of two parts, a sequence generation network that can output the probability distribution of words and an evaluation network that can predict future whole-sentence returns. In particular, the evaluation network can evaluate the impact of currently generated words on whole sentences using deep inheritance features so that the model can consider not only the optimal solution for the current words, as in other generative models, but also the optimal solution for future generated whole sentences. The experimental results show that the English translation model with fuzzy semantic optimal control of the neural network proposed in this study can obtain better semantic feature representation by using a novel bidirectional neural network and a masked language model to train sentence vectors; the combination of semantic features and fuzzy semantic similarity features can obtain higher scoring accuracy and better model generalization. In English translation applications, there are large improvements in scoring accuracy and generality.

A fuzzy semantic representation and reasoning model for multiple associative predicates in knowledge graph

As the latest achievement of the development in semiotics, knowledge graph has been recognized and widely used by more and more researchers for its rich semantic information and clear logical structure. How to discovery the deep relevant knowledge from the massive graph-structured data has become a hot spot of artificial intelligence. Considering that some predicates in knowledge graph express fuzzy relationships whose semantics are not certain, the basic schema of classical knowledge graph in the form of RDF triple cannot describe the fuzzy semantic information effectively. To counter above problems, in this paper, we present a new semantic representation and reasoning model for multiple associative predicates by introducing fuzzy theory. Concretely, the presented method defines a new fuzzy annotating strategy to represent the fuzzy semantics between associative predicates in different RDF triples. On this basis, some fuzzy reasoning rules are presented to realize fuzzy semantic extension for classical knowledge graph. Lastly, the experimental results show that our proposal can discover more implicit valid knowledge with fuzzy semantic and have a good consistency with the intuition of human judgments. Overall, the methods proposed in this paper constitute some effective ways of knowledge discovery of structured semantic data.