Semantic-based Model Research Articles

An ontology-based agriculture decision-support system with an evidence-based explanation model

Effective management of plant diseases and pests requires knowledge that covers multiple domains. At the same time, retrieving the relevant information in a timely manner is always challenging, due to the unstructured nature of agricultural data. Over the years, efforts have been made to develop an ontology-based Decision-Support System (DSS) to facilitate the diagnosis and control of plant diseases. Some major issues with these systems are that: (1) they do not adopt the full extent of the ontological constructs to represent domain entities, which, in turn, reduces reasoning capabilities and prevents systems from being more intelligent, (2) they do not adequately provide the desired level of knowledge to support complex decisions, which requires many factors to be considered, (3) they do not adequately explain or provide evidence to demonstrate the validity of the system's outputs. To address these limitations, we present a novel system termed Agriculture Ontology Based Decision Support System (AgrODSS), which aims to assist in plant disease and pest identification and control. AgrODSS architecture consists of two semantic-based models. First, we developed Plant Diseases and Pests Ontology (PDP-O) to capture, model, and represent diseases and pest knowledge in a machine-understandable format. Second, we designed and developed an Evidence-Based Explanation Model (EBEM) that points to related evidence from the literature to demonstrate the validity of the system outputs. We demonstrate the effectiveness of AgrODSS by executing various queries via AgrODSS SPARQL Endpoint and obtaining valuable information to support decision-making. Finally, we evaluated AgrODSS practically with domain experts (including entomologists and pathologists) and it produced similar answers to those given by the experts, with an overall accuracy of 80.66%. These results demonstrate AgrODSS's ability to assist agricultural stakeholders in making proper disease or pest diagnoses and choosing the appropriate control methods.

A semantic-based model with a hybrid feature engineering process for accurate spam detection

Detecting spam emails is essential to maintaining the security and integrity of email communication. Existing research has made significant progress in developing effective spam detection models, but challenges remain in improving classification performance and adaptability to evolving spamming techniques. In this study, we propose a novel spam detection model with a comprehensive feature engineering approach that combines term frequency-inverse document frequency (TF-IDF) vectorizer and word embedding features to optimize the feature space. Our contribution lies in integrating semantic-based word embeddings, leveraging pre-existing knowledge to capture the semantic meaning of words and enhance the representation of email texts. To identify the most suitable word embedding technique for our model, we evaluated GloVe, Word2Vec, and FastText. GloVe was selected for its better performance, which is the result of its pre-training on a large and diverse text corpus. Furthermore, the model was evaluated without word embeddings, which did not exhibit the same effectiveness level as our word embedding-based model. Additionally, we utilized the support vector machine as a classifier and hyperparameter tuning technique to identify our model’s most effective parameter values. The proposed model was tested on two datasets. The experimental results showed that our model outperformed the other models discussed in the literature, achieving an accuracy of 99.5% on the SpamAssassin dataset, and 99.28% on the Enron-Spam dataset.

SMFSOP: A semantic-based modelling framework for student outcome prediction

Over the past two decades, studying the various factors affecting student performance became essential. Knowing these factors assist in enhancing student’s performance, teaching practices and policy decisions. This research proposes a framework named “Semantic-based Modeling Framework for Student Outcome Prediction” (SMFSOP), to automatically map students’ activities within their learning environment to a standardized behavioral model (Community of Inquiry model (CoI)). The generated student representation is utilized to cluster students and predict an outcome based on their cluster. The framework is divided into three phases: Data gathering and pre-processing, automated mapping, clustering and prediction. The automatic mapping uses semantic similarity between student attribute names/descriptions, and CoI model indicators. Path and BERT similarities were identified as the best performers compared to human annotators. K-means, DBSCAN, and Kernel K-means are used for the clustering step, followed by LassoCV for regression-based prediction, & K-nearest neighbors for classification-based prediction. In order to prove that the proposed framework is generally applicable, three real life datasets were used as a case study. Best-performing trials enhanced outcome prediction as follows: In StudentLife Dataset, Adjusted R2 is enhanced by 3% (95% to 98%), and MSE decreased by 2.375 % (0.126 to 0.031). In social network dataset, Adjusted R2 was enhanced by 17% (65% to 82%). The MSE decreased by 4.4% (0.164 to 0.12). For the “Open university learning Analytics dataset” (OULAD), accuracy is improved by 1.56%, F1-score enhanced by 0.014. Precision is enhanced by 3.1%.

Managing Complex Knowledge in Sustainable Planning: A Semantic-Based Model for Multiagent Water-Related Concepts

The concepts of green infrastructures, nature-based solutions and ecosystem services are today considered an integral part of the broader theme of the urban bioregion, with an intrinsic character of complexity. It is certainly difficult to structure bioregional processes in a balanced and sustainable way, able to keep local energy production and consumption cycles closed. It is a complex issue of knowledge bases, and problems are increased by the participatory dimension of environmental planning. In fact, when rational planning models have failed in the face of prominent individual needs and environmental complexity, a path has emerged towards the inclusion of multiple citizens’ and stakeholders’ knowledge. The cognitive structure of the plans has thus changed from systems of exclusively expert, formal knowledge to systems of diffused, multi-agent knowledge. This has involved richness but also significant problems in understanding and managing knowledge bases. In this complexity, there are some common peculiarities when it comes to socio-environmental systems. A common feature of the reference domains of ecosystem services, nature-based solutions and green infrastructures is the water resource. A management model of hydrological data, which are structurally relevant and cross-sectoral in environmental planning actions, could represent a flagship initiative. The used approach could be conveyed to more complex and extensive areas of the environmental domain in a perspective of sustainable planning. The present paper is part of a research work oriented toward handling complex environmental subjects, such as green infrastructures, nature-based solutions or ecosystem services, with a knowledge modelling approach. This approach is based on semantic extensions, elaborated form the concept of semantic web, to allow shared interpretations of knowledge coming from different languages and scientific domains. It is also based on using applied ontologies, elaborated from the concept of ontology-based classification, to support a structured organization of knowledge contents. The main research objective is therefore to investigate about a knowledge management system with semantic extensions, populated with hydrological knowledge contents, as well as to propose a preliminary functional architecture. A simple ontology of data is extracted, aiming at clarifying and improving inter-domain communication, so as to enhance a common semantic understanding in a complex environmental system.

Enabling building digital twin: Ontology-based information management framework for multi-source data integration

The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.

Artificial Intelligence of Manufacturing Robotics Health Monitoring System by Semantic Modeling.

Robotics is widely used in nearly all sorts of manufacturing. Steady performance and accurate movement of robotics are vital in quality control. Along with the coming of the Industry 4.0 era, oceans of sensor data from robotics are available, within which the health condition and faults are enclosed. Considering the growing complexity of the manufacturing system, an automatic and intelligent health-monitoring system is required to detect abnormalities of robotics in real-time to promote quality and reduce safety risks. Therefore, in this study, we designed a novel semantic-based modeling method for multistage robotic systems. Experiments show that sole modeling is not sufficient for multiple stages. We propose a descriptor to conclude the stages of robotic systems by learning from operational data. The descriptors are akin to a vocabulary of the systems; hence, semantic checking can be carried out to monitor the correctness of operations. Furthermore, the stage classification and its semantics were used to apply various regression models to each stage to monitor the quality of each operation. The proposed method was applied to a photovoltaic manufacturing system. Benchmarks on production datasets from actual factories show the effectiveness of the proposed method to realize an AI-enabled real-time health-monitoring system of robotics.

A semantic recommender algorithm for 3D model retrieval based on deep belief networks

Interest in 3D modelling is growing, however, the retrieval results achieved for semantic-based 3D model retrieval systems have been disappointing. In this paper, we propose a novel semantic recommendation algorithm based on a deep belief network (DBN-SRA) to implement semantic retrieval with potential semantic correlations [between models] being achieved using deep learning from known model samples. The algorithm uses the feature correlation [between the models] as the conditions to enable semantic matching of 3D models to obtain the final recommended retrieval result. Our proposed approach has been shown to improve the effectiveness of 3D model retrieval in terms of both retrieval time and importantly accuracy. Additionally, our study and our reported results suggest that our posited approach will generalise to recommender systems in other domains which are characterised by multiple feature relationships.

SaaS level database integration in cloud environment through database as a service

Data retrieval from multiple heterogeneous cloud databases is a complex engineering task. Ensuring scalability, flexibility and data availability while integrating data from the multiple heterogeneous cloud databases are the major research issues in such task. A proper integration semantics for cloud databases and data retrieval through Database as a Service (DBaaS) can be helpful to solve those issues. In this paper, a graph semantic based model for SaaS level database integration (SLDI) has been proposed, which is useful towards realization of various cloud database integration approaches through services. Several important quality metrics like integration complexity, degree of consistency, degree of availability and degree of partitioning tolerance also have been defined over the proposed model. The proposed SLDI model is compatible with CAP (Consistence-Availability-Partitioning) theorem. Besides, the proposed approach is capable of handling data heterogeneity through service while ensuring scalability and flexibility in data retrieval from multiple cloud databases.

A Game Theory Approach for Multi-document Summarization

In today’s era, information has been growing exponentially on the web, due to which extraction of relevant and concise information has become a challenging task. To overcome the above problem, a fundamental tool known as summarization techniques has been used for understanding and organizing such large datasets. Recently, researchers have been devoting a lot of effort to develop semantics-based models, so as to improve summarization performance. In this paper, a versatile and principled game theory-based multi-document summarization framework integrated with Wikipedia ontology is proposed. The framework exploits the submodularity hidden in underlying ontology and is optimized using the proposed improved algorithm, to enhance the summarization performance. Results of the proposed approach were evaluated with the ROUGE evaluation metric for different multi-document summarization tasks against human-generated summaries and it outperformed DUC, TAC competitors, and other competitive methods.

A Semantic-Based Model for Triage Patients in Emergency Departments

Triage is a process performed in an emergency department that aims to sort patients according to their need for care. When performed speedily and correctly, this process can potentially increase the chances of survival for a patient with serious complications. This study aims to develop a computer model, called UbiTriagem, which supports the process of triage using the concepts of web semantics and ubiquitous computing focused on healthcare. For evaluating the proposal, we performed an analysis of scenario-driven triage based on previously determined ratings. In addition, we conducted a usability evaluation in emergency department with the developed prototype with two user groups: nurses and patients. The main scientific contribution is the automatic triage assessment based on the gathering of patient data on mobile devices, performed automatically through the use of a reasoning technique in an ontology. The results for all evaluations were very positive. The automatic triage assessment has been assertive in 93.3% of the cases and, after adjustments in the model, in 100% of the cases. Regarding user satisfaction, we obtained rates of 98.7% and 96% when considering perception of utility and ease of use, respectively.

Harnessing Semantic Features for Large-Scale Content-Based Hashtag Recommendations on Microblogging Platforms

Twitter is one of the most popular microblog service providers, in this microblogging platform users use hashtags to categorize their tweets and to join communities around particular topics. However, the percentage of messages incorporating hashtags is small and the hashtags usage is very heterogeneous as users may spend a lot of time searching the appropriate hashtags for their messages. In this paper, the authors present an approach for hashtag recommendations in microblogging platforms by leveraging semantic features. Moreover, they conduct a detailed study on how the semantic-based model influences the final recommended hashtags using different ranking strategies. Also, users are interested by fresh and specific hashtags due to the rapid growth of microblogs, thus, the authors propose a time popularity ranking strategy. Furthermore, they study the combination of these ranking strategies. The experiment results conducted on a large dataset; show that their approach improves respectively lexical and semantic based recommendation by more than 11% and 7% on recommending 5 hashtags.

Design of user-centric semantic rights model for validation of user-generated content

In these days, the creative user really enjoys creating digital items and sharing their works with other people on the Web. Most users, who create digital contents, want to make secured packages of their works and distribute them with the attachment of valid licenses. Current digital rights management (DRM) systems, however, do not provide the functionality that supports the requirement of the creative users who are considered as just consumers by the current available DRM systems. To make the user-centric DRM functionality possible, we found that license management should be more intelligent to enable users make appropriate licenses for the secured distribution of their created works. In this paper, we define the semantic-based rights expression and management model for the user generated content. Each user created content can have one or more licenses with different types, reproduction, distribution, and usage. Based on our semantic license model and big data analytics, we can support a new business model in which users can sell and buy their created digital items in a secure environment.

An improved horizontally reversible plow design based on virtual assembly semantics and constraint

There exit model errors in the constructed Horizontally reversible plough (HRP) structure through Interference check technique (ICT). This is basically due to missing parts and geometric interference and, hence, has significantly adverse effects in improving HRP. In this paper an improved design to refine the three dimensional (3D) model of HRP is implemented by using Virtual assembly technology (VAT). To achieve this, the authors propose a combined virtual assembly semantics and constraint for assembly planning and simulation of HRP in the commercial software, e.g., SolidWorks. First, the assembly planning of HRP was captured with a semantics-based model, including spatial position, assembly orientation, type and parameters of the semantic entities; secondly, the assembly simulation of HRP was performed by using constraint-based VAT, e.g. component grasping, moving and releasing; finally, the obtained HRP model was verified through ICT again. The results demonstrate that not any model error exists in the refined 3D HRP model any longer and that semantics and constrain based VAT can support the interactive operation more effectively and accurately than those with geometric constraints. This technique was previously used for HRP model. Based on the refined 3D HRP model, the design improvement of Remote cylinder base (RCB), an important component of HRP, will be forthcoming in a future paper.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen’s semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the “Pandit-Hinch-Niederer” (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

MWI-Sum

Multidocument summarization addresses the selection of a compact subset of highly informative sentences, i.e., the summary, from a collection of textual documents. To perform sentence selection, two parallel strategies have been proposed: (a) apply general-purpose techniques relying on data mining or information retrieval techniques, and/or (b) perform advanced linguistic analysis relying on semantics-based models (e.g., ontologies) to capture the actual sentence meaning. Since there is an increasing need for processing documents written in different languages, the attention of the research community has recently focused on summarizers based on strategy (a). This article presents a novel multilingual summarizer, namely MWI-Sum (Multilingual Weighted Itemset-based Summarizer), that exploits an itemset-based model to summarize collections of documents ranging over the same topic. Unlike previous approaches, it extracts frequent weighted itemsets tailored to the analyzed collection and uses them to drive the sentence selection process. Weighted itemsets represent correlations among multiple highly relevant terms that are neglected by previous approaches. The proposed approach makes minimal use of language-dependent analyses. Thus, it is easily applicable to document collections written in different languages. Experiments performed on benchmark and real-life collections, English-written and not, demonstrate that the proposed approach performs better than state-of-the-art multilingual document summarizers.

Uncovering highly obfuscated plagiarism cases using fuzzy semantic-based similarity model

Highly obfuscated plagiarism cases contain unseen and obfuscated texts, which pose difficulties when using existing plagiarism detection methods. A fuzzy semantic-based similarity model for uncovering obfuscated plagiarism is presented and compared with five state-of-the-art baselines. Semantic relatedness between words is studied based on the part-of-speech (POS) tags and WordNet-based similarity measures. Fuzzy-based rules are introduced to assess the semantic distance between source and suspicious texts of short lengths, which implement the semantic relatedness between words as a membership function to a fuzzy set. In order to minimize the number of false positives and false negatives, a learning method that combines a permission threshold and a variation threshold is used to decide true plagiarism cases. The proposed model and the baselines are evaluated on 99,033 ground-truth annotated cases extracted from different datasets, including 11,621 (11.7%) handmade paraphrases, 54,815 (55.4%) artificial plagiarism cases, and 32,578 (32.9%) plagiarism-free cases. We conduct extensive experimental verifications, including the study of the effects of different segmentations schemes and parameter settings. Results are assessed using precision, recall, F-measure and granularity on stratified 10-fold cross-validation data. The statistical analysis using paired t-tests shows that the proposed approach is statistically significant in comparison with the baselines, which demonstrates the competence of fuzzy semantic-based model to detect plagiarism cases beyond the literal plagiarism. Additionally, the analysis of variance (ANOVA) statistical test shows the effectiveness of different segmentation schemes used with the proposed approach.

Correlation-based concept-oriented bisecting k-means clustering and topic detection for scientific literature and news tracks

Extracting relevant documents from a larger document corpus is a challenging task. The process of clustering groups together the documents sharing similar topics. Incorporating semantic features will improve the accuracy of document clustering methods. Topic detection deals with discovering meaningful and concise labels for the clusters. In this paper, we propose a clustering algorithm named as correlation-based concept-oriented bisecting k-means algorithm using semantic-based similarity measure. This algorithm uses our existing modified semantic-based model in which related terms are extracted as concepts for concept-based document clustering and topic discovery method. The performance of the proposed work is compared with the existing term-based method and also with our earlier work on concept based algorithm. Additional experiments are conducted to demonstrate the ability of the proposed correlation-based concept-oriented bisecting k-means algorithm considering terms only, synonyms and hyponyms and correlated using F-measure and purity as evaluation metrics. Experimental results demonstrate the performance enhancement of the proposed algorithm.

A semantic model for general purpose content-based image retrieval systems

The ‘semantic gap’ is the main challenge in content-based image retrieval. To overcome this challenge, we propose a semantic-based model to retrieve images efficiently with considering user-interested concepts. In this model, an interactive image segmentation algorithm is carried out on the query image to extract the user-interested regions. To recognize the image objects from regions, a neural network classifier is used in this model. In order to have a general-purpose system, no priori assumptions should be made regarding the nature of images in extracting features. So a large number of features should be extracted from all aspect of the image. The high dimensional feature space, not only increases the complexity and required memory, but also may reduce the efficiency and accuracy. Hence, the ant colony optimization algorithm is employed to eliminate irrelevant and redundant features. To find the most similar images to the query image, the similarity between images is measured based on their semantic objects which are defined according to a predefined ontology.

A Semantic-Based Indexing for Indoor Moving Objects

The increasing availability of indoor positioning, driven by techniques like RFID, Bluetooth, and smart phones, enables a variety of indoor location-based services (LBSs). Efficient queries based on semantic-constraint in indoor spaces play an important role in supporting and boosting LBSs. However, the existing indoor index techniques cannot support these semantic constraints-based queries. To solve this problem, this paper addresses the challenge of indexing moving objects in indoor spaces, in which both the moving objects and the indoor environments include semantic meanings. We present an indoor semantic-based model, which gives the formal description of semantics of indoor cells and moving objects. Then, a new semantic-based index is proposed for indoor environment, which can support queries under semantic constraint. In addition, we develop efficient algorithms for two new queries, which are the nearest neighbor query based on semantic constraints for static indoor cells and moving objects, respectively. The conducted experiment demonstrates that the proposed index structure is effective, robust, and efficient.

Semantic-Based Three-Dimensional Modeling of Virtual Reality Scenes

Virtual reality scenes three-dimensional modeling usually is constructed on the basis of learning with virtual data, and learning behavior is too dependent on virtual data.It is difficult to quickly and accurately reflect characteristics of 3D modeling in virtual reality, and learning complexity is higher. In this paper, a method of virtual reality scenes three-dimensional modeling based on semantic was presented on the basis of analysis of the virtual scenes modeling methods. The modeling system architecture using the method is divided into physical model libraries, three-dimensional model semantic knowledge base, semantic-based visual modeling and scene graph automatically generation modules. The experimental results show that the detection performance is better than the results of the three-dimensional modeling based on virtual data, and the system increases flexibility and usability of the three-dimensional modeling,