Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis

Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

SNOMED CT: A Clinical Terminology but Also a Formal Ontology

Context and Objective: Over the past few decades, terminologies developed for clinical descriptions have been increasingly used as key resources for knowledge management, data integration, and decision support to the extent that today they have become essential in the biomedical and health field. Among these clinical terminologies, some may possess the characteristics of one or several types of representation. This is the case for the Systematized Nomenclature of Human and Veterinary Medicine—Clinical Terms (SNOMED CT), which is both a clinical medical terminology and a formal ontology based on the principles of semantic web. Methods: We present and discuss, on one hand, the compliance of SNOMED CT with the requirements of a reference clinical terminology and, on the other hand, the specifications of the features and constructions of descriptive of SNOMED CT. Results: We demonstrate the consistency of the reference clinical terminology SNOMED CT with the principles stated in James J. Cimino’s desiderata and we also show that SNOMED CT contains an ontology based on the EL profile of OWL2 with some simplifications. Conclusions: The duality of SNOMED CT shown is crucial for understanding the versatility, depth, and scope in the health field.

Comparative Ontology of Theories of Space and Time

With a few exceptions, physics theories are based in a conception of time and space;our two major theories, general relativity, and quantum field theory, differ in their conceptions. Key issues herein include mathematics, logic, intuition, experiment, and ontology, with emphasis on simultaneity and dimensionality of the world. The treatment is through ontological comparison of two theories, space-time theory (special relativity) and energy-time theory (local absolute space and universal time). These two theories share many of the same equations but have different ontology.

Ontology-Based News Linking for Semantic Temporal Queries

Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

The Ontology of Velocity

Special relativity formulates a world partitioned into frames in relative motion;absolute motion is prohibited by axiom: no preferred frame, with consequences for the ontology of velocity. The best guide to physical reality is experiment, so ontology of velocity is investigated in the context of primordial field theory in terms of three experiments: Michelson-Morley, Michelson-Gale and Hafele-Keating experiments.

Ontology of Relativistic Mass

The term “relativistic mass” defined by equation m=γm0 with γ=(1-v2/c2)-1/2 has a somewhat controversial history, based on special relativity theory, mathematics, logic, intuition, experiment, and ontology. Key is the ontological framework, specifically whether the framework does or does not include gravity. This paper examines both cases, with detailed analysis of gravitomagnetism and of relativistic mass in collisions.

Ontology-Based Crime News Semantic Retrieval System

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

Ontology-Driven Analytic Models for Pension Management and Decision Support System

Ontology-Driven Analytic Models for Pension Management are sophisticated approaches that integrate the principles of ontology and analytics to optimize the management and decision-making processes within pension systems. While Ontology-Driven Analytic Models offer significant benefits for pension management, there are also challenges associated with implementing and utilizing the models. Developing a comprehensive and accurate ontology for pension management requires a deep understanding of the domain, including regulatory frameworks, investment strategies, retirement planning, and integration of data from heterogenous sources. Integrating these data into a cohesive ontology can be challenging. This research work leverages on semantic ontology as an approach for structured representation of knowledge about concepts and their relationships, and applies it to analyze and optimize decision support for pension management. The proposed ontology presents a formal and explicit specification of concepts (classes), their attributes, and the relationships between them and provides a shared and standardized understanding of the domain;enabling precise communication and knowledge representation for decision-support. The ontology deploys computational frameworks and analytic models to assess and evaluate data, generate insights, predict future pension fund performance as well as assess risk exposure. The research adopts the Reasoner, SPARQL query and OWL Visualizer executed over Java IDE for modelling the ontology-driven analytics. The approach encapsulated and integrated semantic ontologies with analytical models to enhance the accuracy, contextuality, and comprehensiveness of analyses and decisions within pension systems.

An Ontology-Based Question Answering System for University Admissions Advising

Question-Answer systems are now very popular and crucial to support human in automatically responding frequent questions in manyfields.However,these systems depend on learning methods and training data.Therefore,it is necessary to prepare such a good dataset,but it is not an easy job.An ontol-ogy-based domain knowledge base is able to help to reason semantic information and make effective answers given user questions.This study proposes a novel chatbot model involving ontology to generate efficient responses automatically.A case study of admissions advising at the International University–VNU HCMC is taken into account in the proposed chatbot.A domain ontology is designed and built based on the domain knowledge of university admissions using Protégé.The Web user interface of the proposed chatbot system is developed as a prototype using NetBeans.It includes a search engine reasoning the ontology and generat-ing answers to users’questions.Two experiments are carried out to test how the system reacts to different questions.Thefirst experiment examines questions made from some templates,and the second one examines normal questions taken from frequent questions.Experimental results have shown that the ontology-based chatbot can release meaningful and long answers.The results are analysed to prove the proposed chatbot is usable and promising.

An Ontology Based Multilayer Perceptron for Object Detection

In object detection,spatial knowledge assisted systems are effective.Object detection is a main and challenging issue to analyze object-related information.Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification.But these techniques still face,less computational efficiency and high time consumption.This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time.The proposed method uses a multilayer forfinding the similarity score.A fuzzy membership function is used to validate the score for predicting the burned and non-burned zone.Experimental assessment is performed with different factors such as classification rate,time complexity,error rate,space complexity,and precision by using the forestfire dataset.The results show that this novel technique can help to improve the classification rate and reduce the time and space complexity as well as error rate than the conventional methods.

An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

Tropical cyclones(TC)are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in thefield of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine(SVM)to classify the tropical cyclone tracks into four types of classes namely straight,quasi-straight,curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology(TCTRO)described in this paper is a knowledge base which comprises of classes,objects and data properties that represent the interaction among the TC characteristics.A set of SWRL(Semantic Web Rule Language)rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore,we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset,the proposed ontology based SVM classifier achieves an accuracy of 98.3%with reduced classification error rates.

An Approach towards Goal-Oriented Requirements Ontology: Consistency and Completeness Based Requirements Analysis

The paper presents a new approach to managing software requirement elicitation techniques with a high level of analyses based on domain ontology techniques, where we established a mapping between user scenario, structured requirement, and domain ontology techniques to improve many attributes such as requirement consistency, completeness and eliminating duplicate requirements to reduce risk of overrun time and budgets. One of the main targets of requirement engineering is to develop a requirement document with high quality. So, we proposed a user interface to collect all vital information about the project directly from the regular user and requirement engineering;After that, the proposal will generate an ontology based on semantic relations and rules. Requirements Engineering tries to keep requirements throughout a project’s life cycle consistent necessities clear, and up to date. This prototype allows mapping requirement scenarios into ontology elements for semantically interrupted. The general points of our prototype are to guarantee the identification requirements and improved nature of the Software Requirements Specification (SRS) by solving incomplete and conflicting information in the requirements specification.

Marx’s Ontology of Social Power System for Ecological Justice

Historical materialism provides the ontology basis to understand the contemporary ecological justice problem,which is the perspective for analyzing ecological interests from the nature,structure,and transition of the social power system.The transcendence of Marx’s thoughts on western mainstream environmental justice theory lies that it does not based on the“speculative ontology”of metaphysics,but on the basis of“realistic ontology”of social power system.

A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning

Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.

Ontology of Quantum Gravity

A theory of Quantum Gravity based on Primordial Field Theory is applied to a fundamental particle, the neutron. The result is compared to the current quantum description of the neutron bouncing in a gravitational field. Our quantum gravity theory yields results in agreement with the Q-bounce experimental data, but ontologically different from quantum mechanics. The differences are summarized and imply that this experiment on a fundamental particle has the potential to radically alter the ontology of field theory.

Classification of Big Data Security Based on Ontology Web Language

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

Anatomical Retrofitting:Pi(脾)as Spleen and the Persistence of Ontological Ambivalence

Over the past decade,medical researchers in China have debated whether the Pi(脾)corresponds to the biomedical spleen or pancreas.This debate exemplifies a broader phenomenon of“anatomical retrofitting”,or the anachronistic imposition of contemporary categories onto living historical objects.“Anatomical retrofitting”as a means of rectifying cases of mistranslation further positions the biomedical spleen and pancreas as representing ahistorical,universal truths.This framework gives rise to a conceptual binary:while the biomedical spleen is universalized as what philosophers may describe as“logical”ontology,the Pi connects to a different nature of reality,or“metaphysical”ontology.Far from being an object of imprecision,the Pi was a dynamic vessel that also shared characteristics with the humoral spleen.Given that scholars in China have already subjected Pi to historical scrutiny,this paper urges scholars to do the same with biomedical anatomy.For instance,historically situating the humoral spleen demonstrates that it became less known and less articulated as it transformed into the biomedical spleen.Meanwhile,the pancreas remained an unstable epistemic object that took on the dynamic functions of the humoral spleen in nineteenth-century organotherapy.Through primary source analysis and literature review,this paper contends that the apparent ontological incommensurability between Pi and spleen is neither mutually exclusive nor irreconcilable.Instead,the dynamic nature of internal viscera,their many functions,and their participation in epistemic practices contribute to an ongoing ontological ambivalence that persists despite the forced certainty of anatomical retrofitting.

Ontology研究综述

Ontology是描述概念及概念之间关系的概念模型 ,通过概念之间的关系来描述概念的语义。作为一种有效表现概念层次结构和语义的模型 ,Ontology被广泛地应用到计算机科学的众多领域。本文作者对目前Ontology的研究与应用现状进行了综述性地介绍 ,从Ontology的定义、Ontol ogy理论研究、Ontology在信息系统中的应用以及在语义Web中的地位等方面加以了系统阐述。

基于Ontology的语义检索研究

本文从语义检索和概念空间的内涵入手,分析了现有的两种概念空间形式,重点分析了基于Ontology的概念空间的构建方法、描述语言及其编辑工具Protégé.在此基础上,分析了基于Ontology的语义检索系统的构建步骤及其关键技术.最后利用Protégé实现了简单的语义检索.