基于上下结合法的战场态势信息本体构建

由于战场态势信息的来源复杂、数据结构和表达方式存在较大差异,建立统一的知识表达模型具有一定的挑战。本体作为语义化的概念模型,常被用于描述知识领域中的概念、关系和属性。提出了一种基于上下结合法的战场态势信息领域本体构建方法。采用自顶向下法构建上层本体,通过定义概念、关系和属性,设计上下位结构明确的概念层次结构模型,建立概念类之间的层级关系和语义关联。采用自底向上法构建底层本体,提出了一种基于双重聚合算法的本体抽取方法。TF-IDF获得实体的特征表示,利用K-means++初步抽象归纳出本体,利用凝聚式层次聚类算法进一步细分本体概念,定义本体之间的关系。使用描述语言OWL和编辑工具Protégé管理本体,最终构建了对战场态势信息进行一致性管理的知识本体模型BattleOnto,在指挥员做出决策时提供支持和帮助。

Transcriptomic and bioinformatics analysis of the mechanism by which erythropoietin promotes recovery from traumatic brain injury in mice

Recent studies have found that erythropoietin promotes the recovery of neurological function after traumatic brain injury.However,the precise mechanism of action remains unclea r.In this study,we induced moderate traumatic brain injury in mice by intrape ritoneal injection of erythro poietin for 3 consecutive days.RNA sequencing detected a total of 4065 differentially expressed RNAs,including 1059 mRNAs,92 microRNAs,799 long non-coding RNAs,and 2115circular RNAs.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses revealed that the coding and non-coding RNAs that were differentially expressed after traumatic brain injury and treatment with erythropoietin play roles in the axon guidance pathway,Wnt pathway,and MAPK pathway.Constructing competing endogenous RNA networks showed that regulatory relationship between the differentially expressed non-coding RNAs and mRNAs.Because the axon guidance pathway was repeatedly enriched,the expression of Wnt5a and Ephb6,key factors in the axonal guidance pathway,was assessed.Ephb6 expression decreased and Wnt5a expression increased after traumatic brain injury,and these effects were reversed by treatment with erythro poietin.These findings suggest that erythro poietin can promote recove ry of nerve function after traumatic brain injury through the axon guidance pathway.

RNA sequencing of exosomes secreted by fibroblast and Schwann cells elucidates mechanisms underlying peripheral nerve regeneration

Exosomes exhibit complex biological functions and mediate a variety of biological processes,such as promoting axonal regeneration and functional recove ry after injury.Long non-coding RNAs(IncRNAs)have been reported to play a crucial role in axonal regeneration.Howeve r,the role of the IncRNA-microRNAmessenger RNA(mRNA)-competitive endogenous RNA(ceRNA)network in exosome-mediated axonal regeneration remains unclear.In this study,we performed RNA transcriptome sequencing analysis to assess mRNA expression patterns in exosomes produced by cultured fibroblasts(FC-EXOs)and Schwann cells(SCEXOs).Diffe rential gene expression analysis,Gene Ontology analysis,Kyoto Encyclopedia of Genes and Genomes analysis,and protein-protein intera ction network analysis were used to explo re the functions and related pathways of RNAs isolated from FC-EXOs and SC-EXOs.We found that the ribosome-related central gene Rps5 was enriched in FC-EXOs and SC-EXOs,which suggests that it may promote axonal regeneration.In addition,using the miRWalk and Starbase prediction databases,we constructed a regulatory network of ceRNAs targeting Rps5,including 27 microRNAs and five IncRNAs.The ceRNA regulatory network,which included Ftx and Miat,revealed that exsosome-derived Rps5 inhibits scar formation and promotes axonal regeneration and functional recovery after nerve injury.Our findings suggest that exosomes derived from fibro blast and Schwann cells could be used to treat injuries of peripheral nervous system.

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.

Using ontology and rules to retrieve the semantics of disaster remote sensing data

Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

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.

International Healthcare Terminologies for Morbidity New Era: SNOMED and ICD11

The Information Technology (IT) developments have changed the use of Healthcare terminologies from paper-based mortality statistics with the WHO international classifications of diseases (ICD) to the IT-based morbidity implementations for instance for Casemix-based healthcare funding and managing systems. This higher level of granularity is worldwide spread under the umbrella of several national modifications named ICD10 XM. These developments have met the increased use of the International Clinical Reference Terminology named SNOMED. When the updating of WHO ICD10 to WHO ICD11 was decided a merging was envisaged and a WHO SNOMED CT common work proposed a methodology to create a common formal ontology between the 11th version of the WHO International Classification of Diseases and Health Problems (ICD) and the most used in the world clinical terminology named Systematized Nomenclature of Human and Veterinary Medicine - Clinical Terms (SCT). The present work follows this unachieved work and aims to develop a SNOMED-based formal ontology for ICD11 chapter 1 using the textual definitions of ICD11 codes which is a completely new character of ICD and the ontology tools provided by SCT in the publicly available SNOMED Browser. There are two key results: the lexical alignment is complete and the ontology alignment is incomplete with the validated SNOMED concept model can be completed with not yet validated attributes and values of the SNOMED Compositional Grammar. The work opens a new era for the seamless use of both international terminologies for morbidity for instance for DRG/Casemix and clinical management use. The main limitation is that it is restricted to 1 out of 26 chapters of ICD11.

From In-Out Duality to the Foundation of Social Quantum Mechanics

This paper introduces a framework of in-out duality, merging insights from quantum mechanics with social sciences to illuminate the complex interplay between internal potentialities and external manifestations. It articulates foundational, mathematical axioms (Entanglement, Homogeneity, Emergence, and Measurement) that underpin the dynamics of systems, emphasizing the interconnectedness and emergent behaviors resulting from internal and external interactions. By exploring quantum concepts like coherence, entanglement, and superposition, the paper proposes an interdisciplinary approach termed Quantum Social Mechanics. This approach challenges classical paradigms, advocating for a reevaluation of conventional notions through the lens of quantum principles. The paper argues that understanding the universe’s complexities requires a synthesis of motion states and potential states, suggesting a paradigm shift towards integrating quantum mechanics into the philosophical foundation of social theory. Through this comprehensive framework, the paper aims to foster a deeper understanding of the universe’s interconnected nature and the dynamic processes that govern the emergence of complex systems and behaviors.

The Origin of Quarks in Quantum Gravity

A theory of quantum gravity has recently been developed by the author based on the concept that all forces converge to one at the moment of Creation. This primordial field can only interact with itself, as no other field exists, contrasting with the Standard Model of Particle Physics in which each elementary particle is an excitation in its own quantum field. The primordial field theory of quantum gravity has produced a model of a fermion with a mass gap, ½-integral spin, discrete charge, and magnetic moment. The mass gap is based on an existence theorem that is anchored in Yang-Mills, while Calabi-Yau anchors ½-integral spin, with charge and magnetic moment based on duality. Based on N-windings, this work is here extended to encompass fractional charge, with the result applied to quarks, yielding fermion mass and charge in agreement with experiment and novel size correlations and a unique quantum gravity-based ontological understanding of quarks.

Bioinformatics analysis of ferroptosis in spinal cord injury

Ferroptosis plays a key role in aggravating the progression of spinal cord injury(SCI),but the specific mechanism remains unknown.In this study,we constructed a rat model of T10 SCI using a modified Allen method.We identified 48,44,and 27 ferroptosis genes that were differentially expressed at 1,3,and 7 days after SCI induction.Compared with the sham group and other SCI subgroups,the subgroup at 1 day after SCI showed increased expression of the ferroptosis marker acyl-CoA synthetase long-chain family member 4 and the oxidative stress marker malondialdehyde in the injured spinal cord while glutathione in the injured spinal cord was lower.These findings with our bioinformatics results suggested that 1 day after SCI was the important period of ferroptosis progression.Bioinformatics analysis identified the following top ten hub ferroptosis genes in the subgroup at 1 day after SCI:STAT3,JUN,TLR4,ATF3,HMOX1,MAPK1,MAPK9,PTGS2,VEGFA,and RELA.Real-time polymerase chain reaction on rat spinal cord tissue confirmed that STAT3,JUN,TLR4,ATF3,HMOX1,PTGS2,and RELA mRNA levels were up-regulated and VEGFA,MAPK1 and MAPK9 mRNA levels were down-regulated.Ten potential compounds were predicted using the DSigDB database as potential drugs or molecules targeting ferroptosis to repair SCI.We also constructed a ferroptosis-related mRNA-miRNA-lncRNA network in SCI that included 66 lncRNAs,10 miRNAs,and 12 genes.Our results help further the understanding of the mechanism underlying ferroptosis in SCI.

Charcot-Marie-Tooth-1A and sciatic nerve crush rat models:insights from proteomics

The sensorimotor and histological aspects of peripheral neuropathies were already studied by our team in two rat models:the sciatic nerve crush and the Charcot-Marie-Tooth-1A disease.In this study,we sought to highlight and compare the protein signature of these two pathological situations.Indeed,the identification of protein profiles in diseases can play an important role in the development of pharmacological targets.In fact,Charcot-Marie-Tooth-1A rats develop motor impairments that are more severe in the hind limbs.Therefore,for the first time,protein expression in sciatic nerve of Charcot-Marie-Tooth-1A rats was examined.First,distal sciatic nerves were collected from Charcot-Marie-Tooth-1A and uninjured wild-type rats aged 3 months.After protein extraction,sequential window acquisition of all theoretical fragment ion spectra liquid chromatography and mass spectrometry was employed.445 proteins mapped to Swiss-Prot or trEMBL Uniprot databases were identified and quantified.Of these,153 proteins showed statistically significant differences between Charcot-Marie-Tooth-1A and wild-type groups.The majority of these proteins were overexpressed in Charcot-Marie-Tooth-1A.Hierarchical clustering and functional enrichment using Gene Ontology were used to group these proteins based on their biological effects concerning Charcot-Marie-Tooth-1A pathophysiology.Second,proteomic characterization of wild-type rats subjected to sciatic nerve crush was performed sequential window acquisition of all theoretical fragment ion spectra liquid chromatography and mass spectrometry.One month after injury,distal sciatic nerves were collected and analyzed as described above.Out of 459 identified proteins,92 showed significant differences between sciatic nerve crush and the uninjured wild-type rats used in the first study.The results suggest that young adult Charcot-Marie-Tooth-1A rats(3 months old)develop compensatory mechanisms at the level of redox balance,protein folding,myelination,and axonogenesis.These mechanisms seem insufficient to hurdle the progress of the disease.Notably,response to oxidative stress appears to be a significant feature of Charcot-Marie-Tooth-1A,potentially playing a role in the pathological process.In contrast to the first experiment,the majority of the proteins that differed from wild-type were downregulated in the sciatic nerve crush group.Functional enrichment suggested that neurogenesis,response to axon injury,and oxidative stress were important biological processes.Protein analysis revealed an imperfect repair at this time point after injury and identified several distinguishable proteins.In conclusion,we suggest that peripheral neuropathies,whether of a genetic or traumatic cause,share some common pathological pathways.This study may provide directions for better characterization of these models and/or identifying new specific therapeutic targets.

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.

Ontological Model for Cohesive Smart Health Services Management

Health care has become an essential social-economic concern for all stakeholders(e.g.,patients,doctors,hospitals etc.),health needs,private care and the elderly class of society.The massive increase in the usage of health care Internet of things(IoT)applications has great technological evolvement in human life.There are various smart health care services like remote patient monitoring,diagnostic,disease-specific remote treatments and telemedicine.These applications are available in a split fashion and provide solutions for variant diseases,medical resources and remote service management.The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users.The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services.There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model.The proposed ontological model is derived from different classes,relationships,and constraints to integrate health care services.This model is developed using Protégébased on each interrelated/correlated health care service having different values.Semantic querying SPARQL protocol and RDF query language(SPARQL)were used for knowledge acquisition.The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model.Comparative to other smart health care services integration systems,the proposed ontological model provides more cohesiveness.

Exploring Splicing Variants and Novel Genes in Sacred Lotus Based on RNA-seq Data

Sacred lotus(Nelumbo nucifera)is a typical aquatic plant,belonging to basal eudicot plant,which is ideal for genome and genetic evolutionary study.Understanding lotus gene diversity is important for the study of molecular genetics and breeding.In this research,public RNA-seq data and the annotated reference genome were used to identify the genes in lotus.A total of 26,819 consensus and 1,081 novel genes were identified.Meanwhile,a comprehensive analysis of gene alternative splicing events was conducted,and a total of 19,983“internal”alternative splicing(AS)events and 14,070“complete”AS events were detected in 5,878 and 5,881 multi-exon expression genes,respectively.Observations made from the AS events show the predominance of intron retention(IR)subtype of AS events representing 33%.IR is followed by alternative acceptor(AltA),alternative donor(AltD)and exon skipping(ES),highlighting the universality of the intron definition model in plants.In addition,functional annotations of the gene with AS indicated its relationship to a number of biological processes such as cellular process and metabolic process,showing the key role for alternative splicing in influencing the growth and development of lotus.The results contribute to a better understanding of the current gene diversity in lotus,and provide an abundant resource for future functional genome analysis in lotus.

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.

Reduced non-CpG methylation is a potential epigenetic target after spinal cord injury

DNA methylation is a critical epigenetic regulator in the occurrence and development of diseases and is closely related to various functional responses in relation to spinal cord injury.To investigate the role of DNA methylation in spinal cord injury,we constructed a library with reduced-representation bisulfite sequencing data obtained at various time points(day 0-42)after spinal cord injury in mice.Global DNA methylation levels,specifically non-CpG(CHG and CHH)methylation levels,decreased modestly following spinal cord injury.Stages post-spinal cord injury were classified as early(day 0-3),intermediate(day7-14),and late(day 28-42)based on similarity and hie rarchical cluste ring of global DNA methylation patterns.The non-CpG methylation level,which included CHG and CHH methylation levels,was markedly reduced despite accounting for a minor proportion of total methylation abundance.At multiple genomic sites,including the 5’untranslated regions,promoter,exon,intron,and 3’untranslated regions,the non-CpG methylation level was markedly decreased following spinal cord injury,whereas the CpG methylation level remained unchanged at these locations.Approximately one-half of the differentially methylated regions were located in intergenic areas;the other differentially methylated regions in both CpG and non-CpG regions were cluste red in intron regions,where the DNA methylation level was highest.The function of genes associated with differentially methylated regions in promoter regions was also investigated.From Gene Ontology analysis results,DNA methylation was implicated in a number of essential functional responses to spinal cord injury,including neuronal synaptic connection creation and axon regeneration.Notably,neither CpG methylation nor non-CpG methylation was implicated in the functional response of glial or inflammatory cells.In summary,our work elucidated the dynamic pattern of DNA methylation in the spinal co rd following injury and identified reduced nonCpG methylation as an epigenetic target after spinal cord injury in mice.

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.

Differentially expressed whey proteins of donkey and bovine colostrum revealed with a label-free proteomics approach

This study aimed to analyze and compare the differentially expressed whey proteins(DEWPs)of donkey and bovine colostrum using high-performance liquid chromatography with tandem mass spectrometry-based proteomics.A total of 620 and 696 whey proteins were characterized in the donkey and bovine colostrum,respectively,including 383 common whey proteins.Among these common proteins,80 were identified as DEWPs,including 21 upregulated and 59 downregulated DEWPs in donkey colostrum compared to bovine colostrum.Gene Ontology analysis revealed that these DEWPs were mainly related to cellular components,such as extracellular exosome,plasma membrane,and mitochondrion;biological processes,such as oxidation-reduction process,cell-cell adhesion,and small guanosine triphosphate(GTP)ase-mediated signal transduction;and molecular functions,such as GTP binding,GTPase activity,and soluble N-ethylmaleimide-sensitive factor(NSF)attachment protein receptor activity.Metabolic pathway analysis suggested that the majority of the DEWPs were associated with soluble NSF factor attachment protein receptor interactions in vesicular transport,fatty acid biosynthesis,and estrogen signaling pathways.Our results provide a vital insight into the differences between donkey and bovine colostrum,along with important information on the significant components as nutritional and functional factors to be included in infant formula based on multiple milk sources.

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 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.