Ontology Similarity Research Articles

Prediction of miRNA-disease association based on multisource inductive matrix completion.

MicroRNAs (miRNAs) are endogenous non-coding RNAs approximately 23 nucleotides in length, playing significant roles in various cellular processes. Numerous studies have shown that miRNAs are involved in the regulation of many human diseases. Accurate prediction of miRNA-disease associations is crucial for early diagnosis, treatment, and prognosis assessment of diseases. In this paper, we propose the Autoencoder Inductive Matrix Completion (AEIMC) model to identify potential miRNA-disease associations. The model captures interaction features from multiple similarity networks, including miRNA functional similarity, miRNA sequence similarity, disease semantic similarity, disease ontology similarity, and Gaussian interaction kernel similarity between miRNAs and diseases. Autoencoders are used to extract more complex and abstract data representations, which are then input into the inductive matrix completion model for association prediction. The effectiveness of the model is validated through cross-validation, stratified threshold evaluation, and case studies, while ablation experiments further confirm the necessity of introducing sequence and ontology similarities for the first time.

Exploring the molecular mechanisms and shared potential drugs between rheumatoid arthritis and arthrofibrosis based on large language model and synovial microenvironment analysis

Rheumatoid arthritis (RA) and arthrofibrosis (AF) are both chronic synovial hyperplasia diseases that result in joint stiffness and contractures. They shared similar symptoms and many common features in pathogenesis. Our study aims to perform a comprehensive analysis between RA and AF and identify novel drugs for clinical use. Based on the text mining approaches, we performed a correlation analysis of 12 common joint diseases including arthrofibrosis, gouty arthritis, infectious arthritis, juvenile idiopathic arthritis, osteoarthritis, post infectious arthropathies, post traumatic osteoarthritis, psoriatic arthritis, reactive arthritis, rheumatoid arthritis, septic arthritis, and transient arthritis. 5 bulk sequencing datasets and 4 single-cell sequencing datasets of RA and AF were integrated and analyzed. A novel drug repositioning method was found for drug screening, and text mining approaches were used to verify the identified drugs. RA and AF performed the highest gene similarity (0.77) and functional ontology similarity (0.84) among all 12 joint diseases. We figured out that they share the same key pathogenic cell including CD34 + sublining fibroblasts (CD34-SLF) and DKK3 + sublining fibroblasts (DKK3-SLF). Potential therapeutic target database (PTTD) was established with the differential expressed genes (DEGs) of these key pathogenic cells. Based on the PTTD, 15 potential drugs for AF and 16 potential drugs for RA were identified. This work provides a new perspective on AF and RA study which enhances our understanding of their pathogenesis. It also shed light on their underlying mechanism and open new avenues for drug repositioning studies.

Key protein identification by integrating protein complex information and multi-biological features.

Identifying key proteins based on protein-protein interaction networks has emerged as a prominent area of research in bioinformatics. However, current methods exhibit certain limitations, such as the omission of subcellular localization information and the disregard for the impact of topological structure noise on the reliability of key protein identification. Moreover, the influence of proteins outside a complex but interacting with proteins inside the complex on complex participation tends to be overlooked. Addressing these shortcomings, this paper presents a novel method for key protein identification that integrates protein complex information with multiple biological features. This approach offers a comprehensive evaluation of protein importance by considering subcellular localization centrality, topological centrality weighted by gene ontology (GO) similarity and complex participation centrality. Experimental results, including traditional statistical metrics, jackknife methodology metric and key protein overlap or difference, demonstrate that the proposed method not only achieves higher accuracy in identifying key proteins compared to nine classical methods but also exhibits robustness across diverse protein-protein interaction networks.

Application of Rough Concept Lattice Model in Construction of Ontology and Semantic Annotation in Semantic Web of Things

In order to solve the problem of interoperability in Internet of Things, the Semantic Web technology is introduced into the Internet of Things to form Semantic Web of Things. Ontology construction is the core of Semantic Web of Things. Firstly, this paper analyzes the shortcomings of ontology construction methods in the Semantic Web of Things. Then, this paper proposes construction of semantic ontology based on improved rough concept lattice, which provides theoretical basis for semantic annotation of the sensing data attributes. In addition, this paper describes the semantic annotation system for the Internet of Things based on semantic similarity of ontology. The system consists of three steps: ontology mapping integration module, information extraction module, and semantic annotation of sensing data. Finally, the experimental results show that this semantic annotation method effectively improves the flexibility of sensor information and data attributes and effectively enhances the expression ability of sensor information and the use value of data.

Glucocorticoids Promote Extracellular Matrix Component Remodeling by Activating YAP in Human Retinal Capillary Endothelial Cells.

Remodeling of extracellular matrix (ECM) components of endothelial cells is the main cause of retinal vascular basement membrane (BM) thickening, which leads to the initiation and perpetuation of microvasculopathy of diabetic retinopathy (DR). Excessive amounts of glucocorticoids (GCs) are related to the presence and severity of DR, however transcriptional effects of GCs on the biology of human retinal capillary endothelial cells (HRCECs) and its impacts on DR are still unclear. Here, we showed that GC (hydrocortisone) treatment induced ECM component [fibronectin (FN) and type IV collagen (Col IV)] expression and morphological changes in HRCECs via the glucocorticoid receptor (GR), which depended on the nuclear translocation of YAP coactivator. Mechanistically, GCs induced stress fiber formation in HRCECs, while blocking stress fiber formation inhibited GC-induced YAP nuclear translocation. Overexpression of FN, but not Col IV, activated YAP through the promotion of stress fiber formation via ECM-integrin signaling. Thus, a feedforward loop is established to sustain YAP activity. Using mRNA sequencing of HRCECs with overexpressed YAP or GC treatment, we found a similarity in Gene Ontology (GO) terms, differentially expressed genes (DEGs) and transcription factors (TFs) between the two RNA-seq datasets. In vivo, YAP was activated in retina vascular ECs of STZ-induced diabetic mice, and TF prediction analysis of published RNA-seq data of dermal vascular ECs from T2DM patients showed that GR and TEAD (the main transcription factor for YAP) were enriched. Together, GCs activate YAP and promote ECM component (FN and Col IV) remodeling in retinal capillary endothelial cells, and the underlying regulatory mechanism may provide new insights into the vascular BM thickening of the retina in the early pathogenesis of DR.

TIANXIA (ALL-UNDER-HEAVEN): AN ALTERNATIVE SYSTEM OR A ROSE BY ANOTHER NAME?

Tianxia is considered as an alternative institutionalization to govern the international system. It refers to world governance that is regulated by a world institution. Accordingly, a world institution plays the harmonizer role under this system. States, on the other hand, choose their economic models and the leader organizes the relations among different units. This paper thus argues that Tianxia is an alternative framework to Western-oriented IR theories. In that sense, this article aims to explore the similarities between the philosophical idea of Tianxia and Western-oriented IR Theory. More specifically, the article explores the issue from the international system perspective. The epistemological gaps and ontological similarities between the two frameworks will be demonstrated.

Bim Regulates the Survival and Suppressive Capability of CD8+ Foxp3+ Regulatory T Cells during Graft Versus Host Disease

Graft versus host disease (GVHD) is a proinflammatory syndrome that is the major complication of allogeneic hematopoietic stem cell transplantation and can lead to significant pathological damage. The failure to reconstitute an effective regulatory T cell compartment is a defining characteristic of this disease and has been the genesis for strategies designed to augment reconstitution of regulatory T cells (Tregs) by either in vivo expansion or adoptive transfer of this population. While CD4+ Foxp3+ T cells have been the most carefully examined Treg population, CD8+ Foxp3+ Tregs also emerge early during GVHD, have suppressive function, and can attenuate disease severity. The precise role of these cells and the extent to which CD8+ Tregs possess functional and ontological similarities to CD4+ Tregs, however, have not been well delineated. Using established murine models of GVHD, we observed that adoptively transferred alloantigen-specific CD8+ Tregs had reduced in vivo survival in all target organs and were significantly less potent than CD4+ Tregs for the prevention of lethal GVHD. To define the mechanistic basis for their reduced efficacy, we first examined the T cell receptor (TCR) repertoire of in vivo-derived CD4+ and CD8+ Tregs to determine whether a restricted repertoire was a potential explanation for the diminished ability of CD8+ Tregs to prevent GVHD. We employed GVHD models in which both polyclonal CD8+ [B6→Balb/c] and alloantigen-specific [B6→Balb.B] CD8+ H60+ Treg populations could be assessed and used the Shannon entropy index to define TCR repertoire diversity which is critical for the maintenance of self-tolerance by Tregs. Amplicon sequencing of TCR-β rearrangements to analyze the TCR repertoires of in vivo-induced CD8+ and CD4+ Tregs, along with their conventional CD8+ and CD4+ T cell counterparts, revealed that polyclonal CD8+ and alloantigen-specific CD8+ H60+ Tregs had repertoire diversity that was similar to that of conventional CD8+ T cells, indicating that a more restricted repertoire was not the proximate cause of decreased suppressive capability. Subsequent transcriptional profiling and principal component analysis revealed that CD8+ Tregs arising during GVHD possessed a canonical Treg transcriptional signature that was similar to that observed in CD4+ Tregs, yet distinct from conventional CD8+ T cells. Gene set enrichment and Ingenuity pathway analyses, however, both demonstrated that CD8+ Tregs had differential gene expression in pathways involved in cell death and survival when compared to CD4+ Tregs. This was further confirmed by gene expression analysis and protein expression studies which revealed that CD8+ Tregs had increased expression of the pro-apoptotic proteins Bim, cleaved caspase 3, and cleaved caspase 7, as well as a reduction in the anti-apoptotic protein, Mcl-1. To determine whether Bim was functionally important for CD8+ Treg survival, we comparatively examined in vivo-derived CD4+ and CD8+ Treg induction using wild type Foxp3EGFP and Bim-/-Foxp3EGFP animals as donors in transplantation studies. The absence of Bim in the CD8+ Treg compartment resulted in a significant increase in absolute CD8+ Treg numbers in the liver, lung, and colon when examined on days 7, 14, and 21 post transplantation. Given that the absence of Bim augmented in vivo-generated CD8+ Treg survival, we then examined whether Bim also regulated the survival of adoptively transferred, alloantigen-specific CD8+ Tregs which we had observed were inferior to CD4+ Tregs for the suppression of GVHD. We noted an increased percentage and absolute number of adoptively transferred CD8+ Foxp3+ Bim-/- Tregs relative to wild type CD8+ Tregs in all tissue sites, indicating that the absence of Bim resulted in enhanced stabilization of Foxp3 expression and augmented in vivo survival. Finally, animals that were transplanted with in vitro-derived, alloantigen-specific CD8+ Tregs from Bim-/- Foxp3EGFP mice had significantly prolonged survival relative to mice reconstituted with wild type CD8+ Foxp3EGFP Tregs, demonstrating that the prolonged in vivo survival conferred by the absence of Bim resulted in an increased ability to prevent GVHD. Collectively, these studies demonstrate that Bim plays a critical role in the regulation of CD8+ Treg survival which may have clinical implications for the use of regulatory T cell therapy in the treatment and prevention of GVHD. DisclosuresVincent:Merck: Research Funding; Pharmacyclics: Research Funding; Nanostring: Membership on an entity's Board of Directors or advisory committees. Serody:Merck: Research Funding.

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets.

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.

A Partition Based Framework for Large Scale Ontology Matching

:Large amount of data coming from different sources and formats is available on the web which generates heterogeneity problem. Semantic web technologies play an important role for collecting, merging, matching and aggregating big data from heterogeneous resources by determining the semantic correspondence between the entities. However, achieving good efficiency is major challenge for large scale ontology matching task.Objective:We propose a PBOM framework for coping with the large scale ontology matching problem.Methods:Our proposal first selects the source ontology and calculates the similarity of concepts within source ontology by using Lin measure. We use clustering algorithm for partition of the source ontology, obtained clusters of source ontology then used to divide the target ontology. During matching process, we run matchers from the pool of the matchers over each clusters. We aggregate the result of element level matchers and structure level matchers according to weighted sum aggregation. Each cluster is executed in its processor in parallel with other clusters.Results:We have presented step wise execution of proposed approach over one cluster of source and target ontology. The evaluation of our framework is performed by OAEI datasets of bibliographic benchmark 2014, biomedical track 2015 and anatomy 2016.Conclusion:Results show that, the performance of our approach is better in term of F-measure. The combination of clustering algorithm and parallel processing reduces the memory space and time complexity of matching process.

On Approximation of Concept Similarity Measure in Description Logic ELH With Pre-Trained Word Embedding

Data-driven and knowledge-driven methods are two mainstream techniques in the pursuit of developing artificial intelligence systems. While data-driven methods seek to develop a decision model from observations in the real world, they are difficult to provide an explanation for the results in human terms. On the other hand, knowledge-driven methods that employ symbolic reasoning based on formal semantics of a knowledge-base are thus more interpretable and explainable, while lacking an ability to deal with incomplete modeling of the structured knowledge-bases. This work aims to tackle these issues on ontology similarity by proposing a general framework that combines the strengths of both approaches for measuring semantic similarity of concepts in a description logic (DL) ontology. More specifically, a neuro-symbolic integrated framework is defined to exploit the pre-trained word embeddings with semantic definitions in an ontology to yield an explainable degree of concept similarity. To demonstrate its applicability, we develop a concrete similarity measure $ {\textsf {sim}}_\epsilon $ conforming to the proposed framework and also introduce an efficient algorithm that can extract an explanation for why such a degree is indicated. The correctness is shown by analyzing theoretical properties that it guarantees to preserve and also by performing an empirical evaluation with a medical ontology SNOMED CT and a medical pre-trained embedding BioWordVec. The results show that our proposed method remains both interpretability and explainability while achieving comparable performance, relative to the state-of-the-art approaches in the data and knowledge-driven methods.

Are hotel managers taught to be aggressive in intelligence gathering?

The study examines the ontological similarity between the concept of competitor orientation and questionable intelligence-gathering efforts. Respondents from the hotel industry were surveyed with self-administered questionnaires. Exploratory factor analysis was carried out to identify the structure underlying variables of market orientation and ethical judgment on questionable intelligence-gathering efforts. The results suggest that the surveyed hotel managers are unable to distinguish the legitimate tactics of competitor orientation from the questionable practice of industrial espionage.

Similarity measure of agents’ ontologies in cohesive hybrid intelligent multi-agent system

The work is devoted to the issues of building cohesive hybrid intelligent multi-agent systems that implement the following mechanisms for ensuring the joint work of heterogeneous agents: coordination of goals and agent ontologies, development of the problem solving protocol. These mechanisms are similar to the processes that occur in long-standing expert teams. In them, experts, interacting in the course of solving problems, exchange views, knowledge, take into account each other’s goals, establish formal and informal norms of communication. In the paper, in particular, a method for estimating the similarity of ontologies is considered, which is the part of the method for estimating the cohesion of agents of the hybrid intelligent multi-agent system. The estimation of cohesion is used by system’s agents as the interaction effectiveness criterion when making decisions about the need to converge goals, ontologies or correction of the interaction protocol.

SVM-Based Multi-Dividing Ontology Learning Algorithm and Similarity Measuring on Topological Indices

Ontology is one of the oldest terminologies in physics and is used to describe the origin and most essential attributes of all things in the world. With the development of contemporary science, ontology was given a specific definition and then introduced into the computer science as a conceptual model to describe the relationship between objects. In the past decade, the algorithms and applications in the ontology-related field have attracted the attention of many scholars. Most of the computational formulas in ontology algorithms are out of heuristic design ideas. For example, researchers use the ontology's own structural characteristics to design a calculation formula for a specific ontology from four different perspectives: name, instance, structure and attribute. In this paper, we first focus on how to apply these valuable heuristic elements to ontology learning and prediction. We combine these four heuristic elements with deep learning network and back propagation methods to obtain new ontology algorithm for prediction applications. Second, a support vector machines based multi-dividing ontology learning algorithm is proposed. Finally, we pay attention to the similarity of topological indices in chemical graph theory, and apply SVM-based multi-dividing ontology learning algorithms to give some calculation results of similarity between topological indices.

RBUD: A New Functional Potential Analysis Approach for Whole Microbial Genome Shotgun Sequencing.

Whole metagenome shotgun sequencing is a powerful approach to detect the functional potential of microbial communities. Currently, the read-based metagenomics profiling for established database (RBED) method is one of the two kinds of conventional methods for species and functional annotations. However, the databases, which are established based on test samples or specific reference genomes or protein sequences, limit the coverage of global microbial diversity. The other assembly-based metagenomics profiling for unestablished database (ABUD) method has a low utilization rate of reads, resulting in a lot of biological information loss. In this study, we proposed a new method, read-based metagenomics profiling for unestablished database (RBUD), based on Metagenome Database of Global Microorganisms (MDGM), to solve the above problems. To evaluate the accuracy and effectiveness of our method, the intestinal bacterial composition and function analyses were performed in both avian colibacillosis chicken cases and type 2 diabetes mellitus patients. Comparing to the existing methods, RBUD is superior in detecting proteins, percentage of reads mapping and ontological similarity of intestinal microbes. The results of RBUD are in better agreement with the classical functional studies on these two diseases. RBUD also has the advantages of fast analysis speed and is not limited by the sample size.

A Novel Graph Topology-Based GO-Similarity Measure for Signature Detection From Multi-Omics Data and its Application to Other Problems.

Large scale multi-omics data analysis and signature prediction have been a topic of interest in the last two decades. While various traditional clustering/correlation-based methods have been proposed, but the overall prediction is not always satisfactory. To solve these challenges, in this article, we propose a new approach by leveraging the Gene Ontology (GO)similarity combined with multiomics data. In this article, a new GO similarity measure, ModSchlicker, is proposed and the effectiveness of the proposed measure along with other standardized measures are reviewed while using various graph topology-based Information Content (IC)values of GO-term. The proposed measure is deployed to PPI prediction. Furthermore, by involving GO similarity, we propose a new framework for stronger disease-based gene signature detection from the multi-omics data. For the first objective, we predict interaction from various benchmark PPI datasets of Yeast and Human species. For the latter, the gene expression and methylation profiles are used to identify Differentially Expressed and Methylated (DEM)genes. Thereafter, the GO similarity score along with a statistical method are used to determine the potential gene signature. Interestingly, the proposed method produces a better performance ( 0.9 avg. accuracy and 0.95 AUC)as compared to the other existing related methods during the classification of the participating features (genes)of the signature. Moreover, the proposed method is highly useful in other prediction/classification problems for any kind of large scale omics data.

Protein2vec: Predicting Protein-Protein Interactions Based on LSTM.

The semantic similarity of gene ontology (GO) terms is widely used to predict protein-protein interactions (PPIs). The traditional semantic similarity measures are based mainly on manually crafted features, which may ignore some important hidden information of the gene ontology. Moreover, those methods usually obtain the similarity between proteins from similarity between GO terms by some simple statistical rules, such as MAX and BMA (best-match average), oversimplifying the possible complex relationship between the proteins and the GO terms annotated with them. To overcome the two deficiencies, we propose a new method named protein2vec, which characterizes a protein with a vector based on the GO terms annotated to it and combines the information of both the GO and known PPIs. We firstly try to apply the network embedding algorithm on the GO network to generate feature vectors for each GO term. Then, Long Short-Time Memory (LSTM) encodes the feature vectors of the GO terms annotated with a protein into another vector (called protein vector). Finally, two protein vectors are forwarded into a feedforward neural network to predict the interaction between the two corresponding proteins. The experimental results show that protein2vec outperforms almost all commonly used traditional semantic similarity methods.

Mapping Crisp Structural Semantic Similarity Measures to Fuzzy Context: A Generic Approach

Ontology-based similarity measures have received much importance in recent years. In many real-world cases, the domain considered in the ontological similarity assessment consists of uncertainty or incomplete information. Such vagueness has led to the successful implementation of fuzzy ontology (FO)-based similarity measures. Despite various applications of FO-based similarity measures, limited methods have so far been proposed for this purpose. Accordingly, this paper presents a generic model for semantic similarity assessment based on a fuzzy ontology. The proposed approach relies on the broad literature of Crisp Ontology-based Structural Semantic Similarity Measures (CO-SSSM). It provides an approach for mapping CO-SSSMs to fuzzy context. Consequently, the proposed generic model can be applied to various CO-SSSMs to develop their corresponding FO-SSSMs. In this regard, as an empirical investigation, four of the common CO-SSSMs were selected, their equivalent FO-SSSMs were developed by means of the proposed approach, and the accuracy of their similarity assessment was compared with each other. The results show the power of FO-SSSMs in describing the relations between concepts and their superiority over CO-SSSMs.

ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. ViSEAGO is publicly available on https://bioconductor.org/packages/ViSEAGO .

Handling Big Data Scalability in Biological Domain Using Parallel and Distributed Processing: A Case of Three Biological Semantic Similarity Measures.

In the field of biology, researchers need to compare genes or gene products using semantic similarity measures (SSM). Continuous data growth and diversity in data characteristics comprise what is called big data; current biological SSMs cannot handle big data. Therefore, these measures need the ability to control the size of big data. We used parallel and distributed processing by splitting data into multiple partitions and applied SSM measures to each partition; this approach helped manage big data scalability and computational problems. Our solution involves three steps: split gene ontology (GO), data clustering, and semantic similarity calculation. To test this method, split GO and data clustering algorithms were defined and assessed for performance in the first two steps. Three of the best SSMs in biology [Resnik, Shortest Semantic Differentiation Distance (SSDD), and SORA] are enhanced by introducing threaded parallel processing, which is used in the third step. Our results demonstrate that introducing threads in SSMs reduced the time of calculating semantic similarity between gene pairs and improved performance of the three SSMs. Average time was reduced by 24.51% for Resnik, 22.93%, for SSDD, and 33.68% for SORA. Total time was reduced by 8.88% for Resnik, 23.14% for SSDD, and 39.27% for SORA. Using these threaded measures in the distributed system, combined with using split GO and data clustering algorithms to split input data based on their similarity, reduced the average time more than did the approach of equally dividing input data. Time reduction increased with increasing number of splits. Time reduction percentage was 24.1%, 39.2%, and 66.6% for Threaded SSDD; 33.0%, 78.2%, and 93.1% for Threaded SORA in the case of 2, 3, and 4 slaves, respectively; and 92.04% for Threaded Resnik in the case of four slaves.

Ontology-Based Personalized Course Recommendation Framework

Choosing a higher education course at university is not an easy task for students. A wide range of courses are offered by the individual universities whose delivery mode and entry requirements differ. A personalized recommendation system can be an effective way of suggesting the relevant courses to the prospective students. This paper introduces a novel approach that personalizes course recommendations that will match the individual needs of users. The proposed approach developed a framework of an ontology-based hybrid-filtering system called the ontology-based personalized course recommendation (OPCR). This approach aims to integrate the information from multiple sources based on the hierarchical ontology similarity with a view to enhancing the efficiency and the user satisfaction and to provide students with appropriate recommendations. The OPCR combines collaborative-based filtering with content-based filtering. It also considers familiar related concepts that are evident in the profiles of both the student and the course, determining the similarity between them. Furthermore, OPCR uses an ontology mapping technique, recommending jobs that will be available following the completion of each course. This method can enable students to gain a comprehensive knowledge of courses based on their relevance, using dynamic ontology mapping to link the course profiles and student profiles with job profiles. Results show that a filtering algorithm that uses hierarchically related concepts produces better outcomes compared to a filtering method that considers only keyword similarity. In addition, the quality of the recommendations is improved when the ontology similarity between the items’ and the users’ profiles were utilized. This approach, using a dynamic ontology mapping, is flexible and can be adapted to different domains. The proposed framework can be used to filter the items for both postgraduate courses and items from other domains.