Ontology-based Semantic Similarity Research Articles

An Improved Fusion-Based Semantic Similarity Measure for Effective Collaborative Filtering Recommendations

Semantic-enhanced recommendation systems are promising approaches to overcome the sparsity and cold-start problems, which are hard to handle using the conventional collaborative filtering (CF) approaches. Further research is needed to effectively integrate ontologies into collaborative filtering recommender systems. This paper proposes an ontology-based semantic similarity measure to evaluate similarities between items and eventually generate accurate recommendations. The proposed semantic similarity measure termed fusion-based semantic similarity takes into account the semantics of ontological instances (i.e. items) inferred from a specific domain ontology, which is determined by analyzing the hierarchical relationships among the instances, as well as the features of the instances and their relationships to other instances. The new measure comprehensively captures the semantic knowledge associated with instances by exploiting all possible shared semantics between instances in a given domain ontology. Furthermore, this paper proposes a new semantic-enhanced hybrid recommendation approach as a result of combining the new semantic similarity measure with the standard item-based CF to enhance the quality of generated recommendations. In order to assess the effectiveness of our semantic-enhanced hybrid collaborative filtering method, a series of experiments were conducted to compare the performance of the proposed approach against well-established benchmark techniques. The reported experimental results consistently emphasize its superiority, demonstrating enhanced predictive abilities and a notable improvement in the quality of recommendations. More specifically, the proposed approach achieved notable 6% reduction in Mean Absolute Error (MAE) in certain cases, outperforming other benchmark techniques. Additionally, this study highlights the potential of using semantic-based similarity to enhance the performance of recommendation systems. Such enhancements address challenges within collaborative filtering, potentially leading to advancements in recommendation system design and optimization.

Comprehensive Calculation Method of Semantic Similarity of Transport Infrastructure Ontology Concept Based on SHO-BP Algorithm

Semantic information interaction plays an important role in transportation infrastructure modeling and management. To ensure semantic consistency during information exchange and data integration, ontology technology is commonly employed to measure the semantic relevance between concepts. Ontology semantic similarity accurately expresses relationships among various concepts in the domain, and when combined with Building Information Modeling (BIM) technology, it improves the efficiency of information transmission and management in construction. However, the complex structure, diverse components, and strong attribute diversity of transportation infrastructure pose challenges for analysis and computation, leading to limited precision in existing ontology semantic similarity methods. Aimed at these issues, this paper proposes a transport infrastructure ontology concept semantic similarity measurement model based on the Back Propagation (BP) neural network algorithm improved by the Spotted Hyena Optimizer (SHO-BP). Firstly, a semantic network for transportation infrastructure is established, and an ontology-based semantic similarity calculation model is constructed with three approaches, including Edge-Counting method, Feature-based method, and Information-Content method. Then, the SHO-BP algorithm is employed to comprehensively weight the three similarity measure approaches above. Finally, using bridge BIM models as examples, the semantic similarity of transportation infrastructure concepts involved in the BIM models are computed based on the weighted model derived from the aforementioned processes. The experiments demonstrate that the SHO-BP algorithm achieves a higher Pearson correlation coefficient than other algorithms for the comprehensive semantic similarity results in the field of transportation infrastructure. This improvement effectively enhances the accuracy of ontology semantic similarity calculation, and it is conducive to the sharing and integration of BIM information in different systems.

Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins.

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

Cardiovascular diseases display etiological and seasonal trend in human population: Evidence from seasonal cardiovascular comorbid diseases (SCCD) index.

Seasonal changes in the human cardiovascular system are known to play an important role in the onset of many diseases. Confounding variables include behavioral and environmental factors; failing to address such variables makes measuring the true temporal impact of these diseases difficult. On the other hand, numerous clinical studies imply that only specific groups of people are more seasonal sensitive and that their maladaptation might contribute to various illnesses. As a result, it is critical to evaluate the etiological and seasonal sensitive patterns of cardiovascular diseases (CVD), which impact the majority of the human population. The hypothesis for this study formulated that cardiovascular and associated illnesses had substantial connections with seasonal and etiological variations. Thus in the present study, 4519 systematic screen-eligible studies were analyzed using data mining to uncover 852 disease association relationships between cardiovascular and associated disorders. A disease ontology-based semantic similarity network (DSN) analysis was performed to narrow down the identified CVDs. Further, topological analysis was used to predict the seven CVDs, including myocardial infarction (MI), in three clusters. Following that, Mann-Kendall and Cox-Stuart analyses were used to investigate the seasonal sensitivity and temporal relationship of these seven CVDs. Finally, temporal relationships were confirmed using LOESS and TBATS, as well as seasonal breakdown utilizing autocorrelation and fast Fourier transform results. The study provides indirect evidence of a severe etiological association among the three cardiovascular diseases, including MI, atrial fibrillation, and atherosclerosis, which are winter season sensitive in most of the world population. Hypertension has two seasonal falls and peaks due to its seasonal nature, that is, summer and winter hypertension. While, heart failure was also identified, with minor temporal trends. Hence, all five diseases could be classified as seasonal cardiovascular comorbid diseases (SCCD). Furthermore, these diseases could be studied for potential common risk factors such as biochemical, genetic, and physiological factors.

An Efficient Parallelized Ontology Network-Based Semantic Similarity Measure for Big Biomedical Document Clustering.

Semantic mining is always a challenge for big biomedical text data. Ontology has been widely proved and used to extract semantic information. However, the process of ontology-based semantic similarity calculation is so complex that it cannot measure the similarity for big text data. To solve this problem, we propose a parallelized semantic similarity measurement method based on Hadoop MapReduce for big text data. At first, we preprocess and extract the semantic features from documents. Then, we calculate the document semantic similarity based on ontology network structure under MapReduce framework. Finally, based on the generated semantic document similarity, document clusters are generated via clustering algorithms. To validate the effectiveness, we use two kinds of open datasets. The experimental results show that the traditional methods can hardly work for more than ten thousand biomedical documents. The proposed method keeps efficient and accurate for big dataset and is of high parallelism and scalability.

A Semantic Similarity Model to Compare Heterogeneous Data Sources to Augment Engineering Data with New Failure modes in Automotive Industry

In real life industry, in-time exposure of symptoms and their failure modes observed during fault events provide key signals to system engineers to take necessary corrective actions for effectively arresting product defects. A real-life ontology-based semantic similarity system is employed for automatic comparison of engineering data (comes in the form of design failure mode effect analysis, DFMEA data) with the field repair data collected during product warranty period. Given the complexity of engineering data and the overwhelming volume (hundreds of millions of data points) of field repair data makes identification of new symptoms and failure modes from the first principles an impractical task. Typically, the engineering data is recorded by using technical vocabulary, e.g. unstable electric contact, Seat Belt comfort per MVSS208, whereas the field repair data is highly unstructured in nature. Consequently, we observe following types of noises in the field repair data – abbreviated text entries, inconsistent use of vocabulary (‘seat buckle is damaged’ vs ‘buckle unlatching’), and finally the incomplete text entries. More importantly, limited mental mapping capacity of a human agent limits discovery of new symptoms and failure modes from the industrial scale data. Not surprisingly, the text mining and semantic similarity are gaining a serious attention due to their ability to automatically discover the knowledge assets buried in unstructured text by training machines to compare and link high volume of data. In our approach, initially the key constructs (e.g. symptoms, failure modes) from the data are annotated by using the domain ontology. These constructs are then used to construct pairs of terms and pairs of tuples, which are used to compute pair-to-pair and tuple-to-tuple semantic similarity respectively. Finally, the text-to-text semantic similarity is calculated by combining other two semantic similarity scores. It is used to determine whether new symptoms or failure modes from the field repair data can be used to augment the DFMEA data. The proposed method is implemented as a prototype tool and its performance is validated by using real-life data from automobile domain. On an average, our system has F1 score of 0.75 and 0.78 in discovering and identifying new symptoms and synonym symptoms respectively, whereas it achieved the F1 score of 0.72 and 0.68 in discovering new failure modes and in identifying synonym failure modes respectively. The fault detection rate is improved by 35%, whereas the fault isolation rate is improved by 40.5%.

Ontology-based semantic similarity to metadata analysis in the information security domain

It is becoming clear with growing complication of cybersecurity threats, that one of the most important resources to combat cyberattacks is the processing of large amounts of data in the cyber environment. In order to process a huge amount of data and to make decisions, there is a need to automate the tasks of searching, selecting and interpreting Big Data to solve operational information security problems. Big data analytics is complemented by semantic technology, can improve cybersecurity, and allows you to process and interpret large amounts of information in the cyber environment. Using of semantic modeling methods in Big Data analytics is necessary for the selection and combination of heterogeneous Big Data sources, recognition of the patterns of network attacks and other cyber threats, which must occur quickly to implement countermeasures. Therefore to analyze Big Data metadata, the authors propose pre-processing of metadata at the semantic level. As analysis tools, it is proposed to create a thesaurus of the problem based on the domain ontology, which should provide a terminological basis for the integration of ontologies of different levels. To build a thesaurus of the problem, it is proposed to use the standards of open information resources, dictionaries, encyclopedias. The development of an ontology hierarchy formalizes the relationships between data elements that will be used in future for machine learning and artificial intelligence algorithms to adapt to changes in the environment, which in turn will increase the efficiency of big data analytics for the cybersecurity domain.

A large reproducible benchmark of ontology-based methods and word embeddings for word similarity

This work is a companion reproducibility paper of the experiments and results reported in Lastra-Diaz et al. (2019a), which is based on the evaluation of a companion reproducibility dataset with the HESML V1R4 library and the long-term reproducibility tool called Reprozip. Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorization, memory, decision-making and reasoning. For this reason, the research on methods for the estimation of the degree of similarity and relatedness between words and concepts has received a lot of attention in the fields of artificial intelligence and cognitive sciences. However, despite the huge research effort done, there is a lack of a self-contained, reproducible and extensible collection of benchmarks which being amenable to become a de facto standard for large scale experimentation in this line of research. In order to bridge this reproducibility gap, this work introduces a set of reproducible experiments on word similarity and relatedness by providing a detailed reproducibility protocol together with a set of software tools and a self-contained reproducibility dataset, which allow that all experiments and results in our aforementioned work to be reproduced exactly. Our aforementioned primary work introduces the largest, most detailed and reproducible experimental survey on word similarity and relatedness reported in the literature, which is based on the implementation of all evaluated methods into the same software platform. Our reproducible experiments evaluate most of methods in the families of ontology-based semantic similarity measures and word embedding models. We also detail how to extend our experiments to evaluate other unconsidered experimental setups. Finally, we provide a corrigendum for a mismatch in the MC28 similarity scores used in our original experiments.

UFO: A tool for unifying biomedical ontology-based semantic similarity calculation, enrichment analysis and visualization.

BackgroundBiomedical ontologies have been growing quickly and proven to be useful in many biomedical applications. Important applications of those data include estimating the functional similarity between ontology terms and between annotated biomedical entities, analyzing enrichment for a set of biomedical entities. Many semantic similarity calculation and enrichment analysis methods have been proposed for such applications. Also, a number of tools implementing the methods have been developed on different platforms. However, these tools have implemented a small number of the semantic similarity calculation and enrichment analysis methods for a certain type of biomedical ontology. Note that the methods can be applied to all types of biomedical ontologies. More importantly, each method can be dominant in different applications; thus, users have more choice with more number of methods implemented in tools. Also, more functions would facilitate their task with ontology.ResultsIn this study, we developed a Cytoscape app, named UFO, which unifies most of the semantic similarity measures for between-term and between-entity similarity calculation for all types of biomedical ontologies in OBO format. Based on the similarity calculation, UFO can calculate the similarity between two sets of entities and weigh imported entity networks as well as generate functional similarity networks. Besides, it can perform enrichment analysis of a set of entities by different methods. Moreover, UFO can visualize structural relationships between ontology terms, annotating relationships between entities and terms, and functional similarity between entities. Finally, we demonstrated the ability of UFO through some case studies on finding the best semantic similarity measures for assessing the similarity between human disease phenotypes, constructing biomedical entity functional similarity networks for predicting disease-associated biomarkers, and performing enrichment analysis on a set of similar phenotypes.ConclusionsTaken together, UFO is expected to be a tool where biomedical ontologies can be exploited for various biomedical applications.AvailabilityUFO is distributed as a Cytoscape app, and can be downloaded freely at Cytoscape App (http://apps.cytoscape.org/apps/ufo) for non-commercial use

SemImput: Bridging Semantic Imputation with Deep Learning for Complex Human Activity Recognition.

The recognition of activities of daily living (ADL) in smart environments is a well-known and an important research area, which presents the real-time state of humans in pervasive computing. The process of recognizing human activities generally involves deploying a set of obtrusive and unobtrusive sensors, pre-processing the raw data, and building classification models using machine learning (ML) algorithms. Integrating data from multiple sensors is a challenging task due to dynamic nature of data sources. This is further complicated due to semantic and syntactic differences in these data sources. These differences become even more complex if the data generated is imperfect, which ultimately has a direct impact on its usefulness in yielding an accurate classifier. In this study, we propose a semantic imputation framework to improve the quality of sensor data using ontology-based semantic similarity learning. This is achieved by identifying semantic correlations among sensor events through SPARQL queries, and by performing a time-series longitudinal imputation. Furthermore, we applied deep learning (DL) based artificial neural network (ANN) on public datasets to demonstrate the applicability and validity of the proposed approach. The results showed a higher accuracy with semantically imputed datasets using ANN. We also presented a detailed comparative analysis, comparing the results with the state-of-the-art from the literature. We found that our semantic imputed datasets improved the classification accuracy with 95.78% as a higher one thus proving the effectiveness and robustness of learned models.

Ontology-based Extractive Text Summarization: The Contribution of Instances

In this paper, we present a text summarization approach focusing on multi-document, extractive and query-focused summarization that relies on an ontology-based semantic similarity measure, that specifically explores ontology instances. We employ the DBpedia Ontology and a theoretical definition of similarity to determine query-sentence and sentence-sentence similarity. Furthermore, we define an instance-linking strategy that builds the most accurate sentence representation possible while achieving a better coverage of sentences that can be represented by ontology instances. Using primarily this instances linking strategy, the semantic similarity measure and the Maximal Marginal Relevance Algorithm- MMR - we propose a summarization model that is capable of avoiding redundancy from a more fine-grained representation of sentences, due to the irrepresentation as ontology instances. We demonstrate that our summarizer is capable of achieving compelling results when compared with relevant DUC systems and recently published related studies using ROUGE metrics. Moreover, our experiments lead us to a better understanding of how ontology instances can be used to represent sentences and what is the role of said instances in this process.

A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art

Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorisation, memory, decision-making and reasoning. For this reason, the proposal of methods for the estimation of the degree of similarity and relatedness between words and concepts has been a very active line of research in the fields of artificial intelligence, information retrieval and natural language processing among others. Main approaches proposed in the literature can be categorised in two large families as follows: (1) Ontology-based semantic similarity Measures (OM) and (2) distributional measures whose most recent and successful methods are based on Word Embedding (WE) models. However, the lack of a deep analysis of both families of methods slows down the advance of this line of research and its applications. This work introduces the largest, reproducible and detailed experimental survey of OM measures and WE models reported in the literature which is based on the evaluation of both families of methods on a same software platform, with the aim of elucidating what is the state of the problem. We show that WE models which combine distributional and ontology-based information get the best results, and in addition, we show for the first time that a simple average of two best performing WE models with other ontology-based measures or WE models is able to improve the state of the art by a large margin. In addition, we provide a very detailed reproducibility protocol together with a collection of software tools and datasets as supplementary material to allow the exact replication of our results.

Inferring Drug-Protein⁻Side Effect Relationships from Biomedical Text.

Background: Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. Objective: The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. Materials and Methods: We extracted three types of relationships—drug-protein, protein-protein, and protein–side effect—from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein–side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. Results: We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by P@5-P@20. In addition, we confirmed that the paths are novel hypotheses that are worth investigating further. Discussion: The risk of side effects has been an important issue for the US Food and Drug Administration (FDA). However, the causes and mechanisms of such side effects have not been fully elucidated. This study extends previous research on understanding drug side effects by using various techniques such as Named Entity Recognition (NER), Relation Extraction (RE), and semantic similarity. Conclusion: It is not easy to reveal the biomedical mechanisms of side effects due to a huge number of possible paths. However, we automatically generated predictable paths using the proposed approach, which could provide meaningful information to biomedical researchers to generate plausible hypotheses for the understanding of such mechanisms.

Improved Synthetic Weighted Algorithm of Ontology-Based Semantic Similarity Computation

To solve the problems of incomplete consideration and low precision in existing domain ontology semantic similarity computation, an improved synthetic weighted algorithm of ontology-based semantic similarity computation is proposed, mixing path coincidence degree, the shortest distance, and concept property methods. First, the depths of lowest common ancestor (LCA) and an ontology tree are added to the formula of path coincidence degree for distinguishing the influence of LCA depth on similarity when multiple inheritances occur. Second, the analysis of similarity algorithm based on the shortest distance cannot distinguish two situations with the same path distance. One is when the density of LCA is different. The other is a depth difference in the concept pair. So, the number of direct subnodes of the LCA and the depth difference are added to the formula of the shortest distance. Meanwhile, the switch of density factor is set to ensure similarity calculation results between [0,1]. Then, a synthetic weighted algorithm of semantic similarity computation is constructed using the weighting path coincidence degree, the shortest distance, and the concept property. Finally, this algorithm and the other three algorithms in the literature are used to calculate semantic similarity in tea ontology. The results show that this algorithm is closest to expert experience.

Improved scalability in mining using ontology record linkage algorithm

Record linkage offers wide role in record identification and relevant datasets matching. The conventional researchers use probabilistic approach to identify reliable and unique datasets. Record linkage with probabilistic approach exploits data, which are common to an individual record pair. Classical methods have equality based record linkage in common fields. Therefore, errors associated with record linkage reduce the scalability. In this paper, a similarity between individual values of record pairs is improved using ontology-based semantic similarity model. Semantic similarity between the records is tested successfully using angle based neighborhood graph. To validate the proposed approach, a conventional record linkage algorithm is compared with angle based neighborhood ontology record linkage technique, which achieves improved accuracy and scalability. Finally, the accuracy of identifying similar semantic matches is more scalable in proposed technique than conventional methods.

An ontology-based framework for extracting spatio-temporal influenza data using Twitter

ABSTRACTEarly detection of influenza outbreaks is one of the key priorities on a national level for preparedness and planning. This study presents the design and implementation of Fluwitter, which is a spatio-temporal web-based prototype framework for pseudo real-time detection of influenza outbreaks from Twitter. Specifically, the framework integrates PostgreSQL database server with PostGIS spatial extension, Twitter streaming client, pre-processor, tagger and similarity calculator for semantic information extraction (IE). The IE of tagged terms is supported by Natural Language Processing (NLP) techniques, DBpediaSpotlight and WordNet Similarity for Java (WS4J), while data analytics, visualization, and mapping are supported by GeoServer and other GIS Free Open Source Software (FOSS). The prototype was calibrated to maximize detection of influenza using rules developed from ontology-based semantic similarity scores. The Twitter-generated influenza cases were validated by weekly hospitalization records issued by Ohio Department of Health (ODH). The optimized rule produced a final F-measure value of 0.72 and accuracy (ACC) value of 94.4%. The validation suggested the existence of moderate correlations for the beginning of the time period Southeast region (r = 0.52), the Northwestern region (r = 0.38), and the Central region (r = 0.33) and weak correlations for the entire time period. The potential strengths and benefits of the prototype are shown through spatio-temporal assessment and visualization of influenza potential in Ohio.

COMPARISON SEMANTIC SIMILARITY APPROACH USING BIOMEDICAL DOMAIN DATASET

Semantic similarity is defined as the closeness of two concepts, based on the likeliness of their meaning. Most semantic similarity is applied uses ontology. This research are uses ontology as a case study due to their efficiency, scalability, lack of constraints and the availability of large ontologies. Ontology-based semantic similarity is used in two situations. The semantic similarity in a single ontology and when multiple ontologies are involved. In this research focus on single ontology where use MeSH datasets as a case study. Semantic similarity single ontology means similarities are compared from the same ontology. The importance of information in the biomedical field, semantic similarity measures have been of great interest.

Hybrid recommendation system based on semantic interest community and trusted neighbors

The accuracy of a recommendation is an important index to evaluate the performance of a recommendation system. The personalized recommendation system tends to pay too much attention to the accuracy of recommendation results and often neglects the diversity of the recommendation results. In this paper, domain ontology is used to construct the user interest model, and the integrated ontology-based semantic similarity algorithm is used to obtain the user ontology set. Then, the semantic interest community is constructed through the hierarchical clustering method. Users with a high degree of diversity are selected as trusted neighbors to construct a hybrid recommendation model with a combination of accuracy and diversity. The experimental results show that the hybrid model can improve the diversity of the recommendation system by adjusting the weight factor while having less influence on the accuracy.

Towards a knowledge-based probabilistic and context-aware social recommender system

In this article, we propose (1) a knowledge-based probabilistic collaborative filtering (CF) recommendation approach using both an ontology-based semantic similarity metric and a latent Dirichlet allocation (LDA) model-based recommendation technique and (2) a context-aware software architecture and system with the objective of validating the recommendation approach in the eating domain (foodservice places). The ontology on which the similarity metric is based is additionally leveraged to model and reason about users’ contexts; the proposed LDA model also guides the users’ context modelling to some extent. An evaluation method in the form of a comparative analysis based on traditional information retrieval (IR) metrics and a reference ranking-based evaluation metric (correctly ranked places) is presented towards the end of this article to reliably assess the efficacy and effectiveness of our recommendation approach, along with its utility from the user’s perspective. Our recommendation approach achieves higher average precision and recall values (8% and 7.40%, respectively) in the best-case scenario when compared with a CF approach that employs a baseline similarity metric. In addition, when compared with a partial implementation that does not consider users’ preferences for topics, the comprehensive implementation of our recommendation approach achieves higher average values of correctly ranked places (2.5 of 5 versus 1.5 of 5).

Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews

ObjectiveSystematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies. MethodsWe developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure. ResultsWe compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%. ConclusionWe demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability.