Ontology Modules Research Articles

Large-Scale Ontology Alignment- An Extraction Based Method to Support Information System Interoperability

Ontology alignment is an important way of establishing interoperability between Semantic Web applications that use different but related ontologies. Ontology alignment is the process of identifying semantically equivalent entities from multiple ontologies. This is not always obvious because technical constraints such as data volume and execution time are determining factors in the choice of an alignment algorithm. Nowadays, partitioning and modularization are two main strategies for breaking down large ontologies into blocks or ontology modules respectively to align ontologies. This article proposes ONTEM as an effective alignment method for large-scale ontology based on the ontology entities extraction. This article conducts a comprehensive evaluation using the datasets of the OAEI 2018 campaign. The obtained results are promising, and they revealed that ONTEM is one of the most effective systems.

Modular Ontology Learning with Topic Modelling over Core Ontology

Nowadays, modular domain ontology, where each module represents a subdomain of the ontology domain, facilitates the reuse of information and provides users with domain-specific knowledge. In this paper, we focus on modular taxonomy learning from text, where each module collects terms with the same topic insights, and in parallel we manage to discover hypernym and 'related' relations among those collected terms.However, it is difficult to automatically fit terms into modules and discover relations.We propose to employ twice trainedLDAto partition termsof each subdomain, and relate subdomains into modules of ontology. Meanwhile, we apply core concept replacement and subdomain knowledge supplementation as supportive information embedding technique over the corpus. This shows that the twice trained LDA strategy can effectively identify topic-relevant terms into subdomains, with nearly two-fold precision comparing to that of normal LDA training. The combination of core concept replacement and subdomain knowledge supplementation contributes to significant improvements in modular taxonomy learning.

Modular Ontologies Composition

This article describes how a modular ontology is a set of interconnected ontology modules. Modularity is a key requirement for collaborative ontology engineering and for distributed ontology reuse on the Web. Combining ontology modules in this context to get a global ontology is an important issue since it requires to resolves mismatches between the compared concepts. This article proposes a novel approach to automatically compose ontology modules. The proposed approach is based on concept structure comparison. The algorithm allowing to merge the ontology modules into a global ontology is detailed and similarity measures are explained. Similarity measures are computed against concept names, attributes and relationships. Experiments performed to test this algorithm are described and evaluation results are equally discussed.

A knowledge base with modularized ontologies for eco-labeling: Application for laundry detergents

Along with the rising concern of environmental performance, eco-labeling is becoming more and more popular. However, the complex process of eco-labeling is demotivating manufacturers and service providers to be certificated. The knowledge contained in eco-labeling criteria documents is not semantically exploitable to computers. Traditional knowledge base in relational data model is not inter-operable, lacks inference support and is difficult to be reused. In our research, we propose a comprehensive knowledge base composed of interconnected OWL (Ontology Web Language) ontologies. This ontology based knowledge base allows reasoning and semantic query. In this paper, a modularization scheme about ontology development is introduced and it has been applied to EU Eco-label (European Union Eco-label) laundry detergent product criteria. This scheme separates entity knowledge and rule knowledge so that the ontology modules can be reused easily in other domains. Reasoning and inference based on SWRL (Semantic Web Rule Language) rules in favor of eco-labeling process is also presented.

An ontology-based monitoring system for multi-source environmental observations

Multi-source observed data are generally characterized by their syntactic, structural and semantic heterogeneities. A key challenge is the semantic interoperability of these data. In this context, we propose an ontology-based system that supports environmental monitoring. Our contributions could be resumed around 1) the construction of an ontology which allows to represent the knowledge and reuse it in a real-world way, 2) the guarantee of the semantic interoperability of ontological modules since the proposed ontology is based on the upper level ontology Basic Formal Ontology (BFO) 3) the modularity of the proposed ontology in order to facilitate its reuse and evolution. The proposed ontology has been implemented and evaluated using quality metrics. We also present a real use case study that demonstrates how the proposed ontology allows implicit knowledge generation.

Intronic miRNA miR-3666 Modulates its Host Gene FOXP2 Functions in Neurodevelopment and May Contribute to Pathogenesis of Neurological Disorders Schizophrenia and Autism

MicroRNAs (miRNAs) are approximately 22-nucleotide-long, non-coding RNAs that bind to complementary mRNAs with inhibitory effect. An intronic miRNA is embedded in a particular gene called its host gene. Our study focuses on the Homo sapiens intronic miRNA-host gene pair, hsa-miR-3666 and FOXP2. Previous report of co-expression of miR-3666 and FOXP2 indicates possible regulation of FOXP2 functions by miR-3666. However, direct correlation has not been shown yet. Therefore, we took a computational approach to determine if and how such modulation occurs. ChIP-seq identified FOXP2 targets and putative miR-3666 targets showed a significant overlap of 574 common target genes. Functional enrichment analysis of common targets revealed over-representation of KEGG pathways and Gene Ontology modules associated with neurodevelopment. These modules, along with further literature mining and protein-protein interaction analysis of FOXP2 and miR3666 identified several specific genes associated with neurodevelopment and finally integration of transcriptomic expressions data lead to the selection of four models depicting the mechanisms by which miR-3666 can modulate FOXP2 functions. Model 1 illustrates that during neurodevelopment, miR-3666 can directly modulate the functions of FOXP2 through regulation of common targets, such as IGF1 and EFNB2, whereas model 2 shows miR-3666 can also indirectly modulate FOXP2 functions by considering targets that are not common for the intronic miRNA-host gene pair, for example CDH2 and LMO4. This direct and indirect regulation is necessary for precise spatial and temporal expression of genes during neurodevelopment. Models 3 and 4 exhibit mechanisms in which the interactions of miR-3666 and FOXP2 with target genes contribute to the pathogenesis of schizophrenia and autism respectively.

Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation

The ever-zooming issue of personalisation in the domain of the e-learning or the adaptive e-learning has emerged as a hot subject of intriguing debate among the inquisitive investigators in the last few years. In the learning contents module, all the critical learning materials in the shape of text, video or audio are collected and saved by means of the data management approach so as to effectively orchestrate the entire learning material. In the profile ontology module, the learner profile is saved as the ontology with a clear-cut framework and data. The Markov model is also utilised in this regard. In the instructor module, the online tutoring for each and every learner in accordance with the protocol devised by means of the captioned techniques is carried out. The execution is performed through the JAVA programming and protege device and the performance is subjected to assessment.

Query and Predicate Emptiness in Ontology-Based Data Access

In ontology-based data access (OBDA), database querying is enriched with an ontology that provides domain knowledge and additional vocabulary for query formulation. We identify query emptiness and predicate emptiness as two central reasoning services in this context. Query emptiness asks whether a given query has an empty answer over all databases formulated in a given vocabulary. Predicate emptiness is defined analogously, but quantifies universally over all queries that contain a given predicate. In this paper, we determine the computational complexity of query emptiness and predicate emptiness in the EL, DL-Lite, and ALC-families of description logics, investigate the connection to ontology modules, and perform a practical case study to evaluate the new reasoning services.

ELSE: an ontology-based system integrating semantic search and e-learning technologies

ABSTRACTELSE (E-Learning for the Semantic ECM) is an ontology-based system which integrates semantic search methodologies and e-learning technologies. It has been developed within a project of the CME (Continuing Medical Education) program – ECM (Educazione Continua nella Medicina) for Italian participants. ELSE allows the creation of e-learning courses which are customized according to the needs and learning preferences of the user. It relies on a reference domain ontology and teaching multimedial interactive modules, referred to as Reusable Learning Objects (RLOs), which are annotated according to the concepts of the ontology. The SemSim semantic search method allows the selection of the set of RLOs that satisfy the user request at best in efficient way. This set is successively reorganized according to the learning preferences of the user. ELSE has been validated and experimented in the domain of osteoporosis, and the overall judgment about it is very positive, both in terms of usability and effectiveness of customization.

Introduction of Ontology based Query Retrieval Mechanism in Cloud Computing Architecture

Cloud computing is one of the Information Communication Technology (ICT) trends. It is amalgamation of network of computers connected by centralized physical server. These servers are located on remote computers so that we can access applications directly from distant computer by sitting at our place. The paper makes readers aware of concepts of cloud computing along with its architecture. Few distinctive features among different cloud models have been described. It is believed that existing model must be improved time to time to ensure efficient computing of tasks. Keeping this in mind, an improved architecture of cloud computing has been proposed in following paper. The proposed model consists of ontology module into cloud information architecture and retrieves terms related to user query using Query Retrieval Mechanism.

Cellular processes involved in human epidermal cells exposed to extremely low frequency electric fields

We observed on different tissues and organisms a biological response after exposure to pulsed low frequency and low amplitude electric or electromagnetic fields but the precise mechanism of cell response remains unknown. The aim of this publication is to understand, using bioinformatics, the biological relevance of processes involved in the modification of gene expression. The list of genes analyzed was obtained after microarray protocol realized on cultures of human epidermal explants growing on deepidermized human skin exposed to a pulsed low frequency electric field. The directed acyclic graph on a WebGestalt Gene Ontology module shows six categories under the biological process root: “biological regulation”, “cellular process”, “cell proliferation”, “death”, “metabolic process” and “response to stimulus”. Enriched derived categories are coherent with the type of in vitro culture, the stimulation protocol or with the previous results showing a decrease of cell proliferation and an increase of differentiation. The Kegg module on WebGestalt has highlighted “cell cycle” and “p53 signaling pathway” as significantly involved. The Kegg website brings out interactions between FoxO, MAPK, JNK, p53, p38, PI3K/Akt, Wnt, mTor or NF-KappaB. Some genes expressed by the stimulation are known to have an exclusive function on these pathways. Analyses performed with Pathway Studio linked cell proliferation, cell differentiation, apoptosis, cell cycle, mitosis, cell death etc. with our microarrays results. Medline citation generated by the software and the fold change variation confirms a diminution of the proliferation, activation of the differentiation and a less well-defined role of apoptosis or wound healing. Wnt and DKK functional classes, DKK1, MACF1, ATF3, MME, TXNRD1, and BMP-2 genes proposed in previous publications after a manual analysis are also highlighted with other genes after Pathway Studio automatic procedure. Finally, an analysis conducted on a list of genes characterized by an accelerated regulation after extremely low frequency pulsed stimulation also confirms their role in the processes of cell proliferation and differentiation. Bioinformatics approach allows in-depth research, without the bias of pre-selection, on cellular processes involved in a huge gene list.

Integration of a prognostic gene module with a drug sensitivity module to identify drugs that could be repurposed for breast cancer therapy

BackgroundEfficiently discovering low risk drugs is important for drug development. However, the heterogeneity in patient population complicates the prediction of the therapeutic efficiency. Drug repositioning aiming to discover new indications of known drugs provides a possible gateway. MethodWe introduce a novel computational method to identify suitable drugs by using prognosis information of patients. First, we identify prognostic related gene modules, Prognostic Gene Ontology Module (PGOMs), by incorporating multiple functional annotations. Then, we build the drug sensitivity modules based on gene expressions and drug activity patterns. Finally, we analyze the potential effects of drugs on prognostic gene modules and establish the links between PGOMs and drugs. Result and discussionWith PGOMs generated based on the patient outcome, FDA approved drugs for breast cancer treatment have been successfully identified on one hand; several drugs that have not been approved by FDA, such as Etoposide, have found to strongly associate with the outcome on the other hand. With PGOMs generated based on the patient ER status, Tamoxifen and Exemestane rank at the top of the drug list, suggesting that they may be more specific to ER status of breast cancer. Especially, the rank difference of Exemestane in ER+ group and ER− group is very large, demonstrating that Exemestane may be more specific to ER+ breast cancer and would cause side-effect to ER− breast cancer patients. Our method can not only identify the drugs that could be repurposed for breast cancer therapy, but also can reveal their effective pharmacological mechanisms.

A Method for Ontology Module Extract

In semantic Web, extensive reuse of existing large ontology is one of the central ideas of ontology engineering. Ontology extraction should return relative sub-ontology that covers some sub-vocabulary. The efficiency of the existing ontology extraction algorithm is relatively low when they try to get a suitable ontology module from ontology at run time. This paper proposed a kind of ontology module extraction method. Related concepts and criterions of ontology modules extraction are studied; data structures and identification and evaluation methods of ontology module extraction are discussed; preliminary experimental results and the corresponding analysis are also shown.

Modular ontologies and CBR-based hybrid system for web information retrieval

Increasing amounts of data volume used on the Web and their heterogeneous character make the search for information a challenging task. Several advanced computing methods and technologies propose to incorporate a degree of semantic analysis during the search based on ontologies. Ontology engineering is based on the methods and methodologies for building ontologies combined with engineering process. This paper proposes a hybrid system based on ontology engineering and aiming to enhance web information retrieval results, by combining automatic Modular Ontology building with CBR (CBRModOnto). The system integrates a novel dynamic composition approach of modular ontologies, performed to reorganize the overlapping concepts between the different ontology modules and updates their hierarchy using semantic similarity measure. The search in our system occurs in two main phases. The first one composes modular ontologies and manages the knowledge base, while the second phase manages the CBR process. A demonstration case study presents a scenario to illustrate the proposed system. Our system has been implemented, the obtained experimental results show that hybridization that we propose enables an improvement of query reformulation, predicted ranking score and user's satistaction.

A method of extracting ontology module using concept relations for sharing knowledge in mobile cloud computing environment.

In mobile cloud computing environment, the cooperation of distributed computing objects is one of the most important requirements for providing successful cloud services. To satisfy this requirement, all the members, who are employed in the cooperation group, need to share the knowledge for mutual understanding. Even if ontology can be the right tool for this goal, there are several issues to make a right ontology. As the cost and complexity of managing knowledge increase according to the scale of the knowledge, reducing the size of ontology is one of the critical issues. In this paper, we propose a method of extracting ontology module to increase the utility of knowledge. For the given signature, this method extracts the ontology module, which is semantically self-contained to fulfill the needs of the service, by considering the syntactic structure and semantic relation of concepts. By employing this module, instead of the original ontology, the cooperation of computing objects can be performed with less computing load and complexity. In particular, when multiple external ontologies need to be combined for more complex services, this method can be used to optimize the size of shared knowledge.

CiTO + SWAN: The web semantics of bibliographic records, citations, evidence and discourse relationships

Most literature searching in biomedicine is now conducted via PubMed, Google Scholar or other web-based bibliographic search mechanisms. Yet until now a public, open, interoperable and complete web-adapted information schema for bibliographic citations, bibliographic references and scientific discourse has not been available. Such a schema, expressed in the form of a description logic compatible with current web semantics approaches, would provide the ability to treat bibliographic references and citations, and rhetorical discourse in scientific publications, as semantic metadata on the web, with all the benefits that implies for organization, search and mash-up of web-based scientific information.In this paper we present CiTO + SWAN, a set of fully harmonized ontology modules resulting from the harmonization of CiTO the Citation Typing Ontology with SWAN Semantic Web Applications in Neuromedicine, which we have developed by jointly adapting and evolving version 1.6 of CiTO, the Citation Typing Ontology, and version 1.2 of the SWAN Scientific Discourse Ontology v1.2. The CiTO + SWAN model is specified in OWL 2 DL, is fully modular, and inherently supports agent-based searching and mash-ups.Through the harmonization activity presented here, and previous work that harmonized SWAN with the SIOC Semantically-Interlinked Online Communities Ontology for describing blogs, wikis and discussion groups, we have construct the basis of a powerful new web framework for scientific communications.

Pathways systematically associated to Hirschsprung's disease.

Despite it has been reported that several loci are involved in Hirschsprung’s disease, the molecular basis of the disease remains yet essentially unknown. The study of collective properties of modules of functionally-related genes provides an efficient and sensitive statistical framework that can overcome sample size limitations in the study of rare diseases. Here, we present the extension of a previous study of a Spanish series of HSCR trios to an international cohort of 162 HSCR trios to validate the generality of the underlying functional basis of the Hirschsprung’s disease mechanisms previously found. The Pathway-Based Analysis (PBA) confirms a strong association of gene ontology (GO) modules related to signal transduction and its regulation, enteric nervous system (ENS) formation and other processes related to the disease. In addition, network analysis recovers sub-networks significantly associated to the disease, which contain genes related to the same functionalities, thus providing an independent validation of these findings. The functional profiles of association obtained for patients populations from different countries were compared to each other. While gene associations were different at each series, the main functional associations were identical in all the five populations. These observations would also explain the reported low reproducibility of associations of individual disease genes across populations.

Study on Small Power BLDCM Drive System

According to the voltage balance equation, the paper deduces the mathematical model of BLDCM on the basis of analyzing the operation principle. In SIMULINK environment, the BLDCM ontology module is built by adopting the combination of BLDCM state equation with S function method. The built motor ontology module proves to be accurate and reasonable through the analysis of the simulation waveform, which helps to analysis BLDCM electronic commutation, etc. Again taking LM621N as commutation control chip, using PIC microcontroller output variable frequency square wave, and through external input device to adjust the square wave frequency, thus realizes the control of motor speed. A brushless dc motor drive with circuit protection functions is designed by the method of combining hardware and software. Not only the motor work situation of ontology is intuitively reflected, and the development cycle is shorted.

Query-driven approach of contextual ontology module learning using web snippets

The main objective of this work is to automatically build ontology modules that cover search terms of users in ontology-based question answering on the Web. Indeed, some arising approaches of ontology module extraction aim at solving the problem of identifying ontology fragment candidates that are relevant for the application. The main problem is that these approaches consider only the input of predefined ontologies, instead of the underlying semantics represented in texts. This work proposes an approach of contextual ontology module learning covering particular search terms by analyzing past user queries and by searching for web snippets provided by the traditional search engines. The obtained contextual modules will be used for query reformulation. The proposal has been evaluated on the ground of two criteria: the semantic cotopy measure of discovered ontology modules and the precision measure of the search results obtained by using the resulted ontology modules for query reformulation. The experiments have been carried out according to two case studies: an open domain web search and the medical digital library "PubMed".

A dynamic composition of ontology modules approach: application to web query reformulation

As the semantic web emerges, the problems related to the management of ontologies are gaining relevance. Ontology management is considered as one of the main issues in artificial intelligence and knowledge engineering. Ontology modularisation aims at structuring ontologies to facilitate its reuse and maintenance. Compositional methodologies intend to integrate a set of modules into a larger ontology. This paper presents a dynamic approach for ontology modules composition based on semantic similarity measures between concepts on the web, and aiming to improve the resulting modular ontology’s structure and content. The proposed approach takes place in three main phases, namely: cooccurrence graph construction of ontology modules, cooccurrence graph clustering of modular ontologies and hierarchical modules clusters construction. To evaluate our approach, we compare the obtained results with those of other compositional approaches in the literature, and we also show how these modular ontologies can be used to improve web query reformulation.