Ontology Mapping Research Articles

A semantic-based ontology mapping – information retrieval for mobile learning resources

In recent days, Mobile Learning (M-Learning) attains a lot of significance due to its convenience and boundless way of learning. Information retrieval using M-Learning is a challenging and demanding task. For this purpose, some of the techniques are proposed in existing works, but it do not provide the related contents and its results are inaccurate. Moreover, the existing frameworks retrieved many irrelevant information, which leads to high processing time. In order to overcome all these issues, an ontology-based M-Learning framework is introduced in this paper. The main intention of this framework is to reduce the retrieval rate of irrelevant information from the service repository. For this purpose, it performs the mapping and ranking processes with the ontology structure. Moreover, it provides the relevant services to the requested user, which reduces the processing time and computation complexity. This work includes three components such as, user, service user interface and service provider. Initially, the user requests the service to the service provider through the user interface. Then, the service provider receives the request and fetches the related information using the ontology structure. For information retrieval, the processes such as, semantic mapping, sense matching, display relevant information, material ranking and ordered ranking are performed. Moreover, the material repository and clustered repository are utilized to this information. Finally, the service provider fetches the relevant information from these repositories and provides the appropriate service to the requested user. The experimental results evaluate the performance of the proposed system in terms of precision, recall, accuracy, f-measure, term frequency, relevancy, and semantics.

Affine Transformation Based Ontology Sparse Vector Learning Algorithm

Abstract In information science and other engineering applications, ontology plays an irreplaceable role to find the intrinsic semantic link between concepts and to determine the similarity score returned to the user. Ontology mapping aims to excavate the intrinsic semantic relationship between concepts from different ontologies, and the essence of these applications is similarity computation. In this article, we propose the new ontology sparse vector approximation algorithms based on the affine transformation tricks. By means of these techniques, we study the equivalent form of ontology dual problem and determine its feasible set. The simulation experiments imply that our new proposed ontology algorithm has high efficiency and accuracy in ontology similarity computation and ontology mapping in biology, chemical and related fields.

Downregulation of genes related to immune and inflammatory response in IVF implantation failure cases under controlled ovarian stimulation.

Implantation failure (IF) even after the good-quality embryo transfer (ET) is main obstacle in in vitro fertilization (IVF). We aim to study the genomics of endometrial receptivity in IF patients under controlled ovarian stimulation (COS) during which ET is generally practised in IVF. Endometrial gene expression profiling in IF patients (n=10) and oocyte donors (n=8) were compared during window of implantation under COS by microarray. Enrichment analysis of microarray data was performed to determine dysregulated pathways. Microarray results were validated by real-time PCR. Localization of genes related to immune response (progestagen-associated endometrial protein (PAEP), leukaemia inhibitory factor (LIF), interleukin-6 signal transducer (IL6ST) was detected by immunohistochemistry. The gene ontology, pathway analysis and enrichment mapping revealed significant downregulation in activation and regulation of immune and inflammation response in IF patients under COS. The lower expression of PAEP, LIF and IL6ST in cases compared to controls by real time and immunohistochemistry suggests the functional importance of these genes. Importance of immune and inflammatory response in endometrial receptivity adds on to the current knowledge of gene expression profile in IF under COS. The panel of genes involved in these pathways would be useful in determining further line of treatment for IF during IVF.

Exploring biomedical ontology mappings with graph theory methods

BackgroundIn the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies.MethodsWe report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature.ResultsWith comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.

National healthcare programs and policies in India:an ontological analysis

Background: The healthcare system in India is facing multitude of challenges owing to the changing epidemiology of health as well as diseases. Despite having the distinction of being one of the largest public healthcare delivery systems in the world, our health indicators reflect a grim picture in comparison to global targets. India has large number of national healthcare programs and sub-programs, policies, and legislations. Though they are meant to be inclusive and comprehensive the results are far from the desired vision.Methods: Using a logically constructed ontology based on the common body of knowledge as a lens, we map all the programs and policies to reveal the ‘bright’, ‘light’, and ‘blind/blank’ spots in them. The ontological maps are constructed at different levels of granularity–monads and dyads. They provide a synoptic, systematic, and systemic assessment of the policies while highlighting their emphases and biases.Results: In terms of the Focus, the dominant focus of the programs and policies is on information (30) and education (39), drugs (26), administration (31), finance (24), and treatment (28). These are the ‘bright’ spots. Their secondary focus is on technology (18), personnel-staff (12), personnel-physician-specialist (7), and personnel-nurses (8). These are the ‘light’ spots. The tertiary focus is on insurance (4). These are the ‘blind/blank’ spots.Conclusions: The coverage of the National Programs/Policies on healthcare, viewed through the lens of the ontology is patchy. Yet, overall, almost all elements in the ontology find expression in at least one program/policy – an indicator of their extensive coverage. There is absence of frequent co-occurrence of the outcomes, except quality and accessibility. This paper presents the first step in a program of research which can be used to analyse and direct healthcare policy globally, nationally, and locally.

Knowledge Fusion and Synchronization over Ubiquitous Ontology Mapping

With the rapid development and popularization of web services, the available information types and structure are becoming more and more complex and challenging. Actually web services involve the need for dynamic integration and transparent knowledge integration, in light of the urgent information changing track. Under this situation, the traditional search engine and information integration cannot finish this challenge, thereby bringing the opportunity for knowledge fusion and synchronization. This paper proposes a multi-matching strategy ontology mapping method for web information, i.e., ubiquitous ontology mapping method (U-Mapping), which can be viewed as a base collection of information on multiple ontologies made to appear anytime and everywhere. This approach is usually built independently by different information providers, avoiding the grammatical and semantic conflict. Finally, the ontology case information can be utilized under the consolidation of the U-Mapping, concerning language technology and machine learning methods.

TongSACOM: A TongYiCiCiLin and Sequence Alignment-Based Ontology Mapping Model for Chinese Linked Open Data

TongSACOM: A TongYiCiCiLin and Sequence Alignment-Based Ontology Mapping Model for Chinese Linked Open Data

A Semantic Metadata Enrichment Software Ecosystem (SMESE) Based on a Multi-Platform Metadata Model for Digital Libraries

Software industry has evolved to multi-product and multi-platform development based on a mix of proprietary and open source components. Such integration has occurred in software ecosystems through a software product line engineering (SPLE) process. However, metadata are underused in the SPLE and interoperability challenge. The proposed method is first, a semantic metadata enrichment software ecosystem (SMESE) to support multi-platform metadata driven applications, and second, based on mapping ontologies SMESE aggregates and enriches metadata to create a semantic master metadata catalogue (SMMC). The proposed SPLE process uses a component-based software development approach for integrating distributed content management enterprise applications, such as digital libraries. To perform interoperability between existing metadata models (such as Dublin Core, UNIMARC, MARC21, RDF/RDA and BIBFRAME), SMESE implements an ontology mapping model. SMESE consists of nine sub-systems: 1) Metadata initiatives & concordance rules; 2) Harvesting of web metadata & data; 3) Harvesting of authority metadata & data; 4) Rule-based semantic metadata external enrichment; 5) Rule-based semantic metadata internal enrichment; 6) Semantic metadata external & internal enrichment synchronization; 7) User interest-based gateway; 8) Semantic master catalogue. To conclude, this paper proposes a decision support process, called SPLE decision support process (SPLE-DSP) which is then used by SMESE to support dynamic reconfiguration. SPLE-DSP consists of a dynamic and optimized metadata-based reconfiguration model. SPLE-DSP takes into account runtime metadata-based variability functionalities, context-awareness and self-adaptation. It also presents the design and implementation of a working prototype of SMESE applied to a semantic digital library.

China's National Health Policies: An Ontological Analysis.

The health care system in China is facing a multitude of challenges owing to the changing demographics of the country, the evolving economics of health care, and the emerging epidemiology of health as well as diseases. China’s many national health care policies are documented in Chinese text documents. It is necessary to map the policies synoptically, systemically, and systematically to discover their emphases and biases, assess them, and modify them in the future. Using a logically constructed ontology of health care policies based on the common bodies of knowledge as a lens, we map the current policies to reveal their ‘bright’, ‘light’, and ‘blind/blank’ spots. The ontological map will help (a) develop a roadmap for future health care policies in China, and (b) compare and contrast China’s health care policies with other countries’.

Australia’s National Health Programs: An Ontological Mapping

Australia has a large number of health program initiatives whose comprehensive assessment will help refine and redefine priorities by highlighting areas of emphasis, under-emphasis, and non-emphasis. The objectives of our research are to: (a) systematically map all the programs onto an ontological framework, and (b) systemically analyse their relative emphases at different levels of granularity. We mapped all the health program initiatives onto an ontology with five dimensions, namely: (a) Policy-scope, (b) Policy-focus, (c) Outcomes, (d) Type of care, and (e) Population served. Each dimension is expanded into a taxonomy of its constituent elements. Each combination of elements from the five dimensions is a possible policy initiative component. There are 30,030 possible components encapsulated in the ontology. It includes, for example: (a) National financial policies on accessibility of preventive care for family, and (b) Local-urban regulatory policies on cost of palliative care for individual-aged. Four of the authors mapped all of Australia’s health programs and initiatives on to the ontology. Visualizations of the data are used to highlight the relative emphases in the program initiatives. The dominant emphasis of the program initiatives is: [National] [educational, personnel-physician, information] policies on [accessibility, quality] of [preventive, wellness] care for the [community]. However, although (a) information is emphasized technology is not; and (b) accessibility and quality are emphasized cost, satisfaction, and quality are not. The ontology and the results of the mapping can help systematically reassess and redirect the relative emphases of the programs and initiatives from a systemic perspective.

Non-Invasive Sensor Technology for the Development of a Dairy Cattle Health Monitoring System

The intention of this research is to establish a relationship between dairy cattle diseases with various non-invasive sensors for the development of a health monitoring system. This paper expands on the conference paper titled “Sensor technology for animal health monitoring” published in the International Journal on Smart Sensing and Intelligent Systems (s2is) for the proceedings of International Conference on Sensing Technology (ICST) 2014. This paper studies and explores particular characteristics of dairy cattle’s health and behavioural symptoms. The aim is to consider the nature of the diseases a cow may have and relate it with one or many sensors that are suitable for accurate measurement of the behavioural changes. The research uses ontological relationship mapping or ontology matching to integrate heterogeneous databases of diseases and sensors and explains it in detail. This study identifies the sensors needed to determine illnesses in a dairy cow and how they would be beneficial for the development of non-invasive, wearable, smart, dairy cattle health monitoring system to be placed on the cows’ neck. It also explains how the primary sensors identified by this research can be used to forecast cattle health in a simple, basic manner. The scope of this paper is limited to the discussion about the non-invasive, wearable sensors that are needed to determine the cattle diseases. We focused only on non-invasive sensors because they are easy to install on cows and no training is required for them to be installed as compared to invasive sensors. Development of such a system and its evaluation is not in the scope of this paper and is left for our next paper.

An ontology-based semantic retrieval approach for heterogeneous 3D CAD models

Many types of heterogeneous CAD models created by previous product development activities are stored in enterprise databases. Searching reusable CAD models for engineer design requirements is still very difficult. In this paper, we propose an ontology-based semantic retrieval approach for heterogeneous 3D CAD models by using semantic web theory and information retrieval theory. By exploiting layered feature ontology and ontology mapping, a uniform description for heterogeneous CAD models is generated as semantic descriptor. Meanwhile, to improve the representative ability, several heuristic rules are constructed with the aid of SWRL language and ontology reasoning is acted on the semantic descriptors, which makes our retrieval system achieve better performance. Furthermore, multi-mode similarity measurement is carried out to meet engineers’ diversified requirements. We demonstrate the feasibility and effectiveness of our approach through experiments on heterogeneous 3D CAD model library.

Ontology algorithm using singular value decomposition and applied in multidisciplinary

As a popular data management and computation tool, ontology is widely used in material science, biological genetics, chemistry, biology and pharmaceuticals. It can be regarded as a dictionary or a database, and the key of various ontology applications is similarity measuring between concepts in ontology. In this paper, we propose a new ontology learning algorithm for ontology similarity measuring and ontology mapping by means of singular value decomposition method and deterministic sampling iteration. Then, the new ontology learning is applied in plant science, gene biology, bionics and physics ontologies. The data results show the high efficiency of our singular value decomposition based ontology learning algorithm for ontology similarity measuring and ontology mapping.

Ontology mapping and semantics of web interface signs

AbstractAlthough, intuitive interface signs are crucial elements of a good user interface (UI), prior research ignored these in UI design and usability evaluation process. This paper argues that ontology (the set of concepts and skills for understanding the referential meaning of an interface sign) mapping is critical for intuitive sign design. An experiment with fourteen participants and twelve signs (total 168 cases) has been carried out. The data has been analysed using both qualitative and quantitative approaches. The findings suggest that ontology mapping is important for understanding the semantics of interface signs. The paper concludes with design implications and future research plan.

Transcriptional Regulation of Aerobic Metabolism in Pichia pastoris Fermentation.

In this study, we investigated the classical fermentation process in Pichia pastoris based on transcriptomics. We utilized methanol in pichia yeast cell as the focus of our study, based on two key steps: limiting carbon source replacement (from glycerol to methonal) and fermentative production of exogenous proteins. In the former, the core differential genes in co-expression net point to initiation of aerobic metabolism and generation of peroxisome. The transmission electron microscope (TEM) results showed that yeast gradually adapted methanol induction to increased cell volume, and decreased density, via large number of peroxisomes. In the fermentative production of exogenous proteins, the Gene Ontology (GO) mapping results show that PAS_chr2-1_0582 played a vital role in regulating aerobic metabolic drift. In order to confirm the above results, we disrupted PAS_chr2-1_0582 by homologous recombination. Alcohol consumption was equivalent to one fifth of the normal control, and fewer peroxisomes were observed in Δ0582 strain following methanol induction. In this study we determined the important core genes and GO terms regulating aerobic metabolic drift in Pichia, as well as developing new perspectives for the continued development within this field.

Automating the semantic mapping between regulatory guidelines and organizational processes

The mapping of regulatory guidelines with organizational processes is an important aspect of a regulatory-compliance management system. Automating this mapping process can greatly improve the overall compliance process. Currently, there is research on mapping between different entities such as ontology mapping, sentence similarity, semantic similarity and regulation-requirement mapping. However, there has not been adequate research on the automation of the mapping process between regulatory guidelines and organizational processes. In this paper, we explain how Natural Language Processing and Semantic Web technologies can be applied in this area. In particular, we explain how we can take advantage of the structures of regulation-ontology and the process-ontology in order to compute the similarity between a regulatory guideline and a process. Our methodology is validated using a case study in the Pharmaceutical industry, which has shown promising results.

A semantic interoperability approach to health-care data: Resolving data-level conflicts

The integration of data from various electronic health record EHR systems presents a critical conflict in the sharing and exchanging of patient information across a diverse group of health-oriented organizations. Patient health records in each system are annotated with ontologies utilizing different health-care standards, creating ontology conflicts both at the schema and data levels. In this study, we introduce the concept of semantic ontology mapping for the facilitation and interoperability of heterogeneous EHR systems. This approach proposes a means of detecting and resolving the data-level conflicts that generally exist in the ontology mapping process. We have extended the semantic bridge ontology in support of ontology mapping at the data level and generated the required mapping rules to reconcile data from different ontological sources into a canonical format. As a result, linked-patient data are generated and made available in a semantic query engine to facilitate user queries of patient data across heterogeneous EHR systems.

The Anisakis Transcriptome Provides a Resource for Fundamental and Applied Studies on Allergy-Causing Parasites.

BackgroundFood-borne nematodes of the genus Anisakis are responsible for a wide range of illnesses (= anisakiasis), from self-limiting gastrointestinal forms to severe systemic allergic reactions, which are often misdiagnosed and under-reported. In order to enhance and refine current diagnostic tools for anisakiasis, knowledge of the whole spectrum of parasite molecules transcribed and expressed by this parasite, including those acting as potential allergens, is necessary.Methodology/Principal FindingsIn this study, we employ high-throughput (Illumina) sequencing and bioinformatics to characterise the transcriptomes of two Anisakis species, A. simplex and A. pegreffii, and utilize this resource to compile lists of potential allergens from these parasites. A total of ~65,000,000 reads were generated from cDNA libraries for each species, and assembled into ~34,000 transcripts (= Unigenes); ~18,000 peptides were predicted from each cDNA library and classified based on homology searches, protein motifs and gene ontology and biological pathway mapping. Using comparative analyses with sequence data available in public databases, 36 (A. simplex) and 29 (A. pegreffii) putative allergens were identified, including sequences encoding ‘novel’ Anisakis allergenic proteins (i.e. cyclophilins and ABA-1 domain containing proteins).Conclusions/SignificanceThis study represents a first step towards providing the research community with a curated dataset to use as a molecular resource for future investigations of the biology of Anisakis, including molecules putatively acting as allergens, using functional genomics, proteomics and immunological tools. Ultimately, an improved knowledge of the biological functions of these molecules in the parasite, as well as of their immunogenic properties, will assist the development of comprehensive, reliable and robust diagnostic tools.

Pathways, parallels and pitfalls: the Scholarly Web, the ESRC and Linked Open Data

This paper highlights key principles that the eScholarship Research Centre (ESRC) shares with the Linked Open Data (LOD) community, primarily relationship-centric contextualisation of information resources, and a commitment to producing and publishing sustainable, standards-based data outputs suitable for machine-based interchange. This paper illustrates how these principles have enabled the ESRC, without pursuing LOD as a specific end, to hold a path close to the Linked Data road. We also note that, by extension, this has positioned the ESRC ready to translate many of its resources into LOD formats in exchanges where complementary technologies and ontology mappings are available.

Ontology Mapping and Rule-Based Inference for Learning Resource Integration

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.