Ontology Mapping Research Articles

From mud to the museum: Metadata challenges in archaeology

An archaeological site is a palimpsest in which the evidence of the depositional episodes is destroyed through the excavation processes; all that remains are the artefacts and their documentary evidence manifested in registers, datasets, dig diaries and reports. While the reports may represent the end product of a specific excavation, the archaeological record tells a story; it is interpretative and dynamic, with later excavations adding new knowledge and narratives. Museums preserve the artefacts but unless the documentary evidence is preserved in standard formats, it cannot be easily re-used by the archaeology community to create that knowledge; nor can museums provide the narratives for the general public whose cultural heritage it is. This article presents a case study from the Ness of Brodgar excavations that examines possibilities for reconciling one part of the data of an archaeological dig, the small finds register (SFR) and its sparse amount of descriptive metadata, with the potentiality of data re-use and with the requirements of a museum that may have custody of the artefacts. It maps and enriches messy domain-specific ontologies to standard archaeological and cultural heritage ontologies and taxonomies using simple natural language processing, linked open data and the museum CIDOC conceptual reference model (CRM). This research, in examining the application of ontology mapping tools, explores common practices and processes that are useful in any discipline within the cultural heritage domain.

Coordinate descent based ontology sparse vector computing strategy and its applications

In recent years, as a semantic analysis and computational tool, ontology has been widely applied in many engineering applications. Many cases suggests that it’s confronted with countless big data source with the complex data structures. In order to relieve the dilemma, the sparse learning algorithms are introduced into the ontology similarity measuring and ontology mapping. In this setting, it should be a high dimensional expression of each ontology vertex, and the ontology algorithm should extract key component information effectively. Under such background, we consider the ontology sparse vector learning algorithm and application in different engineering applications. In this article, by means of coordinate descent minimization tricks, we present the ontology sparse vector optimization strategy and discuss the different transformation in different settings. At last, the new ontology sparse vector learning proceeding is applied to four engineering applications respectively to get its efficiency verified.

Ontology learning algorithm for similarity measuring and ontology mapping using linear programming

Ontology learning algorithm for similarity measuring and ontology mapping using linear programming

The transcriptome of the developing grain: a resource for understanding seed development and the molecular control of the functional and nutritional properties of wheat

BackgroundWheat is one of the three major cereals that have been domesticated to feed human populations. The composition of the wheat grain determines the functional properties of wheat including milling efficiency, bread making, and nutritional value. Transcriptome analysis of the developing wheat grain provides key insights into the molecular basis for grain development and quality.ResultsThe transcriptome of 35 genotypes was analysed by RNA-Seq at two development stages (14 and 30 days-post-anthesis, dpa) corresponding to the mid stage of development (stage Z75) and the almost mature seed (stage Z85). At 14dpa, most of the transcripts were associated with the synthesis of the major seed components including storage proteins and starch. At 30dpa, a diverse range of genes were expressed at low levels with a predominance of genes associated with seed defence and stress tolerance. RNA-Seq analysis of changes in expression between 14dpa and 30dpa stages revealed 26,477 transcripts that were significantly differentially expressed at a FDR corrected p-value cut-off at ≤0.01. Functional annotation and gene ontology mapping was performed and KEGG pathway mapping allowed grouping based upon biochemical linkages. This analysis demonstrated that photosynthesis associated with the pericarp was very active at 14dpa but had ceased by 30dpa. Recently reported genes for flour yield in milling and bread quality were found to influence wheat quality largely due to expression patterns at the earlier seed development stage.ConclusionsThis study serves as a resource providing an overview of gene expression during wheat grain development at the early (14dpa) and late (30dpa) grain filling stages for use in studies of grain quality and nutritional value and in understanding seed biology.

Differential Expression Profile of lncRNAs from Primary Human Hepatocytes Following DEET and Fipronil Exposure.

While the synthesis and use of new chemical compounds is at an all-time high, the study of their potential impact on human health is quickly falling behind, and new methods are needed to assess their impact. We chose to examine the effects of two common environmental chemicals, the insect repellent N,N-diethyl-m-toluamide (DEET) and the insecticide fluocyanobenpyrazole (fipronil), on transcript levels of long non-protein coding RNAs (lncRNAs) in primary human hepatocytes using a global RNA-Seq approach. While lncRNAs are believed to play a critical role in numerous important biological processes, many still remain uncharacterized, and their functions and modes of action remain largely unclear, especially in relation to environmental chemicals. RNA-Seq showed that 100 µM DEET significantly increased transcript levels for 2 lncRNAs and lowered transcript levels for 18 lncRNAs, while fipronil at 10 µM increased transcript levels for 76 lncRNAs and decreased levels for 193 lncRNAs. A mixture of 100 µM DEET and 10 µM fipronil increased transcript levels for 75 lncRNAs and lowered transcript levels for 258 lncRNAs. This indicates a more-than-additive effect on lncRNA transcript expression when the two chemicals were presented in combination versus each chemical alone. Differentially expressed lncRNA genes were mapped to chromosomes, analyzed by proximity to neighboring protein-coding genes, and functionally characterized via gene ontology and molecular mapping algorithms. While further testing is required to assess the organismal impact of changes in transcript levels, this initial analysis links several of the dysregulated lncRNAs to processes and pathways critical to proper cellular function, such as the innate and adaptive immune response and the p53 signaling pathway.

A novel algorithm for fully automated mapping of geospatial ontologies

Geospatial information is collected from different sources thus making spatial ontologies, built for the same geographic domain, heterogeneous; therefore, different and heterogeneous conceptualizations may coexist. Ontology integrating helps creating a common repository of the geospatial ontology and allows removing the heterogeneities between the existing ontologies. Ontology mapping is a process used in ontologies integrating and consists in finding correspondences between the source ontologies. This paper deals with the “mapping” process of geospatial ontologies which consist in applying an automated algorithm in finding the correspondences between concepts referring to the definitions of matching relationships. The proposed algorithm called “geographic ontologies mapping algorithm” defines three types of mapping: semantic, topological and spatial.

Holland’s Method as a Material in Environmental Education

In this work, the Holland’s methodology is modified for the categorization of human personalities by including queries (in the corresponding questionnaire that serves as an evaluation tool) investigating its responders’ attitude as regards his/her willingness to be professionally engaged with the natural or/and anthropogenic environment. The population used as representative sample consisted of 250 students following courses at high school and university level. The quantitative methods used were descriptive statistics, parametric and non-parametric statistics hypothesis (on causal relations) testing, categorical semantics, ontological mapping fuzzy sets and interval algebra. The results obtained showed relative significant internal consistency at macro-level for almost half of the interviewees, but the dependence of answers to environmental queries on the rest responds to the rest queries was insignificant, indicating lack of specific knowledge and clarification of the corresponding concepts at micro-level.

Analysis of protein targets in pathogen-host interaction in infectious diseases: a case study on Plasmodium falciparum and Homo sapiens interaction network.

Infection and disease progression is the outcome of protein interactions between pathogen and host. Pathogen, the role player of Infection, is becoming a severe threat to life as because of its adaptability toward drugs and evolutionary dynamism in nature. Identifying protein targets by analyzing protein interactions between host and pathogen is the key point. Proteins with higher degree and possessing some topologically significant graph theoretical measures are found to be drug targets. On the other hand, exceptional nodes may be involved in infection mechanism because of some pathway process and biologically unknown factors. In this article, we attempt to investigate characteristics of host-pathogen protein interactions by presenting a comprehensive review of computational approaches applied on different infectious diseases. As an illustration, we have analyzed a case study on infectious disease malaria, with its causative agent Plasmodium falciparum acting as 'Bait' and host, Homo sapiens/human acting as 'Prey'. In this pathogen-host interaction network based on some interconnectivity and centrality properties, proteins are viewed as central, peripheral, hub and non-hub nodes and their significance on infection process. Besides, it is observed that because of sparseness of the pathogen and host interaction network, there may be some topologically unimportant but biologically significant proteins, which can also act as Bait/Prey. So, functional similarity or gene ontology mapping can help us in this case to identify these proteins.

Automated mapping for geospatial ontologies integration

ABSTRACTThis paper proposes a novel method to overcome the geospatial ontologies heterogeneity by developing an algorithm for their fully automated mapping. The proposed algorithm, called Geospatial Ontology Mapping Algorithm (GOMA), defines three types of mapping: semantic, topological and spatial. The proposed algorithm is applied to the road domain of Sfax city and revealed correspondences between concepts that would serve to building a resulting integrated ontology.

Holland’s Method as a Material in Environmental Education

In this work, the Holland’s methodology is modified for the categorization of human personalities by including queries (in the corresponding questionnaire that serves as an evaluation tool) investigating its responders’ attitude as regards his/her willingness to be professionally engaged with the natural or/and anthropogenic environment. The population used as representative sample consisted of 250 students following courses at high school and university level. The quantitative methods used were descriptive statistics, parametric and non-parametric statistics hypothesis (on causal relations) testing, categorical semantics, ontological mapping fuzzy sets and interval algebra. The results obtained showed relative significant internal consistency at macro-level for almost half of the interviewees, but the dependence of answers to environmental queries on the rest responds to the rest queries was insignificant, indicating lack of specific knowledge and clarification of the corresponding concepts at micro-level.

Multi-domain ontology mapping based on semantics

Ontology mapping indicates the semantic interconnection between the concepts of ontologies, while multi-domain ontology mapping is usually used to solve the semantic interconnection problem between domain ontologies. However, due to the differences in the definition approaches, there exists the heterogeneity among the domain ontologies to a certain extent. This paper proposes a probability-based and similarity-based ontology mapping algorithm, the purpose of which is to calculate the similarity between the concepts of the multi-domain ontology. Using the ESA algorithm based on Wikipedia and the principle that the similarity between the concepts with the same name equals 1, the paper proposes a new concept, ontology mapping association graph, to represent mapping results. The experiments show that the accuracy rate of the probability-based and similarity-based ontology mapping algorithm can reach 80% on both two Chinese test sets, namely, WordSimilarity-353 and Words-240. Compared with other algorithms, it does stand out on the aspect of accuracy.

Ontological Map of Service Oriented Architecture Based on Zachman

<em>Service orientation is an approach in the field of enterprise architecture, business information systems and software application that its main element is the service. Shared services is an organization model of sharing, across an organization. It enables collaboration among the functions/departments. Main motivations for shared services are sharing, promote efficiency, reduce cost, and support scalability. Despite of the widespread use of these two approaches in information technology, there is no tool to optimize the management of them. The aim of this study is Ontological map of service oriented architecture based on zachman framework to adapt it in the reference enterprise architecture framework through implementation ontology views on system architect software and as well as equivalent ontology component with UML diagrams. After the implementation of the suggested model, the results showed that ontology is a formal description and explicit display of objects, concepts and other entities in the relationship between them. In other words, there is a model that describe all that is in fact in to understandable language for the system. Thus the proposed establishes have association between all aspects of zachman framework, also to create a clear description of business concepts in the management of shared services and is effective to provide a unified platform for enterprise modeling.</em>

Abstract 2596: MediSapiens Explorer Technology Platform - Integrated Solution for big biomedical data

Abstract During recent years, the increasing throughput and decreasing cost of technologies for genomic, transcriptomic, proteomic and other high throughput profiling of samples has enabled collection of very rich data sets on large sample cohorts. As methods have been developed to analyze e.g. large human germline sequencing studies, it has simultaneously become clear that the potential of the data can be fully and effectively leveraged only if technical solutions are developed to curate, store, harmonize and jointly use these omics data sets together with clinical and phenotypic data on the samples. In the field of modern data-driven research, heterogeneous data is increasingly available through many dispersed resources, and in many cases, data from public sources is combined with proprietary data for analysis. Thus, it is not only important to have effective solutions for data storing and data management, but for data curation and data analysis and visualization. Data curation ensures that data from various sources are harmonized, standardized and cross-comparable. Powerful data analytics and visualization is pivotal in transforming data into information, new insights and added value. To address these problems, MediSapiens has developed the Explorer™ Technology Platform solution, based on its extensive experience in both industrial software development and academic biomedical research. The Explorer™ Technology Platform has been designed for all of the three major parts of the data research workflow: For curating data, storing and managing data, and analyzing and visualizing data. This web browser based, integrated solution provides functionalities to import, curate, ontology map and store rich clinical data, as well as very large data sets of gene expression, copy number, mutation and other profiling data for both bulk and single-cell analyses (e.g. single-cell RNA-seq), and perform various analyses and visualization on the data. Existing software components in the Explorer™ Technology Platform can quickly and efficiently be used to construct custom UI tools to query, analyze and interpret data according to individual customer needs. Further, the platform provides APIs which enable retrieval of data for integration with other systems, or development of custom analyses using e.g. R. Here, we will illustrate the application of Explorer Technology Platform concept to solve a variety of problems in preclinical cancer research and personalized medicine, starting from efficient automated curation of clinical data through to integration of clinical and multi-omics data. Citation Format: Henrik Edgren, MediSapiens bioinformatics and development teams. MediSapiens Explorer Technology Platform - Integrated Solution for big biomedical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2596. doi:10.1158/1538-7445.AM2017-2596

The Implementation of DSSim

This paper presents the decisions taken during the implementation of DSSim (DSSim stands for Similarity based on Dempster-Shafer) our multi-agent ontology mapping system. It describes several types of agents and their roles in the DSSim architecture. These agents are mapping agents which are able to perform either semantic or syntactic similarity. Our architecture is generic as no mappings need to be learned in advance and it could be easily extended by adding new mapping agents in the framework. The new added mapping agents could run different similarity algorithms (either semantic or syntactic). In this way, DSSim could assess which algorithm has a better performance. Additionally, this paper presents the algorithms used in our ontology alignment system DSSim.

Graph Laplacian Based Ontology Regularization Distance Framework for Ontology Similarity Measuring and Ontology Mapping

The core of a large number of ontology engineering applications is,similarity measuring, and the essence of ontology mapping and its applications are also similarity computation. There are a great variety of learning techniques are designed for ontology similarity calculating in different engineering circumstances. In these learning settings, all the semantic information of concept is enclosed in the p dimensional vector. In this paper, the ontology similarity measuring is considered via geometric distance calculating between the vectors of ontology vertex corresponding to. We use regularization framework to solute the optimal ontology distance function and apply graph Laplacian to make full use of the unlabeled ontology information. Finally, two simulation experiments imply that our graph Laplacian based ontology regularization distance framework works well in plant and humanoid robotics ontology applications in which higher precision ratio results are obtained from our algorithm than previous ontology learning tricks.

Survey of High Throughput RNA-Seq Data Reveals Potential Roles for lncRNAs during Development and Stress Response in Bread Wheat.

Long non-coding RNAs (lncRNAs) are a family of regulatory RNAs that play essential role in the various developmental processes and stress responses. Recent advances in sequencing technology and computational methods enabled identification and characterization of lncRNAs in certain plant species, but they are less known in Triticum aestivum (bread wheat). Herein, we analyzed 52 RNA seq data (>30 billion reads) and identified 44,698 lncRNAs in T. aestivum genome, which were characterized in comparison to the coding sequences (mRNAs). Similar to the mRNAs, lncRNAs were also derived from each sub-genome and chromosome, and showed tissue developmental stage specific and differential expression, as well. The modulated expression of lncRNAs during abiotic stresses like heat, drought, and salt indicated their putative role in stress response. The co-expression of lncRNAs with vital mRNAs including various transcription factors and enzymes involved in Abscisic acid (ABA) biosynthesis, and gene ontology mapping inferred their regulatory roles in numerous biological processes. A few lncRNAs were predicted as precursor (19 lncRNAs), while some as target mimics (1,047 lncRNAs) of known miRNAs involved in various regulatory functions. The results suggested numerous functions of lncRNAs in T. aestivum, and unfolded the opportunities for functional characterization of individual lncRNA in future studies.

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

ABSTRACTIn the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system also provides a natural language interface to easily query linked databases. The expected result of this ontology-based approach of knowledge representation and data access is an increase in knowledge and data sharing in this domain, which will enhance existing business analysis methods.

Disequilibrium multi-dividing ontology learning algorithm

ABSTRACTAs an effective tool for data storage, processing, and computing, ontology has been used in many fields of computer science and information technology. By means of its powerful performance on semantic query and knowledge extraction, domain ontology has been built on various disciplines such as biology, pharmaceutics, geography, chemistry, etc. and been smoothly employed for their engineering applications. In these ontology applications, we aim to get an optimal ontology function which maps each ontology to a real number and then determine the similarity between concepts by the distance of their corresponding real numbers. In former ontology learning approaches, all the instances in the training sample have equal status in the learning process. In this article, we present the disequilibrium multi-dividing ontology algorithm in which the important ontology data will be highlighted during the learning, and the relevant ontology data tend to be eliminated. Four experiments are designed to test the serviceability of our disequilibrium multi-dividing algorithm from angles of ontology similarity measuring and ontology mapping construction.

Distance learning techniques for ontology similarity measuring and ontology mapping

Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

Information technology to support patient engagement: where do we stand and where can we go?

Given the strong push to empower patients and make them partners in their health care, we evaluated the current capability of hospitals to offer health information technology that facilitates patient engagement (PE). Using an ontology mapping approach, items from the American Hospital Association Information Technology Supplement were mapped to defined levels and categories within the PE Framework. Points were assigned for each health information technology function based upon the level of engagement it encompassed to create a PE-information technology (PE-IT) score. Scores were divided into tertiles, and hospital characteristics were compared across tertiles. An ordered logit model was used to estimate the effect of characteristics on the adjusted odds of being in the highest tertile of PE-IT scores. Thirty-six functions were mapped to specific levels and categories of the PE Framework, and adoption of each item ranged from 23.5 to 96.7%. Hospital characteristics associated with being in the highest tertile of PE-IT scores included medium and large bed size (relative to small), nonprofit (relative to government nonfederal), teaching hospital, system member, Midwest and South regions, and urban location. Hospital adoption of PE-oriented technology remains varied, suggesting that hospitals are considering how technology can create partnerships with patients. However, PE functionalities that facilitate higher levels of engagement are lacking, suggesting room for improvement. While hospitals have reached modest levels of adoption of PE technologies, consistent monitoring of this capacity can identify opportunities to use technology to facilitate engagement.