Cross Ontology Research Articles

Novel approach for semantic similarity cross ontology

Measuring <span>semantic similarity between terms is a crucial step in information retrieval and integration since it necessitates semantic content matching. Even though several models have been proposed to measure semantic similarity, these models are not able to effectively quantify the weight of relevant items that affect the semantic similarity judgment process. In this study, we present a new method for measuring semantic similarity between cross-ontologies, that consists of hybridizing node-based approaches such as WuP and Reda with the weight of similarity computed using WordNet. The proposed approach has been experimented to show its efficiency with two ontologies, configuration management tool (CMT) and ConfOf, from the conference domaine in the web ontology language (OWL) ontologies benchmark OAEI 2015 and evaluated using two metrics: density and cohesion.</span>

HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences.

Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provides researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein—ontology term—disease relations. As an application of the proposed approach, HPO term—protein associations (i.e., HPO2protein) were predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO performance was among the best (Fmax = 0.35). The automated cross ontology mapping approach developed in this work may be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

Identification of New Parameters for Ontology Based Semantic Similarity Measures

A major challenge among various applications of computational linguistics, natural language processing and information retrieval is to measure semantic similarity accurately. In this research paper, various ontology-based approaches i.e. compute semantic similarity between words have been studied and listed their benefits and shortcomings on the various identified parameters. Earlier, correlation with human judgment was the single criteria for the judgment of good similarity measures. In this paper, more parameters for semantic similarity measures have been identified and a relative analysis of similarity measures is performed on the identified parameters. These identified parameters can be further utilized for formulating the new semantic similarity-measures in the latest research area of text mining, web mining and information retrieval. We have identified various parameters like features-set, applicability on various ontologies such as single ontology, cross ontology or fuzzy ontology, Ontology type, dataset applied and relationship mapping for the various measures. Through detailed analysis we have found that feature based and hybrid approaches has higher accuracy as compare to edge and content based methods and works in different type of ontologies. Recent research drawing interest to find new feature set in this area like fuzzy distance, graph generation and text snippets etc. Max Accuracy was achieved in single ontology 0.87 and 0.83 over cross ontologies. WordNet and MeSH are maximally utilized Global Ontologies.

Prediction of Micro-RNA Diseases using Cross Ontology and Gene Ontology

Prediction of Micro-RNA Diseases using Cross Ontology and Gene Ontology

Personalized Cross Ontological Framework for Secured Document Retrieval in the Cloud

Personalization is crucial in the prevailing internet scenario. An efficient information retrieval relies not only on personalization but also upon security. For this, a semantic association is needed for document retrieval, to further boost the search quality. In this paper, multiple ontologies are used, for mining the semantic associations. Multiple ontologies can share a variety of concepts and automatic merging is also feasible. With semantic association any input query is matched for finding relevant words. Semantic similarity measure is proposed to match the semantic association of query therewith of the documents, based on the query expansion technique. A user profiling model is constructed which is trained for personalization. This model ensures secure retrieval of the documents. A novel framework is proposed for providing secured document retrieval based on cross ontology mining. The results show good MAP and F-Measure rate of document retrieval when compared to the existing techniques.

Using concept similarity in cross ontology for adaptive e-Learning systems

e-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert’s ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods.

Designing a novel cross ontology measure to extract resources from knowledge base for an adaptive e-learning system

E-Learning deals with the interaction between the teacher and learner on the basis of knowledge possessed by the learner. Ontology is a key concept that helps in providing more relevant and accurate information to the users for a particular domain or from multiple domains. Finding semantic similarity between the concepts of different ontologies will enhance the integration and retrieval of resources in a more meaningful way. In this paper, an adaptive e-learning system is proposed, which generates use r-specific e-learning contents based on their knowledge level and by comparing the similarity of concepts between ontologies. Based on this a new approach is designed to retrieve more relevant and accurate documents by embedding a new Concept Similarity measure ConSim, after evaluating the user's knowledge based on the search query. The experimental results prove that after embedding the ConSim measure the retrieval results have been improved.

DANI-Boy: A Data Access and Navigation Interface for Biology

Event Abstract Back to Event DANI-Boy: A Data Access and Navigation Interface for Biology Martin Telefont1*, Shruti Muralidhar2, Renaud Richardet1 and Sean L. Hill2 1 École Polytechnique Fédérale de Lausanne, Blue Brain Project, Switzerland 2 École Polytechnique Fédérale de Lausanne, Brain Mind Institute, Switzerland Biomedical laboratories are producing ever increasing amounts of data, yet tools used to annotate and link the data are not flexible enough to accommodate rapidly evolving community standards and the demands of data integration, analysis and model workflows. Experimenters produce data and annotate parts of a project while performing data gathering and analysis of other part while performing annotation on others. Data integration tools used in the bio-medical domain currently do not provide a flexible enough framework to keep up with evolving ontologies and the daily reality of data generation. The increasing pressure to publish well annotated data and provide links to existing ontologies and data repositories creates the opportunity for a new set of tools. We present a prototype of a Data Access and Navigation Interface for Biology (DANI-Boy) which support the continuous integration and annotation data by allowing easy access to community developed ontologies while allowing enough flexibility to add information not yet standardized by a wider community. DANI-Boy was build on best data integration practices, allowing to build on existing resources, such as NeuroLex biomedical ontologies. Semantic Media Wiki (SMW) is used in the current version as a graphical user interface to facilitating the continuous refinement of data annotation and linking to ontologically defined entities. Python is used for various background processing tasks to facilitate information extraction, cross ontology matching and in providing an ontology anchored object API. The intent is to provide a more intuitive service model than the standard SPARQL services provided by SMW. The present prototype offers access to integrated experimental data gathered over the course of multiple years in the exploration of Layer I cells and microcircuitry of the neocortex of a P14 rat. The example implementation shows how morphological, electrophysiological, molecular and connectivity data can be organized, explored, annotated and updated. Keywords: Semantic Web technologies, ontologies, data integration, python, Neuroscience Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Demo Topic: Infrastructural and portal services Citation: Telefont M, Muralidhar S, Richardet R and Hill SL (2013). DANI-Boy: A Data Access and Navigation Interface for Biology. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00072 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Mr. Martin Telefont, École Polytechnique Fédérale de Lausanne, Blue Brain Project, Lausanne, Vaud, 1015, Switzerland, martin.telefont@epfl.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Martin Telefont Shruti Muralidhar Renaud Richardet Sean L Hill Google Martin Telefont Shruti Muralidhar Renaud Richardet Sean L Hill Google Scholar Martin Telefont Shruti Muralidhar Renaud Richardet Sean L Hill PubMed Martin Telefont Shruti Muralidhar Renaud Richardet Sean L Hill Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Cross-Ontology Multi-level Association Rule Mining in the Gene Ontology

The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.