Ontology Similarity Research Articles

Film and History

How many vague descriptions we will abandon the day a class can watch, projected on precise and moving images, the calm or troubled faces of a deliberating assembly, the meeting of chiefs of state ready to sign an alliance, the departure of troops and squadrons, or even the mobile and changing physiognomy of cities. (Matuszewski, 1997:1) The enthusiasm evinced by Boleslas Matuszewski about moving pictures as the basis for a vast historical image archive, dates back to the very early years of film’s history. Although as an unemployed cameraman Matuszewski may have had self-interested reasons for promoting film, his 1898 description anticipates what has become a common-place notion about the power of film. Rather than the vague descriptions fostered by the written word, film would show rather than tell. Underlying this attitude is a naive faith that photographic images are somehow less ambiguous and more direct than words because they are indexical and thus bear a literal trace of a person or object that was present in the world. In that way, photography and film are thought not to represent but to bear witness. As a result, they fulfill the dream of every historian – to somehow be present in the past. The last hundred years have also produced much discussion aimed at unsettling just such easy notions about the “directness” of the photographic image. This perspective, by contrast, suggests that showing can be just as ambiguous as telling. Few forms of narrative and documentation have simultaneously stirred as much utopian fantasy and eye-rolling dismissal as history on film. There are so many dimensions to any consideration of the connection between Film and History that to attempt to analyze this vexed relation is, in the end, to discuss several different and not always over-lapping issues. Yet where film and history themselves overlap, it is because of an ontological similarity between them. After all, both film and history claim to bear a reference or relation to the real world in somewhat literal ways; both are also fundamentally concerned with issues of temporality. History and film can be thought to share the common project of presenting us, as Phil Rosen put it, with “an absence, namely that of the represented past” (Rosen, 1984: 31). But if there is an ontological relation, such questions have largely escaped the majority of historians who are more interested in the stories they tell than in the ways they tell stories. But rather than stress the disciplinary divisions as the fundamental divide in considerations of film and history, this essay maps four areas of intersection between film and history that transcend mere distinction in field of study. Just as this essay suggests it is time to move the discussion away from who has the “right” to adjudicate representations of the past, it also suggests that anyone interested in the intersection

Stability Analysis of Learning Algorithms for Ontology Similarity Computation

Ontology, as a useful tool, is widely applied in lots of areas such as social science, computer science, and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A recent approach is to make use of similarity between vertices on ontology graphs. It is, instead of pairwise computations, based on a function that maps the vertex set of an ontology graph to real numbers. In order to obtain this, the ranking learning problem plays an important and essential role, especiallyk-partite ranking algorithm, which is suitable for solving some ontology problems. A ranking function is usually used to map the vertices of an ontology graph to numbers and assign ranks of the vertices through their scores. Through studying a training sample, such a function can be learned. It contains a subset of vertices of the ontology graph. A good ranking function means small ranking mistakes and good stability. For ranking algorithms, which are in a well-stable state, we study generalization bounds via some concepts of algorithmic stability. We also find that kernel-based ranking algorithms stated as regularization schemes in reproducing kernel Hilbert spaces satisfy stability conditions and have great generalization abilities.

Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term's specificity in the GO DAG.

An Improved Approach for Ontology Similarity Computation

An Improved Approach for Ontology Similarity Computation

Characteristics of Optimal Function for Ontology Similarity Measure via Multi-dividing

As a powerful tool, ontology has been widely applied in social science, medicine science and computer science. In computer networks, especially, ontology is used for search extension, thus boost the quality of information retrieval. Ontology concept similarity calculation is an essential problem in these applications. A new method to get similarity between vertices on ontology graph is by machine learning, and multi-dividing algorithm is suitable for ontology problem. It is usually get an ontology function which maps the vertices of ontology graph to real numbers. Such function is given by learning a training sample which contains a subset of vertices of ontology graph. In this paper, we study the properties of best ontology function for this method. Some results under different loss functions are given.

The Development of a Product Ontology Information System for Similarity Measurement

Recently, the amount of multimedia content is increasing considerably. Several concurrent trends made this happen. Current software made creating, sharing, and editing of multimedia resources a common and easy task. In this paper we present a formal and implemented generic framework for building ontology-based information systems. The recall and precise of product ontology similarity measurement were higher than that of the traditional similarity measurement. In order to address this research issue, an ontology-based multi criteria decision model is proposed. The experimental results indicate that this method has great promise.

Complex-disease networks of trait-associated single-nucleotide polymorphisms (SNPs) unveiled by information theory

ObjectiveThousands of complex-disease single-nucleotide polymorphisms (SNPs) have been discovered in genome-wide association studies (GWAS). However, these intragenic SNPs have not been collectively mined to unveil the genetic architecture between complex clinical traits. The authors hypothesize that biological annotations of host genes of trait-associated SNPs may reveal the biomolecular modularity across complex-disease traits and offer insights for drug repositioning.MethodsTrait-to-polymorphism (SNPs) associations confirmed in GWAS were used. A novel method to quantify trait–trait similarity anchored in Gene Ontology annotations of human proteins and information theory was developed. The results were then validated with the shortest paths of physical protein interactions between biologically similar traits.ResultsA network was constructed consisting of 280 significant intertrait similarities among 177 disease traits, which covered 1438 well-validated disease-associated SNPs. Thirty-nine percent of intertrait connections were confirmed by curators, and the following additional studies demonstrated the validity of a proportion of the remainder. On a phenotypic trait level, higher Gene Ontology similarity between proteins correlated with smaller ‘shortest distance’ in protein interaction networks of complexly inherited diseases (Spearman p<2.2×10−16). Further, ‘cancer traits’ were similar to one another, as were ‘metabolic syndrome traits’ (Fisher's exact test p=0.001 and 3.5×10−7, respectively).ConclusionAn imputed disease network by information-anchored functional similarity from GWAS trait-associated SNPs is reported. It is also demonstrated that small shortest paths of protein interactions correlate with complex-disease function. Taken together, these findings provide the framework for investigating drug targets with unbiased functional biomolecular networks rather than worn-out single-gene and subjective canonical pathway approaches.

Ontology Similarity Measure and Ontology Mapping via Learning Optimization SimilarityFunction

Ontology similarity calculation and ontology mapping are important research topics in information retrieval. By learning optimization similarity function, we propose the new algorithm for ontology similarity measure and ontology mapping. The stability of ontology algorithms is studied by adopting a strategy which adjusts the sample set by deleting one or two element from it. Relationship between uniform loss stability and uniform score stability is investigated. A sufficient condition for uniform score stability is given. The result of our work shows that if for any (v,v') ∈ V × V, a kernel function K((v,v'), (v,v')) has a limited upper bound, then the ontology algorithm which minimizes the regularization empirical l-error will have good uniform score stability. Also, two experiments results show that the proposed algorithm has high accuracy and efficiency for similarity calculation and ontology mapping. Index Terms—Ontology, similarity calculation, ontology mapping.

Decision support in e-business based on assessing similarities between ontologies

As ontology usage becomes more prevalent in e-business decision support systems, it is essential to establish interoperation between agents or web services with different ontologies. Ontology matching represents a promising technique in information integration, agent communication and web services. Here, an ontology similarity assessment algorithm is proposed to map concepts and properties between ontologies of semantic web services. It has the two phases (1) syntactic analysis based on string tokenisation, and (2) semantic analysis employing the WordNet thesaurus and tree-structured graphs with the Tversky model. Promising experimental results indicate that this algorithm can find matching results with relatively high precision.

Exact score distribution computation for ontological similarity searches.

BackgroundSemantic similarity searches in ontologies are an important component of many bioinformatic algorithms, e.g., finding functionally related proteins with the Gene Ontology or phenotypically similar diseases with the Human Phenotype Ontology (HPO). We have recently shown that the performance of semantic similarity searches can be improved by ranking results according to the probability of obtaining a given score at random rather than by the scores themselves. However, to date, there are no algorithms for computing the exact distribution of semantic similarity scores, which is necessary for computing the exact P-value of a given score.ResultsIn this paper we consider the exact computation of score distributions for similarity searches in ontologies, and introduce a simple null hypothesis which can be used to compute a P-value for the statistical significance of similarity scores. We concentrate on measures based on Resnik's definition of ontological similarity. A new algorithm is proposed that collapses subgraphs of the ontology graph and thereby allows fast score distribution computation. The new algorithm is several orders of magnitude faster than the naive approach, as we demonstrate by computing score distributions for similarity searches in the HPO. It is shown that exact P-value calculation improves clinical diagnosis using the HPO compared to approaches based on sampling.ConclusionsThe new algorithm enables for the first time exact P-value calculation via exact score distribution computation for ontology similarity searches. The approach is applicable to any ontology for which the annotation-propagation rule holds and can improve any bioinformatic method that makes only use of the raw similarity scores. The algorithm was implemented in Java, supports any ontology in OBO format, and is available for non-commercial and academic usage under: https://compbio.charite.de/svn/hpo/trunk/src/tools/significance/

Gene Ontology-driven inference of protein–protein interactions using inducers

Protein-protein interactions (PPIs) are pivotal for many biological processes and similarity in Gene Ontology (GO) annotation has been found to be one of the strongest indicators for PPI. Most GO-driven algorithms for PPI inference combine machine learning and semantic similarity techniques. We introduce the concept of inducers as a method to integrate both approaches more effectively, leading to superior prediction accuracies. An inducer (ULCA) in combination with a Random Forest classifier compares favorably to several sequence-based methods, semantic similarity measures and multi-kernel approaches. On a newly created set of high-quality interaction data, the proposed method achieves high cross-species prediction accuracies (Area under the ROC curve ≤ 0.88), rendering it a valuable companion to sequence-based methods. Software and datasets are available at http://bioinformatics.org.au/go2ppi/ m.ragan@uq.edu.au.

An Ontology Learning Method Based on Document Clustering

Ontology learning is a series method and technology of semi-automatic ontology construction, which uses various data sources to create or expand in-built ontology by semi-automatic method to build a new ontology. Existing ontology construction methods are to collect a large number of conceptual terms based on a large number of field text and background corpus, and then to select field concepts to construct a body. The proposed Cluster-Merge algorithm is to use k-means clustering algorithm in the field document at first, then according to document clustering results to construct body by themself, at last accoring to the ontology similarity for ontology merging to get final output ontology. The experiment may prove that Cluster-Merge algorithm can improve the body resulting recall and precision.

Research on domain ontology in different granulations based on concept lattice

This paper introduces concept lattice and granular computing into ontology learning, and presents a unified research model for ontology building, ontology merging and ontology connection based on the domain ontology base in different granulations. In this model, as the knowledge in the lowest and most basic level, the domain ontology base is presented firstly, which provides a uniform technology for ontology learning on the whole; secondly, in order to better understand problems rather than be overwhelmed unnecessary details, granular computing is introduced to abstract and simplify domain ontology bases in complex domains. Moreover, the similarly of concepts in different granulations is introduced to help domain experts judging relations except for inheritance relation, and the similarity of ontologies in multi-granulations is introduced to measure the degree of connection of ontologies; finally, based on similarity models mentioned above, the ontology building, ontology merging and ontology connection can be obtained in different granulations with the help of domain experts. It is shown by instances that the application of the model presented in this paper is valid and practicable. Although there are still some problems in applications of this model (for example, ontology learning cannot dispense with the intervention of domain experts yet), this paper offers a new way for combining ontology learning and concept lattice.

Research of the Design and Application of the Negotiation Service Management Agent

The Negotiation Service Management Agent (NSMA) is the directorial proxy of negotiation service in the E-Commerce negotiation platform based on Service-Oriented Architecture, and takes the tasks of acceptance the registration of the negotiation web services, startup the negotiation services and the message communication management among the negotiators in the negotiation procedure. This paper presented a SOA Negotiation platform with the kernel part of NSMA, designed and implemented the main algorithm of the negotiation request response, the negotiation ontology similarity computation and the negotiation services invoker in the NSMA. The results validated the efficiency of the NSMA in the negotiation procedure.

Using ontological and document similarity to estimate museum exhibit relatedness

Exhibits within cultural heritage collections such as museums and art galleries are arranged by experts with intimate knowledge of the domain, but there may exist connections between individual exhibits that are not evident in this representation. For example, the visitors to such a space may have their own opinions on how exhibits relate to one another. In this article, we explore the possibility of estimating the perceived relatedness of exhibits by museum visitors through a variety of ontological and document similarity-based methods. Specifically, we combine the Wikipedia category hierarchy with lexical similarity measures, and evaluate the correlation with the relatedness judgements of visitors. We compare our measure with simple document similarity calculations, based on either Wikipedia documents or Web pages taken from the Web site for the museum of interest. We also investigate the hypothesis that physical distance in the museum space is a direct representation of the conceptual distance between exhibits. We demonstrate that ontological similarity measures are highly effective at capturing perceived relatedness and that the proposed RACO (Related Article Conceptual Overlap) method is able to achieve results closest to relatedness judgements provided by human annotators compared to existing state-of-the art measures of semantic relatedness.

Ontology Similarity Measure and Ontology Mapping Via Fast Ranking Method

Ontology Similarity Measure and Ontology Mapping Via Fast Ranking Method

SNPRanker: a tool for identification and scoring of SNPs associated to target genes

Summary The identification of genes and SNPs involved in human diseases remains a challenge. Many public resources, databases and applications, collect biological data and perform annotations, increasing the global biological knowledge. The need of SNPs prioritization is emerging with the development of new high-throughput genotyping technologies, which allow to develop customized disease-oriented chips. Therefore, given a list of genes related to a specific biological process or disease as input, a crucial issue is finding the most relevant SNPs to analyse. The selection of these SNPs may rely on the relevant a-priori knowledge of biomolecular features characterising all the annotated SNPs and genes of the provided list. The bioinformatics approach described here allows to retrieve a ranked list of significant SNPs from a set of input genes, such as candidate genes associated with a specific disease. The system enriches the genes set by including other genes, associated to the original ones by ontological similarity evaluation. The proposed method relies on the integration of data from public resources in a vertical perspective (from genomics to systems biology data), the evaluation of features from biomolecular knowledge, the computation of partial scores for SNPs and finally their ranking, relying on their global score. Our approach has been implemented into a web based tool called SNPRanker, which is accessible through at the URL http://www.itb.cnr.it/snpranker. An interesting application of the presented system is the prioritisation of SNPs related to genes involved in specific pathologies, in order to produce custom arrays.

Measuring Concept Similarity of Heterogeneous Ontologies in Multi-angent System

Different kinds of agents in a multi-agent system have different knowledge structure, which results in difficulties of interaction and coordination among agents. At present, ontology based knowledge representation is an effective way of resolving such difficulties, the key of which is heterogeneous ontology concept similarity measuring. In this paper, we designed an integrated heterogeneous ontology concept similarity measuring method. Experiments are conducted to demonstrate its feasibility and effectiveness.

A yeast's eye view of mammalian reproduction: cross-species gene co-expression in meiotic prophase

BackgroundMeiotic prophase is a critical stage in sexual reproduction. Aberrant chromosome recombination during this stage is a leading cause of human miscarriages and birth defects. However, due to the experimental intractability of mammalian gonads, only a very limited number of meiotic genes have been characterized. Here we aim to identify novel meiotic genes important in human reproduction through computational mining of cross-species and cross-sex time-series expression data from budding yeast, mouse postnatal testis, mouse embryonic ovary, and human fetal ovary.ResultsOrthologous gene pairs were ranked by order statistics according to their co-expression profiles across species, allowing us to infer conserved meiotic genes despite obvious differences in cellular synchronicity and composition in organisms. We demonstrated that conserved co-expression networks could successfully recover known meiotic genes, including homologous recombination genes, chromatin cohesion genes, and genes regulating meiotic entry. We also showed that conserved co-expression pairs exhibit functional connections, as evidenced by the annotation similarity in Gene Ontology and overlap with physical interactions. More importantly, we predicted six new meiotic genes through their co-expression linkages with known meiotic genes, and subsequently used the genetically more amenable yeast system for experimental validation. The deletion mutants of all six genes showed sporulation defects, equivalent to a 100% validation rate.ConclusionsWe identified evolutionarily conserved gene modules in meiotic prophase by integrating cross-species and cross-sex expression profiles from budding yeast, mouse, and human. Our co-expression linkage analyses confirmed known meiotic genes and identified several novel genes that might be critical players in meiosis in multiple species. These results demonstrate that our approach is highly efficient to discover evolutionarily conserved novel meiotic genes, and yeast can serve as a valuable model system for investigating mammalian meiotic prophase.

Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

BackgroundA new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance.ResultsHere we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria). The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted.ConclusionThe analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks.