Ontology Graph Research Articles

Numerical Data Clustering Algorithms in Mining Real Estate Listings

In the paper, we propose a method for mining real-estate listings using clustering algorithms intended for numerical data. The presented approach is based on information systems over ontological graphs. Such information systems have been proposed to deal with data in the form of concepts linked by different semantic relations. A special attention is focused on preprocessing steps transforming advertisements in the textual form into information systems defined over ontological graphs, as well as on encoding attribute values for clustering algorithms.

A shortcut for multiple testing on the directed acyclic graph of gene ontology.

BackgroundGene set testing has become an important analysis technique in high throughput microarray and next generation sequencing studies for uncovering patterns of differential expression of various biological processes. Often, the large number of gene sets that are tested simultaneously require some sort of multiplicity correction to account for the multiplicity effect. This work provides a substantial computational improvement to an existing familywise error rate controlling multiplicity approach (the Focus Level method) for gene set testing in high throughput microarray and next generation sequencing studies using Gene Ontology graphs, which we call the Short Focus Level.ResultsThe Short Focus Level procedure, which performs a shortcut of the full Focus Level procedure, is achieved by extending the reach of graphical weighted Bonferroni testing to closed testing situations where restricted hypotheses are present, such as in the Gene Ontology graphs. The Short Focus Level multiplicity adjustment can perform the full top-down approach of the original Focus Level procedure, overcoming a significant disadvantage of the otherwise powerful Focus Level multiplicity adjustment. The computational and power differences of the Short Focus Level procedure as compared to the original Focus Level procedure are demonstrated both through simulation and using real data.ConclusionsThe Short Focus Level procedure shows a significant increase in computation speed over the original Focus Level procedure (as much as ∼15,000 times faster). The Short Focus Level should be used in place of the Focus Level procedure whenever the logical assumptions of the Gene Ontology graph structure are appropriate for the study objectives and when either no a priori focus level of interest can be specified or the focus level is selected at a higher level of the graph, where the Focus Level procedure is computationally intractable.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0349-3) contains supplementary material, which is available to authorized users.

Extracting Decision Rules from Linguistic Data Describing Economic Phenomena. The Approach Based on Decision Systems over Ontological Graphs and PSO

The aim of the paper is to present a heuristic method for extracting the most general decision rules from linguistic data describing economic phenomena included in simple decision systems over ontological graphs. Such decision systems have been proposed to deal with linguistic attribute values, describing objects of interest, which are concepts placed in semantic spaces expressed by means of ontological graphs. Ontological graphs deliver some additional knowledge (the so-called background knowledge) about semantic relations between concepts which can be useful in classification processes. As heuristics, we propose to use Particle Swarm Optimization (PSO ), which is reported as a successful method in many applications.

Encoding symbolic features in simple decision systems over ontological graphs for PSO and neural network based classifiers

In the paper, we present the idea of encoding symbolic features appearing in simple decision systems over ontological graphs for building classifiers based on Particle Swarm Optimization (PSO) as well as Neural Networks . Simple decision systems over ontological graphs refer to a general trend in computations proposed by Zadeh and called “computing with words”. In case of such decision systems, we deal with attribute values, describing objects of interest, which are concepts placed in semantic spaces expressed by means of ontological graphs. Ontological graphs deliver us some additional knowledge which can be useful in classification processes. Symbolic data, in our approach in the form of concepts from ontologies, require special treatment to be used in classifiers based on searching for the numerical mapping functions between the known inputs and the corresponding known outputs. • Encoding symbolic features in decision systems over ontological graphs is shown. • Classifiers based on numerical mapping functions can be used for encoded data. • The idea is based on characteristic functions defined according to data semantics. • Encoding based on fuzzy characteristic function becomes a promising method.

Linear Statistical Analysis of Multi-dividing Ontology Algorithm

As a concept structured model, ontology has been widely used in various fields such as query expansion and image retrieval. The core trick of ontology applications is to calculate the similarity between the vertices in the ontology graph. Multi-dividing technology has been proved to be an effective approach, which maps the ontology graph into real line and determines the similarity between the different vertices according to the difference of real numbers they correspond to. In this paper, we study the multi-dividing ontology algorithm from a theoretical view. It is highlighted that empirical multi-dividing ontology model can be expressed as conditional linear statistical, and an approximation result is achieved based on projection method. Moreover, we determine the bound of algorithm error for W -ontology performance criteria, which we propose for multi-dividing ontology setting.

Piecewise function approximation and vertex partitioning schemes for multi-dividing ontology algorithm in AUC criterion setting (I)

Ontology is a useful tool with wide applications in various fields and attracts widespread attention of scholars, and ontology concept similarity calculation is an essential problem in these application algorithms. An effective method to get similarity between vertices on ontology is based on a function, which maps ontology graph into a line and maps each vertex in graph into a real-value, and the similarity is measured by the difference of their corresponding scores. The area under the receiver operating characteristics curve (AUC) criterion multi-dividing method is suitable for ontology problem. In this paper, we present piecewise constant function approximation approach for AUC criterion multi-dividing ontology algorithm and focus on vertex partitioning schemes. Using the techniques of statistical learning theory, theoretical characteristics of the approximation algorithm are provided with partitioning schemes, and a splitting rule is designed for vertex partitioning.

POEAS: Automated Plant Phenomic Analysis Using Plant Ontology

Biological enrichment analysis using gene ontology (GO) provides a global overview of the functional role of genes or proteins identified from large-scale genomic or proteomic experiments. Phenomic enrichment analysis of gene lists can provide an important layer of information as well as cellular components, molecular functions, and biological processes associated with gene lists. Plant phenomic enrichment analysis will be useful for performing new experiments to better understand plant systems and for the interpretation of gene or proteins identified from high-throughput experiments. Plant ontology (PO) is a compendium of terms to define the diverse phenotypic characteristics of plant species, including plant anatomy, morphology, and development stages. Adoption of this highly useful ontology is limited, when compared to GO, because of the lack of user-friendly tools that enable the use of PO for statistical enrichment analysis. To address this challenge, we introduce Plant Ontology Enrichment Analysis Server (POEAS) in the public domain. POEAS uses a simple list of genes as input data and performs enrichment analysis using Ontologizer 2.0 to provide results in two levels, enrichment results and visualization utilities, to generate ontological graphs that are of publication quality. POEAS also offers interactive options to identify user-defined background population sets, various multiple-testing correction methods, different enrichment calculation methods, and resampling tests to improve statistical significance. The availability of such a tool to perform phenomic enrichment analyses using plant genes as a complementary resource will permit the adoption of PO-based phenomic analysis as part of analytical workflows. POEAS can be accessed using the URL http://caps.ncbs.res.in/poeas.

Research of Composite Ontology Mapping Strategy on the Parsing Graph

According to complex context relation of ontology, considering different input schema, ontology graph is created with general environment. Based on ontology structure, the paper constructs multiple level graph representation, By introducing similarity propagation of structural and instance level on context relation and rapid mapping with a rapid matching algorithm, a composite ontology mapping strategy is proposed, which iteratively achieves ontology mapping result with reused idea. Finally feasibility and effectiveness of the strategy is proved by complexity analysis to algorithm and some contrast test.

A Framework to Defense against Insider Attacks on Information Sources

As for development and growth of information systems and security organizations, protecting information against probable attacks is of great importance. External raids on these organizations, for the most part, are not practicable due to high defensive layers. Therefore to intrude on such organizations, insiders are employed. In this paper, by introducing c onsequence and necessity of recognition of insider attacks perils, we intend to propose a new framework for detecting and preventing from insider attacks on information systems. The suggested framework is defined according to ontology graphs, thus, a struc ture of so-called ontology is firstly explained. This composition represents data structure for saving and presenting information and is then practiced to detect user’s behavioral patterns within such framework. The framework consists of three phases of construction, comparison and analysis such that it first receives a set of user’s requests alongside with his legal access level and in case of encountering an attack it communicates an appropriate warning message to the organization administrative system.

A Framework to Defense Against Insider Attacks on Information Sources

As for development and growth of information systems and security organizations, protecting information against probable attacks is of great importance. External raids on these organizations, for the most part, are not practicable due to high defensive layers. Therefore to intrude on such organizations, insiders are employed. In this paper, by introducing consequence and necessity of recognition of insider attacks perils, we intend to propose a new framework for detecting and preventing from insider attacks on information systems. The suggested framework is defined according to ontology graphs, thus, a structure of so-called ontology is firstly explained. This composition represents data structure for saving and presenting information and is then practiced to detect user’s behavioral patterns within such framework. The framework consists of three phases of construction, comparison and analysis such that it first receives a set of user’s requests alongside with his legal access level and in case of encountering an attack it communicates an appropriate warning message to the organization administrative system.

A New Method for Knowledge and Information Management Domain Ontology Graph Model

A new ontology learning model called domain ontology graph (DOG) is proposed in this paper. There are two key components in the DOG, i.e., the definition of the ontology graph and the ontology learning process. The former defines the ontology and knowledge conceptualization model from the domain-specific text documents; the latter offers the necessary method of semiautomatic domain ontology learning and generates the corresponding ontology graphs. Two kinds of ontological operations are also defined based on the proposed DOG, i.e., document ontology graph generation and ontology-graph-based text classification. The simulation studies focused upon Chinese text data are used to demonstrate the potential effectiveness of our proposed strategy. This is accomplished by generating DOGs to represent the domain knowledge and conducting the text classifications based on the generated ontology graph. The experimental results show that the new method can produce significantly better classification accuracy (e.g., with 92.3% in f-measure) compared with other methods (such as 86.8% in f-measure for the term-frequency-inverse-document-frequency approach). The high performance demonstrates that our presented ontological operations based on the ontology graph knowledge model are effectively developed.

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.

Semantic annotations similarity measure to compare processes profiles

Improved method to calculate similarity measure for semantic ontological graph annotations is introduced in the article. Nodes of such a graph are concepts of problem area ontology and edges – relations among them. The method proposed can be used to improve synthesis of structural procedural program schemas based on semantic computing networks. Improved synthesis method takes into account not only computation relations among network components but their semantic annotation, that formalizes the aim of the software component. By integrating such semantic annotations ontology into top level OWL-S ontology one can use introduced metric to discover or recommend web-service by their detailed profile and user’s request

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.

Credit Card Fraud Detection Based on Ontology Graph

Credit Card Fraud Detection Based on Ontology Graph

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.

OLSVis: an animated, interactive visual browser for bio-ontologies

BackgroundMore than one million terms from biomedical ontologies and controlled vocabularies are available through the Ontology Lookup Service (OLS). Although OLS provides ample possibility for querying and browsing terms, the visualization of parts of the ontology graphs is rather limited and inflexible.ResultsWe created the OLSVis web application, a visualiser for browsing all ontologies available in the OLS database. OLSVis shows customisable subgraphs of the OLS ontologies. Subgraphs are animated via a real-time force-based layout algorithm which is fully interactive: each time the user makes a change, e.g. browsing to a new term, hiding, adding, or dragging terms, the algorithm performs smooth and only essential reorganisations of the graph. This assures an optimal viewing experience, because subsequent screen layouts are not grossly altered, and users can easily navigate through the graph. URL: http://ols.wordvis.comConclusionsThe OLSVis web application provides a user-friendly tool to visualise ontologies from the OLS repository. It broadens the possibilities to investigate and select ontology subgraphs through a smooth visualisation method.

An efficient concept-based retrieval model for enhancing text retrieval quality

Most of the common techniques in text retrieval are based on the statistical analysis terms (words or phrases). Statistical analysis of term frequency captures the importance of the term within a document only. Thus, to achieve a more accurate analysis, the underlying model should indicate terms that capture the semantics of text. In this case, the model can capture terms that represent the concepts of the sentence, which leads to discovering the topic of the document. In this paper, a new concept-based retrieval model is introduced. The proposed concept-based retrieval model consists of conceptual ontological graph (COG) representation and concept-based weighting scheme. The COG representation captures the semantic structure of each term within a sentence. Then, all the terms are placed in the COG representation according to their contribution to the meaning of the sentence. The concept-based weighting analyzes terms at the sentence and document levels. This is different from the classical approach of analyzing terms at the document level only. The weighted terms are then ranked, and the top concepts are used to build a concept-based document index for text retrieval. The concept-based retrieval model can effectively discriminate between unimportant terms with respect to sentence semantics and terms which represent the concepts that capture the sentence meaning. Experiments using the proposed concept-based retrieval model on different data sets in text retrieval are conducted. The experiments provide comparison between traditional approaches and the concept-based retrieval model obtained by the combined approach of the conceptual ontological graph and the concept-based weighting scheme. The evaluation of results is performed using three quality measures, the preference measure (bpref), precision at 10 documents retrieved (P(10)) and the mean uninterpolated average precision (MAP). All of these quality measures are improved when the newly developed concept-based retrieval model is used, confirming that such model enhances the quality of text retrieval.

ApiNATOMY: A novel toolkit for visualizing multiscale anatomy schematics with phenotype-related information

A significant proportion of biomedical resources carries information that cross references to anatomical structures across multiple scales. To improve the visualization of such resources in their anatomical context, we developed an automated methodology that produces anatomy schematics in a consistent manner,and provides for the overlay of anatomy-related resource information onto the same diagram. This methodology, called ApiNATOMY, draws upon the topology of ontology graphs to automatically lay out treemaps representing body parts as well as semantic metadata linking to such ontologies. More generally, ApiNATOMY treemaps provide an efficient and manageable way to visualize large biomedical ontologies in a meaningful and consistent manner. In the anatomy domain, such treemaps will allow epidemiologists, clinicians, and biomedical scientists to review, and interact with, anatomically aggregated heterogeneous data and model resources. Such an approach supports the visual identification of functional relations between anatomically colocalized resources that may not be immediately amenable to automation by ontology-based inferencing. We also describe the application of ApiNATOMY schematics to integrate, and add value to, human phenotype-related information—results are found at http://apinatomy.org. The long-term goal for the ApiNATOMY toolkit is to support clinical and scientific graphical user interfaces and dashboards for biomedical resource management and data analytics.

Semantically enabling a genome-wide association study database

BackgroundThe amount of data generated from genome-wide association studies (GWAS) has grown rapidly, but considerations for GWAS phenotype data reuse and interchange have not kept pace. This impacts on the work of GWAS Central – a free and open access resource for the advanced querying and comparison of summary-level genetic association data. The benefits of employing ontologies for standardising and structuring data are widely accepted. The complex spectrum of observed human phenotypes (and traits), and the requirement for cross-species phenotype comparisons, calls for reflection on the most appropriate solution for the organisation of human phenotype data. The Semantic Web provides standards for the possibility of further integration of GWAS data and the ability to contribute to the web of Linked Data.ResultsA pragmatic consideration when applying phenotype ontologies to GWAS data is the ability to retrieve all data, at the most granular level possible, from querying a single ontology graph. We found the Medical Subject Headings (MeSH) terminology suitable for describing all traits (diseases and medical signs and symptoms) at various levels of granularity and the Human Phenotype Ontology (HPO) most suitable for describing phenotypic abnormalities (medical signs and symptoms) at the most granular level. Diseases within MeSH are mapped to HPO to infer the phenotypic abnormalities associated with diseases. Building on the rich semantic phenotype annotation layer, we are able to make cross-species phenotype comparisons and publish a core subset of GWAS data as RDF nanopublications.ConclusionsWe present a methodology for applying phenotype annotations to a comprehensive genome-wide association dataset and for ensuring compatibility with the Semantic Web. The annotations are used to assist with cross-species genotype and phenotype comparisons. However, further processing and deconstructions of terms may be required to facilitate automatic phenotype comparisons. The provision of GWAS nanopublications enables a new dimension for exploring GWAS data, by way of intrinsic links to related data resources within the Linked Data web. The value of such annotation and integration will grow as more biomedical resources adopt the standards of the Semantic Web.