Ontology Graph Research Articles

A substrate-based ontology for human solute carriers.

Solute carriers (SLCs) are the largest family of transmembrane transporters in the human genome with more than 400 members. Despite the fact that SLCs mediate critical biological functions and several are important pharmacological targets, a large proportion of them is poorly characterized and present no assigned substrate. A major limitation to systems‐level de‐orphanization campaigns is the absence of a structured, language‐controlled chemical annotation. Here we describe a thorough manual annotation of SLCs based on literature. The annotation of substrates, transport mechanism, coupled ions, and subcellular localization for 446 human SLCs confirmed that ~30% of these were still functionally orphan and lacked known substrates. Application of a substrate‐based ontology to transcriptomic datasets identified SLC‐specific responses to external perturbations, while a machine‐learning approach based on the annotation allowed us to identify potential substrates for several orphan SLCs. The annotation is available at https://opendata.cemm.at/gsflab/slcontology/. Given the increasing availability of large biological datasets and the growing interest in transporters, we expect that the effort presented here will be critical to provide novel insights into the functions of SLCs.

Mapping ontology vertices to a line using hypergraph framework

As a conceptual semantic tool, ontology is widely used in many disciplines such as genetics, nutrition, and social sciences. The key issues for ontology applications are similarity calculations and ontology alignment. In recent years, various machine learning methods and computational models have been widely used in ontology optimization and computation. The core idea is to map the entire ontology graph into one-dimensional data, such as on a real axis or on a natural number set. Through the analysis of the previous multi-dividing ontology algorithm, the technique of achieving dimensionality reduction comes from the pairwise comparison of the ontology sample vertices. The weakness of such tricks is that only two ontology vertices can be extracted for comparison at a time, which causes the number of vertex pairs to be compared in the optimization model to become very large as the totally ontology sample size increases. This paper proposes a new class of ontology learning strategies, which aims to arrange the ontology concepts into one-dimensional data according to the sequence of natural numbers. The ontology optimization model does not compare two ontology vertices, but compares a set of ontology vertices and calculates the weight of each vertex by means of random walk calculating. Each set of compared ontology vertices constitutes a hyperedge, and thus the ontology sample sets and the computational framework are represented by hypergraph and its associated bipartite graph. The algorithm proposed in this paper has potential guiding significance and theoretical value for engineering applications. In addition, two examples are presented to illustrate that our hypergraph based ontology learning algorithm is effective for a specific application background.

XAI LANGUAGE TUTOR - A XAI-BASED LANGUAGE LEARNING CHATBOT USING ONTOLOGY AND TRANSFER LEARNING TECHNIQUES

In this paper, we proposed a XAI-based Language Learning Chatbot (namely XAI Language Tutor) by using ontology and transfer learning techniques. To facilitate three levels of language learning, XAI Language Tutor consists of three levels for systematically English learning, which includes: 1) phonetics level for speech recognition and pronunciation correction; 2) semantic level for specific domain conversation, and 3) simulation of “free-style conversation” in English - the highest level of language chatbot communication as “free-style conversation agent”. In terms of academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model. From implementation perspective, our XAI Language Tutor agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph.

Automated generation of gene summaries at the Alliance of Genome Resources.

Short paragraphs that describe gene function, referred to as gene summaries, are valued by users of biological knowledgebases for the ease with which they convey key aspects of gene function. Manual curation of gene summaries, while desirable, is difficult for knowledgebases to sustain. We developed an algorithm that uses curated, structured gene data at the Alliance of Genome Resources (Alliance; www.alliancegenome.org) to automatically generate gene summaries that simulate natural language. The gene data used for this purpose include curated associations (annotations) to ontology terms from the Gene Ontology, Disease Ontology, model organism knowledgebase (MOK)-specific anatomy ontologies and Alliance orthology data. The method uses sentence templates for each data category included in the gene summary in order to build a natural language sentence from the list of terms associated with each gene. To improve readability of the summaries when numerous gene annotations are present, we developed a new algorithm that traverses ontology graphs in order to group terms by their common ancestors. The algorithm optimizes the coverage of the initial set of terms and limits the length of the final summary, using measures of information content of each ontology term as a criterion for inclusion in the summary. The automated gene summaries are generated with each Alliance release, ensuring that they reflect current data at the Alliance. Our method effectively leverages category-specific curation efforts of the Alliance member databases to create modular, structured and standardized gene summaries for seven member species of the Alliance. These automatically generated gene summaries make cross-species gene function comparisons tenable and increase discoverability of potential models of human disease. In addition to being displayed on Alliance gene pages, these summaries are also included on several MOK gene pages.

Triclustering of gene expression microarray data using coarse grained and dynamic deme based parallel genetic approach

Interpretation of microarray data is often crucial in various aspects of computational analysis. Dozens of techniques like clustering, subspace-clustering are often applied to these datasets to pluck-out significant results that provide solutions to drug discovery, disease identification like practical healthcare problems. Clustering techniques are used to group the genes exhibiting similar behavior under particular conditions. This classical method fails while the grouping of genes is done according to a subset of conditions as it performs globally. Biclustering addresses successfully this issue having constraints for evaluation of gene grouping only under a subset of the conditions. However, one of the limitations of the biclustering technique is, it is not capable to analyze the longitudinal experiments which consider different time points for the analysis of the gene expression profiles under a subset of conditions. This affair motivates to adopt triclustering on gene expression microarray data. Triclustering usually finds a set of genes of similar behaviors under a subset of conditions under certain time points. In this research article, two new frameworks based on different versions of parallel genetic algorithms are proposed to detect significant triclusters in gene expression profiles. In the first framework, the proposed algorithm is based on Coarse Grained parallel genetic approach and in the second framework the proposed algorithm is based on the Dynamic Deme parallel genetic approach. Both of them consider the experimental conditions and along with the time points with an advantage of paralleling the process and reducing the computational time. The proposed frameworks are tested on a standard yeast cell cycle(Saccharomyces cerevisiae) dataset and on its different synthetic versions which are widely used in the gene expression analysis. The performance analysis is done with respect to the aspects like the convergence speed of both the algorithms for different input sizes and with respect to the computation time. The statistical analysis is performed followed by the biological relevance of the simulation results is established with their functional annotations derived from the Gene Ontology and KEGG pathway analysis graph. The performance of the proposed frameworks demonstrate its effectiveness with the other state-of-the-art schemes. Experimental results reveal that the proposed architectures are efficient as they consume less computational time due to their inherent parallel behavior. Finally, the suggested architectures are considered as reliable frameworks and can be preferable over the traditional genetic approaches to analyze the gene expression microarray data from the triclustering prospective.

A FRAMEWORK FOR ONTOLOGY- BASED DIABETES DIAGNOSIS USING BAYELSIAN OPTIMIZATION TECHNIQUE

Diabetes Management System (DMS) is a computer-based system which aid physicians in properly diagnosing diabetes mellitus disease in patients. The DMS is essential in making individuals who have diabetes aware of their state and type. Existing approaches employed have not been efficient in considering all the diabetes type as well as making full prescription to diabetes patients. In this paper, a framework for an improved Ontology-based Diabetes Management System with a Bayesian optimization technique is presented. This helped in managing the diagnosis of diabetes and the prescription of treatment and drug to patients using the ontology knowledge management. The framework was implemented using Java programming language on Netbeans IDE, Protégé 4.2 and mysql. An extract of the ontology graph and acyclic probability graph was shown. The result showed that the nature of Bayesian network which has to do with statistical calculations based on equations, functions and sample frequencies led to more precise and reliable outcome.
 

Knowledge-Powered Inference of Crowd Behaviors in Semantically Rich Environments

Interactive authoring of collaborative, context-dependent virtual agent behaviors can be challenging. Current approaches often rely heavily on users’ input, leading to cumbersome behavior authoring experiences and biased results, which do not reflect realistic space-people interactions in virtual settings. To address these issues, we generate an ontology graph from commonsense knowledge corpus and use it to automatically infer behavior distributions that determine agents’ context-dependent interactions with the built environment. By means of a natural-language interface, users can interactively refine a building’s design by adding semantic labels to spaces and populating rooms with equipment following suggestions that the system provides based on commonsense knowledge. Based on the chosen setup, an authoring system automatically populates the environment and allocates agents to specific behaviors while satisfying a behavior distribution inferred from the ontology graph. This approach holds promise to help architects, engineers, and game designers interactively author plausible agent behaviors that reveal the mutual interactions between people and the spaces they inhabit.

An Ontological Graph Identification Method for Improving Localization of IP Prefix Hijacking in Network Systems

IP prefix hijacking continues to be a pervasive cyber security threat to the core internet routing infrastructure. The data security of multiple cloud-based services is also susceptible to these threats, due to the high dependency on traditional routing protocols. Although a number of hijacking detection techniques have been recently proposed, no existing system has effectively addressed the problem of detecting malicious transit Autonomous System (AS) services in any detected hijacking occurrences. The ability to locate and isolate malicious services is critical for conducting a necessary mitigation strategy at an early stage, to minimise the impact of the attack, to restore cloud services quickly. In this paper, we propose an effective real-time processing method, so-called Ontological Graph Identification (OGI), for detecting IP prefix hijacking of nodes and suspicious transit nodes caused by the hijacked nodes through ASs. The proposed method is evaluated using the two public datasets of RIPE RIS and RouteView. Experimental results revealed improved performance for the detection of malicious transit nodes compared with peer techniques. It is, therefore, shown that the proposed method has utility in automating the process of investigating nodes with suspicious activities in real network systems.

Ontology-Based Design of the Learner’s Knowledge Domain in Electrical Engineering

Abstract This research addresses some personalization aspects of education in electrical engineering. Its goal is to help students and educators evaluate the complexity of the disciplines they have chosen for studying and optimize the order of the learned courses and topics. A new instrument, namely, an educational thesaurus, is presented and its assembling procedure is shown. The offered educational thesauri implemented in the labs and integrated in the exercises have become smart platforms suitable for design and management of the students’ individual knowledge domains. The ontology-based Web manuals in Electronics and Power Electronics for the Bachelor study cycle have been introduced. An example of ontology graph to be applied within the Master study cycle has been developed and explained in the paper. According to the authors’ investigation, the decrease of stress caused by the new educational environment and achievement of success in learning were observed thanks to the individual knowledge domain organization proposed in this study.

Advances in gene ontology utilization improve statistical power of annotation enrichment.

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission or modification lest erroneous term mappings occur. To address these issues, we created the Gene Ontology Categorization Suite, or GOcats—a novel tool that organizes the Gene Ontology into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods. Specifically, we show that GOcats’ unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value = 1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats’ path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcat’s path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44.

Semantic Search Tools Based on Ontological Representations of Documentary Information

Ontological means of identification and representation of text documents semantics in relation to the problems of interactive information retrieval are considered. The implementations of ontologies operations are presented, which allow forming images of new meanings in the subject area. Taxonomies of relations (paradigmatic and syntagmatic) and entities (polythematic thesaurus of concepts) are used to perform operations, as well as to identify fuzzy relationships (in the case when entities and relationships are specified at different levels of generality and/or expressed by different linguistic constructions). Interactive tools are proposed that use the operation of constructing aspect projections for graph representations of ontologies, which make it possible to reduce the dimension of the graph to a level acceptable from the point of view of display and perception. The possibilities of context-based use of entities and relationships for the development of the search process are considered. This paper discuses the use of ontology graphs of organizational processes as a “navigation map” that provides new entry points in the graphical interface, that is, objects that will be contextually defined patterns of search queries in similarity selection tasks or expert analysis tasks. This allows one not only to increase the completeness and accuracy of information retrieval, but also to make the scheme of search navigation more natural, bringing it closer to the schemes of understanding and synthesis of knowledge.

Ontology-based entity matching in attributed graphs

Keys for graphs incorporate the topology and value constraints needed to uniquely identify entities in a graph. They have been studied to support object identification, knowledge fusion, and social network reconciliation. Existing key constraints identify entities as the matches of a graph pattern by subgraph isomorphism, which enforce label equality on node types. These constraints can be too restrictive to characterize structures and node labels that are syntactically different but semantically equivalent. We propose a new class of key constraints, Ontological Graph Keys (OGKs) that extend conventional graph keys by ontological subgraph matching between entity labels and an external ontology. We show that the implication and validation problems for OGKs are each NP-complete. To reduce the entity matching cost, we also provide an algorithm to compute a minimal cover for OGKs. We then study the entity matching problem with OGKs, and a practical variant with a budget on the matching cost. We develop efficient algorithms to perform entity matching based on a (budgeted) Chase procedure. Using real-world graphs, we experimentally verify the efficiency and accuracy of OGK-based entity matching.

Hybrid user action prediction system for automated home using association rules and ontology

Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle. Moreover, the evolution of the internet of things has introduced new insights into smart platforms and devices that leads to the new vision of ‘smart homes’. The idea of smart homes is not a recent concept; it has been in high interest by both academia and industry to make smart homes a more convenient technology for human's comfort. In this study, the authors propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems. Their proposed system simulates the human prediction actions by adding common sense data by utilizing the advantages of the ontology graph and the FP-growth to find a better solution in predicting home user actions for automated systems. For the evaluation of the proposed system, two ontology graphs are introduced with FP-growth to achieve the best results. Both graphs are tested through multiple weight values with the results of FP-growth. As a result, the best weight distribution selected in this study is (70, 30) for time and location ontology graphs respectively. Their results showed that the proposed prediction system achieved an accuracy of 79% for all weekdays and 81% excluding weekend days.

TODOS as digital science-support environment to provide STEM-education

The amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of unstructured data. The approach to systematization of scientific information based on the ontological IT platform Transdisciplinary Ontological Dialogs of Object-Oriented Systems (TODOS) has many benefits. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform TODOS. An ontological graph with a ranking function for previous scientific research and for a system of selection of journals has been worked out. These systems provide high performance of information management of scientific information.

Rab-KAMS: A Reproducible Knowledge Management System with Visualization for Preserving Rabbit Farming and Production Knowledge

The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production’ Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users’ interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users’ rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security.

Fact Checking in Knowledge Graphs with Ontological Subgraph Patterns

Given a knowledge graph and a fact (a triple statement), fact checking is to decide whether the fact belongs to the missing part of the graph. Facts in real-world knowledge bases are typically interpreted by both topological and semantic context that is not fully exploited by existing methods. This paper introduces a novel fact checking method that explicitly exploits discriminant subgraph structures. Our method discovers discriminant subgraphs associated with a set of training facts, characterized by a class of graph fact checking rules. These rules incorporate expressive subgraph patterns to jointly describe both topological and ontological constraints. (1) We extend graph fact checking rules ({mathsf{GFCs}}) to a class of ontological graph fact checking rules ({mathsf{OGFCs}}). {mathsf{OGFCs}} generalize {mathsf{GFCs}} by incorporating both topological constraints and ontological closeness to best distinguish between true and false fact statements. We provide quality measures to characterize useful patterns that are both discriminant and diversified. (2) Despite the increased expressiveness, we show that it is feasible to discover {mathsf{OGFCs}} in large graphs with ontologies, by developing a supervised pattern discovery algorithm. To find useful {mathsf{OGFCs}} as early as possible, it generates subgraph patterns relevant to training facts and dynamically selects patterns from a pattern stream with a small update cost per pattern. We verify that {mathsf{OGFCs}} can be used as rules and provide useful features for other statistical learning-based fact checking models. Using real-world knowledge bases, we experimentally verify the efficiency and the effectiveness of {mathsf{OGFC}}-based techniques for fact checking.

Ontology based systemazing of the science information devoted to waste utilizing by methanogenesys

Quantity of the scientific materials in the field of biogas production is growing. Thus, it is actual to provide informational management of it. Previously text documents and not-interactive pictures were used to describe it. The approach to systematization of scientific information on the production of biogas based on the ontological IT platform of TODOS is developed. It is proposed to separate the semantic characteristics of each work for their further introduction it into the IT platform of the TODOS. To construct a system of ranking of previous studies, we have identified the semantic characteristics of scientific research devoted to the production of biogas from chicken manure. Temperature (°C), volume of reactor (l), chicken manure content (%), moisture content (%), active sludge content (%), final solids content (%), biogas production and methane (ml/g TS), methane content (%), year of the research, ammonium nitrogen content (mg/l), VFA (mg/l), pH is final, initial pH, minimum pH, maximum pH were used to create ontology management system of the previous researches devoted to biogas production. The characteristics of the microorganisms were used to create the ontology based system of the microorganism selection. Google sheets was used to create base of knowledge and previous researches. An ontological graph with a ranking function for previous scientific research and a system of selection of microorganisms has been developed. Thus, the proposed systems allow to systematize previous research and theoretical information devoted to help of ontological graphs and provide information management in this field. Developed approaches allow us to analyze the results of previous research and theoretical information which provides systemizing of information and information management in general.

Partial multi-dividing ontology learning algorithm

As an effective data representation, storage, management, calculation and model for analysis, ontology has attracted more and more attention by researchers and it has been applied to various engineering disciplines. In the background of big data, the ontology is expected to increase the amount of data information and the structure of its corresponding ontology graph has become more important due to its complexity. It demands that the ontology algorithm must be more efficient than before. In a specific engineering application, the ontology algorithm is required to find in a quick way the semantic matching set of the concept and rank it back to the user according to their similarities. Therefore, to use learning tricks to get better ontology algorithms is an open problem nowadays.The aim of the present paper is to present a partial multi–dividing ontology algorithm with the aim of obtaining an efficient approach to optimize the partial multi–dividing ontology learning model. For doing it we state several theoretical results from a statistical learning theory perspective. Moreover, we present five experiments in different engineering fields to show the precision of our partial multi-dividing algorithm from angles of ontology, similarity measuring and ontology mapping building point of view.

Detecting Human Diseases Relatedness

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

Enumerating consistent sub-graphs of directed acyclic graphs: an insight into biomedical ontologies.

MotivationModern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics), often organized in concept hierarchies or ontologies. Most ontology can be seen as directed acyclic graphs (DAGs), where nodes represent concepts and edges represent relational ties between these concepts. Given an ontology graph, each object can only be annotated by a consistent sub-graph; that is, a sub-graph such that if an object is annotated by a particular concept, it must also be annotated by all other concepts that generalize it. Ontologies therefore provide a compact representation of a large space of possible consistent sub-graphs; however, until now we have not been aware of a practical algorithm that can enumerate such annotation spaces for a given ontology.ResultsWe propose an algorithm for enumerating consistent sub-graphs of DAGs. The algorithm recursively partitions the graph into strictly smaller graphs until the resulting graph becomes a rooted tree (forest), for which a linear-time solution is computed. It then combines the tallies from graphs created in the recursion to obtain the final count. We prove the correctness of this algorithm, propose several practical accelerations, evaluate it on random graphs and then apply it to characterize four major biomedical ontologies. We believe this work provides valuable insights into the complexity of concept annotation spaces and its potential influence on the predictability of ontological annotation.Availability and implementation https://github.com/shawn-peng/counting-consistent-sub-DAG Supplementary information Supplementary data are available at Bioinformatics online.