Ontology Graph Research Articles

Astoundingly Smart System Furnishing Ranking of Big Data in Search Engines

The abruptly escalating internet is sensational. It inculcates a humungous volume of big data, which is obsolete and tedious to manage, scrutinize, analyze and perform operations upon them in conventional ways. Big data has thus expedited the search and retrieval of information, necessitating the development of contemporary search algorithms to aid this process. However, the primary hindrance for the first and second generations of conventional search engines was the syntax of keywords devoid of semantic meaning and the lack of a knowledge base that linked disparate web material. This article presents a framework based on trendy technologies, specifically Extracting, Transforming and Integrating (ETI) processes, ontology graphs, and indexing Resource Description Framework (RDF) using the wide-column Not only Structured Query Language (NoSQL) method. The most significant contribution in this regard is developing a mathematical model to compute the similarity score between a query and stored RDF documents using semantic relations. Numerous operations were carried out to evaluate the effectiveness of the proposed methodology in installing data sources, such as DBpedia and YAGO dataset. Insofar as experimental results are concerned, the suggested model achieves greater precision than other comparable systems.

Implementation of a Photovoltaic System Model Using FAIR Digital Objects

Implementation of a Photovoltaic System Model Using FAIR Digital Objects

A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks

Research in artificial intelligence (AI) is addressing a growing number of tasks through a rapidly growing number of models and methodologies. This makes it difficult to keep track of where novel AI methods are successfully – or still unsuccessfully – applied, how progress is measured, how different advances might synergize with each other, and how future research should be prioritized. To help address these issues, we created the Intelligence Task Ontology and Knowledge Graph (ITO), a comprehensive, richly structured and manually curated resource on artificial intelligence tasks, benchmark results and performance metrics. The current version of ITO contains 685,560 edges, 1,100 classes representing AI processes and 1,995 properties representing performance metrics. The primary goal of ITO is to enable analyses of the global landscape of AI tasks and capabilities. ITO is based on technologies that allow for easy integration and enrichment with external data, automated inference and continuous, collaborative expert curation of underlying ontological models. We make the ITO dataset and a collection of Jupyter notebooks utilizing ITO openly available.

GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression.

Medical care is becoming more and more specific to patients' needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning (DL), can improve the field of precision medicine. However, their use in clinics is limited as their predictions are not accompanied by an explanation. The production of accurate and intelligible predictions can benefit from the inclusion of domain knowledge. Therefore, knowledge-based DL models appear to be a promising solution. In this article, we propose GraphGONet, where the Gene Ontology is encapsulated in the hidden layers of a new self-explaining neural network. Each neuron in the layers represents a biological concept, combining the gene expression profile of a patient and the information from its neighboring neurons. The experiments described in the article confirm that our model not only performs as accurately as the state-of-the-art (non-explainable ones) but also automatically produces stable and intelligible explanations composed of the biological concepts with the highest contribution. This feature allows experts to use our tool in a medical setting. GraphGONet is freely available at https://forge.ibisc.univ-evry.fr/vbourgeais/GraphGONet.git. The microarray dataset is accessible from the ArrayExpress database under the identifier E-MTAB-3732. The TCGA datasets can be downloaded from the Genomic Data Commons (GDC) data portal. Supplementary data are available at Bioinformatics online.

TAXONOMIZATION OF THE EXPEDITION RESEARCH RESULTS OF STUDENTS IN THE CONTEXT OF SCIENTIFIC EDUCATION

The modern development of humankind, particularly in the field of information technology, has led to the existence and accumulation of a great deal of unsystematic information. This problem also concerns the point at which results are presented in the scientific method. In particular, there is a lot of documentation that links results to geographic coordinates, particularly expeditionary search results. The paper aims to propose an innovative method linked to the structuring of expositional data with ontologies for the structuring of data and GIS. The fundamental characteristics and benefits of the proposed methodology are also described in this paper. The ontological expedition journal models have been developed to streamline and distribute expeditionary search results. Google tables, where the columns represent the name of the object to be analyzed, and in the lines, the measured parameter were designed. The special rows were designed to save geographical coordinates which were used in the future to locate objects on GIS. The approbation was carried out on the example of expedition research of students of the National Center “Junior Academy of Sciences of Ukraine” in the field of water quality. The main information classes are pH, Electrical conductivity, Mineralization., Hardness, Cl, SO4, Pb, Fe total, Fe (II), Fe (III), Cu, corresponding to indicators: acidity, electrical conductivity, mineralization, content of chlorides, sulfates, lead, total iron, ferrous iron, ferric iron and copper. The ontological graphs were developed in Cognitive IT Polyhedron with the possibility of processing results using the “Audit” and “Ranking” tools, as well as presenting information in the form of an ontological prism. The proposed method will provide simplification of information collected during the review (background research) stage of the science approach.

A New Implementation of ontostep: Flexible Generation of Ontology and Knowledge Graphs of express-Driven Data

Abstract ontostep is a method to translate the STandard for the Exchange of Product Model Data (STEP) schema and its instances to an ontology and knowledge graphs represented in the web ontology language (owl). ontostep models can be integrated with any owl models to enrich their semantics. However, the current implementation has several limitations, mainly in (1) supporting the latest International Organization for Standardization (ISO) 10303 schemas and (2) generating various representation types depending on the purpose of use. We present an improved implementation of ontostep to overcome these limitations. In this article, we demonstrate that the new implementation can successfully translate STEP schemas and instances in a faster and more flexible way, thus furthering the adoption of the full capabilities of ISO 10303. By encoding STEP entities in owl, we facilitate integration with other standards through knowledge graphs.

Leveraging the Cell Ontology to classify unseen cell types

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

TREE-BASED SEMANTIC ANALYSIS METHOD FOR NATURAL LANGUAGE PHRASE TO FORMAL QUERY CONVERSION

Context. This work is devoted to the problem of natural language interface construction for ontological graph databases. The focus here is on the methods for the conversion of natural language phrases into formal queries in SPARQL and CYPHER query languages. Objective. The goals of the work are the creation of a semantic analysis method for the input natural language phrases semantic type determination and obtaining meaningful entities from them for query template variables initialization, construction of flexible query templates for the types, development of program implementation of the proposed technique. Method. A tree-based method was developed for semantic determination of a user’s phrase type and obtaining a set of terms from it to put them into certain places of the most suiting formal query template. The proposed technique solves the tasks of the phrase type determination (and this is the criterion of the formal query template selection) and obtaining meaningful terms, which are to initialize variables of the chosen template. In the current work only interrogative and incentive user’s phrases are considered i.e. ones that clearly propose the system to answer or to do something. It is assumed that the considered dialog or reference system uses a graph ontological database, which directly impacts the formal query patterns – the resulting queries are destined to be in SPARQL or Cypher query languages. The semantic analysis examples considered in this work are aimed primarily at inflective languages, especially, Ukrainian and Russian, but the basic principles could be suitable to most of the other languages. Results. The developed method of natural language phrase to a formal query in SPARQL and CYPHER conversion has been implemented in software for Ukrainian and Norwegian languages using narrow subjected ontologies and tested against formal performance criteria. Conclusions. The proposed method allows the dialog system fast and with minimum number of steps to select the most suitable query template and extract informative entities from a natural language phrase given the huge phrase variability in inflective languages. Carried out experiments have shown high precision and reliability of the constructed system and its potential for practical usage and further development.

A Generic Ontology Framework for Indexing Keyword Search on Massive Graphs

Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. In this paper, we propose a generic ontology-based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index BiG-index), to enhance the search performance. The novelties of BiG-index BiG-index reside in using an ontology graph G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ont</sub> GOnt to summarize and index a data graph G G iteratively, to form a hierarchical index structure G. BiG-index BiG-index is generic since it only requires keyword search algorithms to generate query answers from summary graphs having two simple properties. Regarding query evaluation, we transform a keyword search q q into Q according to G <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ont</sub> GOnt in runtime. The transformed query is searched on the summary graphs in G. The efficiency is due to the small sizes of the summary graphs and the early pruning of semantically irrelevant subgraphs. To illustrate BiG-index BiG-index's applicability, we show popular indexing techniques for keyword search (e.g., Blinks Blinks and r-clique r-clique) can be easily implemented on top of BiG-index BiG-index. Our extensive experiments show that BiG-index BiG-index reduced the runtimes of popular keyword search work Blinks Blinks by 50.5 percent and r-clique r-clique by 29.5 percent.

Characterization and differentiation of quinoa seed proteomes by label-free mass spectrometry-based shotgun proteomics

Quinoa seed proteins are of prime importance in human nutrition and in plant breeding for cultivar identification and improvement. In this study, proteins from seeds of black, red, white quinoa from Peru and white quinoa from Bolivia (also known as royal) were extracted, digested and analyzed by nano-liquid chromatography coupled to Orbitrap tandem mass spectrometry (LC-MS/MS). The raw mass spectra data were processed for identification and label-free quantification (LFQ) using MaxQuant/Andromeda against a specific quinoa database from The National Center for Biotechnology Information (NCBI). In total, 1,211 quinoa proteins (85 were uncharacterized) were identified. Inspection and visualization using Venn diagrams, heat maps and Gene Ontology (GO) graphs revealed proteome similarities and differences between the four varieties. The presented data provides the most comprehensive experimental quinoa seed proteome map existing to date in the literature, as a starting point for more specific characterization and nutritional studies of quinoa and quinoa-containing foodstuff.

A semantic structuring of educational research using ontologies

This article is devoted to the presentation of the semantic interoperability of research and scientific results through an ontological taxonomy. To achieve this, the principles of systematization and structuration of the scientific/research results in scientometrics databases have been analysed. We use the existing cognitive IT platform Polyhedron and extend it with an ontology-based information model as main contribution. As a proof-of-concept we have modelled two ontological graphs, “Development of a rational way for utilization of methane tank waste at LLC Vasylkivska poultry farm” and “Development a method for utilization of methane tank effluent”. Also, for a demonstration of the perspective of ontological systems for a systematization of research and scientific results, the “Hypothesis test system” ontological graph has created.

Adaptive Information Sharing with Ontological Relevance Computation for Decentralized Self-Organization Systems.

Decentralization is a peculiar characteristic of self-organizing systems such as swarm intelligence systems, which function as complex collective responsive systems without central control and operates based on contextual local coordination among relatively simple individual systems. The decentralized particularity of self-organizing systems lies in their capacity to spontaneously respond to accommodate environmental changes in a cooperative manner without external control. However, if members cannot obtain observations of the state of the whole team and environment, they have to share their knowledge and policies with each other through communication in order to adapt to the environment appropriately. In this paper, we propose an information sharing mechanism as an independent decision phase to improve individual members’ joint adaption to the world to fulfill an optimal self-organization in general. We design the information sharing decision analogous to human information sharing mechanisms. In this case, information can be shared among individual members by evaluating the semantic relationship of information based on ontology graph and their local knowledge. That is, if individual member collects more relevant information, the information will be used to update its local knowledge and improve sharing relevant information by measuring the ontological relevance. This will enable more related information to be acquired so that their models will be reinforced for more precise information sharing. Our simulations and experimental results show that this design can share information efficiently to achieve optimal adaptive self-organizing systems.

Learning Knowledge Using Frequent Subgraph Mining from Ontology Graph Data

In many areas, vast amounts of information are rapidly accumulating in the form of ontology-based knowledge graphs, and the use of information in these forms of knowledge graphs is becoming increasingly important. This study proposes a novel method for efficiently learning frequent subgraphs (i.e., knowledge) from ontology-based graph data. An ontology-based large-scale graph is decomposed into small unit subgraphs, which are used as the unit to calculate the frequency of the subgraph. The frequent subgraphs are extracted through candidate generation and chunking processes. To verify the usefulness of the extracted frequent subgraphs, the methodology was applied to movie rating prediction. Using the frequent subgraphs as user profiles, the graph similarity between the rating graph and new item graph was calculated to predict the rating. The MovieLens dataset was used for the experiment, and a comparison showed that the proposed method outperformed other widely used recommendation methods. This study is meaningful in that it proposed an efficient method for extracting frequent subgraphs while maintaining semantic information and considering scalability in large-scale graphs. Furthermore, the proposed method can provide results that include semantic information to serve as a logical basis for rating prediction or recommendation, which existing methods are unable to provide.

КОНВЕРТАЦІЯ ФОРМАТІВ ЗБЕРІГАННЯ ГРАФОВИХ БАЗ ДАНИХ І ЇЇ РОЛЬ В ОНТОЛОГІЧНОМУ ІНЖИНІРИНГУ НА ПРИКЛАДІ СИСТЕМИ СУПРОВОДЖЕННЯ РЕАБІЛІТАЦІЇ

КОНВЕРТАЦІЯ ФОРМАТІВ ЗБЕРІГАННЯ ГРАФОВИХ БАЗ ДАНИХ І ЇЇ РОЛЬ В ОНТОЛОГІЧНОМУ ІНЖИНІРИНГУ НА ПРИКЛАДІ СИСТЕМИ СУПРОВОДЖЕННЯ РЕАБІЛІТАЦІЇ

Intelligent System for Ranking Big Data in Search Engine

The spread of Internet sources has increased the volume of big data that is difficult to handle in traditional ways. So, most users need modern search systems to facilitate the search and retrieval of information in the presence of big data. However, the main challenge in the first and second conventional generations of search engines are linking different web data based on the syntax of keywords not on the semantic meaning and without a knowledge base. This manuscript proposes a framework based on modern technologies such as ETI processes, ontology graphs, and indexing RDF using wide column NoSQL technique. The main contribution of our work is introducing a mathematical model that is used to calculate the similarity score between a query and stored RDF documents based on semantic relations. Various operations were carried out to measure the proposed model's efficiency using data sources such as DBpedia, YAGO dataset. According to experimental results, the proposed model is achieving high precision compared to other related systems.

Terminological graph of context-oriented ontology

Terminological graph of context-oriented ontology

Использование генеративно-состязательных сетей в задаче предсказания существования рёбер в онтологическом графе

Рассматривается модель машинного обучения для предсказания существования рёбер в графе онтологии, основанная на использовании генеративно-состязательной сети. Проведены вычислительные эксперименты для различных наборов значений гиперпараметров модели. Показано, что модель решает поставленную задачу. Сформулированы направления дальнейшего развития данного подхода. A machine learning model for edge existence prediction in an ontology graph, based on generative adversarial networks, is considered. Computational experiments for different sets of hyper parameter values are fulfilled. It is shown that the model solves the task. Further steps on this approach research are formulated.

Approach to Blank Node Processing in Incremental Data Visualization by the Example of Ontodia

The problem of lazy visualization of ontological graphs has certain constraints. Visualization of structures that contain blank nodes is especially challenging. In this paper, we propose an approach to visualization of these structures that is implemented in Ontodia, analyze the limitations of this approach, and consider some alternative solutions.

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.

Probabilistic Topic Models for Enriching Ontology from Texts

The ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant approach able to achieve automatic enrichment from a textual corpus or dataset of various topics. In this paper, we describe a new approach for automatic learning of terminological ontologies from textual corpus based on probabilistic models. In our approach, two topic modeling algorithms are explored, namely LDA and pLSA for learning topic ontology. The objective is to capture semantic relationships between word-topic and topic-document in terms of probability distributions to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and retrieving corresponding topic ontology for a user query demonstrates the effectiveness of the proposed approach.