Ontology Creation Research Articles

Ontological Anti-naturalism and the Emergence of Life and Mind: Castoriadis and Deacon

ABSTRACTThis essay compares the ideas of Cornelius Castoriadis and Terrence W. Deacon. Castoriadis’s anti-Naturalistic ontology, with its conception of radical ontological creation and fundamental indeterminacy, along with his analysis of the category of the “for-itself”, comprising all subjective beings from the living organism to the social-historical, is compared to Deacon’s exploration of the emergence of life and mind, which sees the emergence of teleological beings as resulting from the creation of form-generating constraints that involve new types of dynamic process. Significant parallels and convergences between Castoriadis and Deacon are uncovered and explored.

The Creation of an Arabic Emotion Ontology Based on E-Motive

Abstract: There is an increased interest in social media monitoring to analyse massive, free form, short user-generated text from multiple social media sites such as Facebook, WhatsApp and Twitter. Companies are interested in sentiment analysis to understand customers’ opinions about their products/services. Governments and law enforcement agencies are interested in identifying threats to safeguard their country's national security. They are actively seeking ways to monitor and analyse the public's responses to various services, activities and events, especially since social media has become a valuable real-time resource of information. This study builds on prior work that focused on sentiment classification (i.e., positive, negative). This study primarily aims to design and develop a social sentiment-parsing algorithm for capturing and monitoring an extensive and comprehensive range of emotions from Arabic social media text. The study contributes to the field of sentiment analysis (opinion mining) and can subsequently be used for web mining, cleansing and analytics.

Ontological Modeling of Satellite's Manufacturing Work Flow Instruction

The article describes the ontological modeling of formation manufacturing work flow instructions. Ontology inspection of incoming goods is exposed to more detailed consideration. Ontological modeling was performed using Fluent Editor. Fluent Editor is an instrument of creation of ontologies based on Controlled Natural Language (CNL). Functional ontology visualization was described method of creation and verification instructions ontology. The analysis was performed on methods of creation ontology and its verification using visualization instrument. Ontology instructions can be used as a basis for the development of information systems instruction execution in this or related engineering fields. In the future, this information system can be used as an electronic assistant supervisors conducting inspection on any instructions or manuals. Ontological modeling makes a transition from document-centric from data-centric system with different kinds of checks or inspections.

Domain Specific Ontology Creation for Marathi Language

Domain Specific Ontology Creation for Marathi Language

Temporal schema versioning in τOWL: a systematic approach for the management of time-varying knowledge

In this work, we consider the adoption of an OWL 2 ontology for the representation and management of time-varying knowledge. The W3C OWL 2 recommendation lacks explicit support for time-varying OWL ontologies. In previous work, we introduced τOWL which is an infrastructure and suite of tools to support the creation and validation of OWL ontologies with time-varying instances. In this paper, we extend τOWL to also support temporal versioning of the ontology schema. Schema versioning in the τOWL framework means that all the three components of a τOWL schema (i.e. conventional schema, logical and physical annotations) are versioned. In particular, we propose two complete sets of primitives, one for updating the conventional ontology schema and the other for managing the temporal ontology schema, and define their operational semantics. We also illustrate their use, through an example.

Re-creating Wilderness 2.0: Or getting back to work in a virtual nature

Camping on Twitter, trekking in Google Street View, mountaineering on Snapchat. Wilderness is dead. Long live Wilderness 2.0. In this paper, the term “Wilderness 2.0” refers to the articulation of new media technologies, including mobile digital devices, web 2.0 and locative media, with the practice of wilderness recreation. In this new era of virtual nature, outdoor recreation occurs as much in the statusphere and blogosphere as it does in the biosphere. While much public and academic debate about Wilderness 2.0 has focused on the extent to which new media technologies connect people to, or disconnect them from, nature, this paper argues that wilderness is not a static and essential reality that can simply be connected to or disconnected from, but a social construct that is continually re-created in different cultural contexts. In this sense, Wilderness 2.0 reflects the re-creation of a new ontology of wilderness as DigiPlace: an augmented reality that blurs the lines between the “actual” and the “virtual.” Moreover, Wilderness 2.0 does not simply refer to the creation of a new ontology of wilderness, but the incorporation of outdoor recreation into the political economy of the web 2.0. In the context of Wilderness 2.0, outdoor recreation is increasingly exploited as a form of virtual labour. Thus, despite being associated with a discourse of “sharing” and “connecting to nature,” Wilderness 2.0 is, above all else, a nature that capital can see.

Perspectives on making big data analytics work for oncology

Oncology, with its unique combination of clinical, physical, technological, and biological data provides an ideal case study for applying big data analytics to improve cancer treatment safety and outcomes. An oncology treatment course such as chemoradiotherapy can generate a large pool of information carrying the 5Vs hallmarks of big data. This data is comprised of a heterogeneous mixture of patient demographics, radiation/chemo dosimetry, multimodality imaging features, and biological markers generated over a treatment period that can span few days to several weeks. Efforts using commercial and in-house tools are underway to facilitate data aggregation, ontology creation, sharing, visualization and varying analytics in a secure environment. However, open questions related to proper data structure representation and effective analytics tools to support oncology decision-making need to be addressed. It is recognized that oncology data constitutes a mix of structured (tabulated) and unstructured (electronic documents) that need to be processed to facilitate searching and subsequent knowledge discovery from relational or NoSQL databases. In this context, methods based on advanced analytics and image feature extraction for oncology applications will be discussed. On the other hand, the classical p (variables)≫n (samples) inference problem of statistical learning is challenged in the Big data realm and this is particularly true for oncology applications where p-omics is witnessing exponential growth while the number of cancer incidences has generally plateaued over the past 5-years leading to a quasi-linear growth in samples per patient. Within the Big data paradigm, this kind of phenomenon may yield undesirable effects such as echo chamber anomalies, Yule–Simpson reversal paradox, or misleading ghost analytics. In this work, we will present these effects as they pertain to oncology and engage small thinking methodologies to counter these effects ranging from incorporating prior knowledge, using information-theoretic techniques to modern ensemble machine learning approaches or combination of these. We will particularly discuss the pros and cons of different approaches to improve mining of big data in oncology.

Automated population of an i2b2 clinical data warehouse from an openEHR-based data repository

BackgroundDetailed Clinical Model (DCM) approaches have recently seen wider adoption. More specifically, openEHR-based application systems are now used in production in several countries, serving diverse fields of application such as health information exchange, clinical registries and electronic medical record systems. However, approaches to efficiently provide openEHR data to researchers for secondary use have not yet been investigated or established. MethodsWe developed an approach to automatically load openEHR data instances into the open source clinical data warehouse i2b2. We evaluated query capabilities and the performance of this approach in the context of the Hanover Medical School Translational Research Framework (HaMSTR), an openEHR-based data repository. ResultsAutomated creation of i2b2 ontologies from archetypes and templates and the integration of openEHR data instances from 903 patients of a paediatric intensive care unit has been achieved. In total, it took an average of ∼2527s to create 2.311.624 facts from 141.917 XML documents. Using the imported data, we conducted sample queries to compare the performance with two openEHR systems and to investigate if this representation of data is feasible to support cohort identification and record level data extraction. DiscussionWe found the automated population of an i2b2 clinical data warehouse to be a feasible approach to make openEHR data instances available for secondary use. Such an approach can facilitate timely provision of clinical data to researchers. It complements analytics based on the Archetype Query Language by allowing querying on both, legacy clinical data sources and openEHR data instances at the same time and by providing an easy-to-use query interface. However, due to different levels of expressiveness in the data models, not all semantics could be preserved during the ETL process.

CREATION OF THE DOMAIN KNOWLEDGE ONTOLOGY BY THE FUTURE ENGINEERS-TEACHERS IN THE AREA OF COMPUTER TECHNOLOGY

The problem of creation of the domain knowledge ontology in education of future engineering teachers in branch of Information Technology is considered in the article. The ontology of Information System domain knowledge has been built. This enables to combine a huge amount of existing information into a single knowledge base that combines several disciplines. It has been shown in the process of investigation that Protege OWL programming product is the best for the creation of computer system ontology in preparing future engineering teachers and makes it possible to describe not only concepts, but also specific objects. It is based on a logical model that allows creating definitions of Information System domain knowledge of informal description. The structure and methods of building and using in future the ontology of Information System domain knowledge in the professional activity future engineering teachers in the area of computer technology has been studied.

Design and development of semantic web-based system for computer science domain-specific information retrieval

Summary In semantic web-based system, the concept of ontology is used to search results by contextual meaning of input query instead of keyword matching. From the research literature, there seems to be a need for a tool which can provide an easy interface for complex queries in natural language that can retrieve the domain-specific information from the ontology. This research paper proposes an IRSCSD system (Information retrieval system for computer science domain) as a solution. This system offers advanced querying and browsing of structured data with search results automatically aggregated and rendered directly in a consistent user-interface, thus reducing the manual effort of users. So, the main objective of this research is design and development of semantic web-based system for integrating ontology towards domain-specific retrieval support. Methodology followed is a piecemeal research which involves the following stages. First Stage involves the designing of framework for semantic web-based system. Second stage builds the prototype for the framework using Protege tool. Third Stage deals with the natural language query conversion into SPARQL query language using Python-based QUEPY framework. Fourth Stage involves firing of converted SPARQL queries to the ontology through Apache's Jena API to fetch the results. Lastly, evaluation of the prototype has been done in order to ensure its efficiency and usability. Thus, this research paper throws light on framework development for semantic web-based system that assists in efficient retrieval of domain-specific information, natural language query interpretation into semantic web language, creation of domain-specific ontology and its mapping with related ontology. This research paper also provides approaches and metrics for ontology evaluation on prototype ontology developed to study the performance based on accessibility of required domain-related information.

CNLs for the semantic web: a state of the art

One of the core challenges for building the semantic web is the creation of ontologies, a process known as ontology authoring. Controlled natural languages (CNLs) propose different frameworks for interfacing and creating ontologies in semantic web systems using restricted natural language. However, in order to engage non-expert users with no background in knowledge engineering, these language interfacing must be reliable, easy to understand and accepted by users. This paper includes the state-of-the-art for CNLs in terms of ontology authoring and the semantic web. In addition, it includes a detailed analysis of user evaluations with respect to each CNL and offers analytic conclusions with respect to the field.

Webulous and the Webulous Google Add-On--a web service and application for ontology building from templates.

BackgroundAuthoring bio-ontologies is a task that has traditionally been undertaken by skilled experts trained in understanding complex languages such as the Web Ontology Language (OWL), in tools designed for such experts. As requests for new terms are made, the need for expert ontologists represents a bottleneck in the development process. Furthermore, the ability to rigorously enforce ontology design patterns in large, collaboratively developed ontologies is difficult with existing ontology authoring software.DescriptionWe present Webulous, an application suite for supporting ontology creation by design patterns. Webulous provides infrastructure to specify templates for populating ontology design patterns that get transformed into OWL assertions in a target ontology. Webulous provides programmatic access to the template server and a client application has been developed for Google Sheets that allows templates to be loaded, populated and resubmitted to the Webulous server for processing.ConclusionsThe development and delivery of ontologies to the community requires software support that goes beyond the ontology editor. Building ontologies by design patterns and providing simple mechanisms for the addition of new content helps reduce the overall cost and effort required to develop an ontology. The Webulous system provides support for this process and is used as part of the development of several ontologies at the European Bioinformatics Institute.

A Naive Method For Ontology Construction

Ontologies are being used to organize information in many domains like artificial intelligence, information science, semantic web, library science. Ontologies of an entity having different information can be merged to create more knowledge of that particular entity. Ontologies today are powering more accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0, also termed as the semantic web, ontologies will play a more important role. Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can yield basic information about an entity. This paper proposes an automated method for ontology creation, using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning. Concepts drawn from these domains help in designing more accurate ontologies represented using the XML format. This paper uses document classification using classification algorithms for assigning labels to documents, document similarity to cluster similar documents to the input document, together, and summarization to shorten the text and keep important terms essential in making the ontology. The module is constructed using the Python programming language and NLTK (Natural Language Toolkit). The ontologies created in XML will convey to a lay person the definition of the important term's and their lexical relationships.

Concept Based Dynamic Ontology Creation for Job Recommendation System

The basis of our research is to construct a job recommendation system to the job seekers by collecting the job portals data. Due to huge amounts of the data in job portals the employers are facing difficulty in the identification of right candidate for the required skill and experience. The job seekers are also facing the problem of getting the suitability of the job based on their skill and experience. The knowledge acquisition based on the requirements is very difficult in case of huge amounts of the data sources. In fact classical development of domain ontology is typically entirely based on strong human participation. It does not adequately fit new applications requirements, because they need a more dynamic ontology and the possibility to manage a considerable quantity of concepts that human cannot achieve alone. The main focus of our work is to generate a job recommendation system with the details of job by taking account into the data posted in the web sites and data from the job seekers by the creation of dynamic ontology. We strongly believe that our system will give the best outcome in case of suitable job recommendation for both employers and job seekers without spending much time. To achieve this first we have extracted the data from various web pages and stored the collected data into .csv files. In the second stage the stored input files are used by the similarity measure and ontology creation module by generating the corresponding Web Ontology Language (.owl) file. The third stage is creating the ontology with the generated .owl by using protégé tool.

Towards Domain Ontology Creation Based on a Taxonomy Structure in Computer Vision

In computer vision to create a knowledge base usable by information systems, we need a data structure facilitating the information access. Artificial intelligence community uses the ontologies to structure and represent the domain knowledge. This information structure can be used as a database of many geographic information systems (GIS) or information systems treating real objects for example road scenes, besides it can be utilized by other systems. For this, we provide a process to create a taxonomy structure based on new hierarchical image clustering method. The hierarchical relation is based on visual object features and contributes to build domain ontology.

Graph databases methodology and tool supporting index/store versioning

Graph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed with inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, deleting concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is a time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, this paper proposes a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.

An ontological architecture for orbital debris data

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.

Using Gene Ontology to describe the role of the neurexin-neuroligin-SHANK complex in human, mouse and rat and its relevance to autism.

BackgroundPeople with an autistic spectrum disorder (ASD) display a variety of characteristic behavioral traits, including impaired social interaction, communication difficulties and repetitive behavior. This complex neurodevelopment disorder is known to be associated with a combination of genetic and environmental factors. Neurexins and neuroligins play a key role in synaptogenesis and neurexin-neuroligin adhesion is one of several processes that have been implicated in autism spectrum disorders.ResultsIn this report we describe the manual annotation of a selection of gene products known to be associated with autism and/or the neurexin-neuroligin-SHANK complex and demonstrate how a focused annotation approach leads to the creation of more descriptive Gene Ontology (GO) terms, as well as an increase in both the number of gene product annotations and their granularity, thus improving the data available in the GO database.ConclusionsThe manual annotations we describe will impact on the functional analysis of a variety of future autism-relevant datasets. Comprehensive gene annotation is an essential aspect of genomic and proteomic studies, as the quality of gene annotations incorporated into statistical analysis tools affects the effective interpretation of data obtained through genome wide association studies, next generation sequencing, proteomic and transcriptomic datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0622-0) contains supplementary material, which is available to authorized users.

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research.

Adverse and suboptimal health behaviors and habits are responsible for approximately 40% of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Support for cross-domain composition of embedded systems using MARTE models

Embedded systems have evolved from tailormade systems developed by specialists to artefacts built from complex software/hardware components with many extra-functional concerns. Ubiquity of embedded devices demands other facets such as security and safety to be brought to the forefront. At the same time, cost efficiency dictates building systems from reusable building blocks. However, integration of extra-functional building blocks comes with a certain resource overhead that must be taken into consideration while designing resource-constrained embedded systems. This paper builds on the premise that functional models can be extended with platform modelling to help the application engineers to select the right extra-functional building blocks while accounting for performance implications of their integration. We define a UML profile relating it to relevant parts of the MARTE profile in order to capture the performance analysis results for a reusable building block, and a generic notion of model-based compatibility analysis for platform models. Additionally, our approach rests on creation of ontologies to store MARTE descriptions of hardware components, and is supported by a MagicDraw plugin developed for capturing the analysis results and performing the compatibility analysis.