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Showing papers on "Ontology-based data integration published in 2015"


Journal ArticleDOI
TL;DR: Improvements and expansions to several branches of the Gene Ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community are described.
Abstract: The Gene Ontology (GO; http://wwwgeneontologyorg) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology

2,529 citations


Book
31 Jul 2015
TL;DR: This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.
Abstract: In the era of "big data," science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use.After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies.

659 citations


Proceedings ArticleDOI
01 Feb 2015
TL;DR: This survey paper investigates why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process.
Abstract: Semantic Data Mining refers to the data mining tasks that systematically incorporate domain knowledge, especially formal semantics, into the process. In the past, many research efforts have attested the benefits of incorporating domain knowledge in data mining. At the same time, the proliferation of knowledge engineering has enriched the family of domain knowledge, especially formal semantics and Semantic Web ontologies. Ontology is an explicit specification of conceptualization and a formal way to define the semantics of knowledge and data. The formal structure of ontology makes it a nature way to encode domain knowledge for the data mining use. In this survey paper, we introduce general concepts of semantic data mining. We investigate why ontology has the potential to help semantic data mining and how formal semantics in ontologies can be incorporated into the data mining process. We provide detail discussions for the advances and state of art of ontology-based approaches and an introduction of approaches that are based on other form of knowledge representations.

157 citations


Journal ArticleDOI
TL;DR: An overall process model synthesized from an overview of the existing models in the literature is provided, which concludes on future challenges for techniques aiming to solve that particular stage of ontology evolution.
Abstract: Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

138 citations


Proceedings ArticleDOI
07 Apr 2015
TL;DR: An ontology developed for a cyber security knowledge graph database is described to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain.
Abstract: In this paper we describe an ontology developed for a cyber security knowledge graph database. This is intended to provide an organized schema that incorporates information from a large variety of structured and unstructured data sources, and includes all relevant concepts within the domain. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe areas for future work.

118 citations


Proceedings ArticleDOI
12 Nov 2015
TL;DR: This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology Evaluation techniques, while discussing their advantages and drawbacks.
Abstract: Ontologies nowadays have become widely used for knowledge representation, and are considered as foundation for Semantic Web. However with their wide spread usage, a question of their evaluation increased even more. This paper addresses the issue of finding an efficient ontology evaluation method by presenting the existing ontology evaluation techniques, while discussing their advantages and drawbacks. The presented ontology evaluation techniques can be grouped into four categories: gold standard-based, corpus-based, task-based and criteria based approaches.

110 citations


Journal ArticleDOI
TL;DR: DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.

87 citations


Journal ArticleDOI
TL;DR: A comprehensive study of the concept of Ontology is proposed firstly in its domain of origin, Philosophy, and secondly in information science to provide a framework describing the general state of research on the use of ontologies in the context of PLM.
Abstract: The use of ontologies in the context of product lifecycle management (PLM) is gaining importance and popularity, while at the same time it generates a lot of controversy in discussions within scientific and engineering communities. Yet, what is ontology? What challenges have been addressed so far? What role does ontology play? Do we really need ontology? These are the core questions this paper seeks to address. We propose to conduct a comprehensive study of the concept of Ontology firstly in its domain of origin, Philosophy, and secondly in information science. Based on the understanding of this concept and an in-depth analysis of the state of the art, seven key roles of ontology are defined. These roles serve as a framework describing the general state of research on the use of ontologies in the context of PLM.

84 citations


Book ChapterDOI
11 Oct 2015
TL;DR: In this article, the authors present data access challenges in the data-intensive petroleum company Statoil and their experience in addressing these challenges with OBDA technology, and develop a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion, and a query processing module to perform and optimize the process of translating ontological queries into data queries and their execution.
Abstract: Ontology Based Data Access OBDA is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is 'connected' to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion, and a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution. Our modules have been successfully deployed and evaluated for an OBDA solution in Statoil.

80 citations


Journal ArticleDOI
TL;DR: A proposed semantic ETL framework applies semantics to various data fields and so allows richer data integration in the extract-transform-load process.
Abstract: Current tools that facilitate the extract-transform-load (ETL) process focus on ETL workflow, not on generating meaningful semantic relationships to integrate data from multiple, heterogeneous sources. A proposed semantic ETL framework applies semantics to various data fields and so allows richer data integration.

77 citations


Journal ArticleDOI
14 Jan 2015-PLOS ONE
TL;DR: This work uses ontologies to organize and describe the medical concepts of both the source system and the target system and demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.
Abstract: Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.

Book ChapterDOI
11 Oct 2015
TL;DR: Klink-2 is presented, a novel approach which improves on earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web.
Abstract: The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics --- e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii they do not distinguish between different kinds of hierarchical relationships; and iii they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities including papers, authors, venues, and technologies to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords e.g., "ontology" and separates them into the appropriate distinct topics --- e.g., "ontology/philosophy" vs. "ontology/semantic web". Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall.

Journal ArticleDOI
TL;DR: In this paper, the ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL).
Abstract: Background Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning.

Journal ArticleDOI
TL;DR: KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts rather than features of source-specific data schemas or file formats.
Abstract: The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources. We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license. KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Journal ArticleDOI
TL;DR: A type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently.

Journal ArticleDOI
TL;DR: The eNanoMapper project as discussed by the authors is a pan-European computational infrastructure for toxicological data management for ENM, based on semantic web standards and ontologies, which is used for the development of the eNANOMAP ontology based on the existing ontologies of relevance for the nanosafety domain.
Abstract: Engineered nanomaterials (ENMs) are being developed to meet specific application needs in diverse domains across the engineering and biomedical sciences (e.g. drug delivery). However, accompanying the exciting proliferation of novel nanomaterials is a challenging race to understand and predict their possibly detrimental effects on human health and the environment. The eNanoMapper project (www.enanomapper.net) is creating a pan-European computational infrastructure for toxicological data management for ENMs, based on semantic web standards and ontologies. Here, we describe the development of the eNanoMapper ontology based on adopting and extending existing ontologies of relevance for the nanosafety domain. The resulting eNanoMapper ontology is available at http://purl.enanomapper.net/onto/enanomapper.owl. We aim to make the re-use of external ontology content seamless and thus we have developed a library to automate the extraction of subsets of ontology content and the assembly of the subsets into an integrated whole. The library is available (open source) at http://github.com/enanomapper/slimmer/. Finally, we give a comprehensive survey of the domain content and identify gap areas. ENM safety is at the boundary between engineering and the life sciences, and at the boundary between molecular granularity and bulk granularity. This creates challenges for the definition of key entities in the domain, which we also discuss.

Journal ArticleDOI
TL;DR: A four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology, and a classification of semantic interoperability issues.
Abstract: The present-day health data ecosystem comprises a wide array of complex heterogeneous data sources. A wide range of clinical, health care, social and other clinically relevant information are stored in these data sources. These data exist either as structured data or as free-text. These data are generally individual person-based records, but social care data are generally case based and less formal data sources may be shared by groups. The structured data may be organised in a proprietary way or be coded using one-of-many coding, classification or terminologies that have often evolved in isolation and designed to meet the needs of the context that they have been developed. This has resulted in a wide range of semantic interoperability issues that make the integration of data held on these different systems changing. We present semantic interoperability challenges and describe a classification of these. We propose a four-step process and a toolkit for those wishing to work more ontologically, progressing from the identification and specification of concepts to validating a final ontology. The four steps are: (1) the identification and specification of data sources; (2) the conceptualisation of semantic meaning; (3) defining to what extent routine data can be used as a measure of the process or outcome of care required in a particular study or audit and (4) the formalisation and validation of the final ontology. The toolkit is an extension of a previous schema created to formalise the development of ontologies related to chronic disease management. The extensions are focused on facilitating rapid building of ontologies for time-critical research studies.

Journal ArticleDOI
TL;DR: A set of ontologies that complement CORA with notions such as industrial design and positioning are introduced and updates to CORA are introduced in order to provide more ontologically sound representations of autonomy and of robot parts.
Abstract: The working group Ontologies for Robotics and Automation, sponsored by the IEEE Robotics & Automation Society, recently proposed a Core Ontology for Robotics and Automation (CORA). This ontology was developed to provide an unambiguous definition of core notions of robotics and related topics. It is based on SUMO, a top-level ontology of general concepts, and on ISO 8373:2012 standard, developed by the ISO/TC184/SC2 Working Group, which defines-in natural language-important terms in the domain of Robotics and Automation (R&A). In this paper, we introduce a set of ontologies that complement CORA with notions such as industrial design and positioning. We also introduce updates to CORA in order to provide more ontologically sound representations of autonomy and of robot parts. HighlightsWe discuss extensions to a core ontology for the robotics and automation field.The ontology aims to specify the main notions across robotics subdomains.We define robot, robotic system, robotic environment, and related notions.We discuss concepts regarding the notion of design, in industrial contexts.We discuss notions regarding the modes of operation of a robot.We discuss notions regarding the position, orientation and pose of a robot.

Journal ArticleDOI
TL;DR: A new extraction and opinion mining system based on a type-2 fuzzy ontology called T2FOBOMIE is proposed, which retrieves targeted hotel reviews and extracts feature opinions from reviews using a fuzzy domain ontology.
Abstract: The volume of traveling websites is rapidly increasing. This makes relevant information extraction more challenging. Several fuzzy ontology-based systems have been proposed to decrease the manual work of a full-text query search engine and opinion mining. However, most search engines are keyword-based, and available full-text search engine systems are still imperfect at extracting precise information using different types of user queries. In opinion mining, travelers do not declare their hotel opinions entirely but express individual feature opinions in reviews. Hotel reviews have numerous uncertainties, and most featured opinions are based on complex linguistic wording (small, big, very good and very bad). Available ontology-based systems cannot extract blurred information from reviews to provide better solutions. To solve these problems, this paper proposes a new extraction and opinion mining system based on a type-2 fuzzy ontology called T2FOBOMIE. The system reformulates the user's full-text query to extract the user requirement and convert it into the format of a proper classical full-text search engine query. The proposed system retrieves targeted hotel reviews and extracts feature opinions from reviews using a fuzzy domain ontology. The fuzzy domain ontology, user information and hotel information are integrated to form a type-2 fuzzy merged ontology for the retrieving of feature polarity and individual hotel polarity. The Protege OWL-2 (Ontology Web Language) tool is used to develop the type-2 fuzzy ontology. A series of experiments were designed and demonstrated that T2FOBOMIE performance is highly productive for analyzing reviews and accurate opinion mining.

Journal ArticleDOI
TL;DR: The proposed framework for addressing the semantic heterogeneity problem through merging domain-specific ontologies based on multiple external semantic resources soundly enriches the knowledge bases with missing background knowledge, and thus enables the reuse of the newly obtained knowledge in future ontology merging tasks.
Abstract: With the development of the Semantic Web (SW), the creation of ontologies to formally conceptualize our understanding of various domains has widely increased in number. However, the conceptual and terminological differences (a.k.a semantic heterogeneity problem) between ontologies form a major limiting factor towards their use/reuse and full adoption in practical settings. A key solution to addressing this problem can be through identifying semantic correspondences between the entities (including concepts, relations, and instances) of heterogeneous ontologies, and consequently achieving interoperability between them. This process is also known as ontology alignment. The output of this process can be further exploited to merge ontologies into a single coherent ontology. Indeed, this is widely regarded as a crucial, yet difficult task, specifically when dealing with heavyweight ontologies that consist of hundreds of thousands of concepts. To address this issue, various ontology merging approaches have been proposed. These approaches can be classified into three categories: single-strategy-based approaches, multiple-strategy-based approaches, and approaches based on exploiting external semantic resources. In this paper, we first discuss the strengths and limitations of each of these approaches, and then present our framework for addressing the semantic heterogeneity problem through merging domain-specific ontologies based on multiple external semantic resources. The novelty of the proposed approach is mainly based on employing knowledge represented by multiple external resources (knowledge bases in our work) to make aggregated decisions on the semantic correspondences between the entities of heterogeneous ontologies. Other important issues that we attempt to tackle in the proposed framework are: (i) Identifying and handling inconsistency of semantic relations between the ontology concepts and, (ii) Handling the issue of missing background knowledge (such as concepts and instances) in the exploited knowledge bases by utilizing an integrated statistical and semantic technique. Additionally, the proposed solution soundly enriches the knowledge bases with missing background knowledge, and thus enables the reuse of the newly obtained knowledge in future ontology merging tasks. To validate our proposal, we tested the framework using the OAEI 2009 benchmark and compared the produced results with state-of-the-art syntactic and semantic based systems. In addition, we utilized the proposed techniques to merge three heavyweight ontologies from the environmental domain.

Journal ArticleDOI
Thabet Slimani1
TL;DR: This paper reviews and compares some Ontology Development Tools, Formalisms and Languages from those reported in the Literature, with a special attention accorded to the interoperability between them.
Abstract: This paper reviews and compares some Ontology Development Tools, Formalisms and Languages from those reported in the Literature, with a special attention accorded to the interoperability between them. Additionally, this paper presents the Structure and Basic Features of Tools, Formalisms and languages. The main criterion for comparison of these tools and languages was the user interest and their application in different kind of real world tasks. The primary goal of this study is to introduce several tools and languages to ensure more understanding from their use. Consequently,we can solve the problems of current tools and languages and ensure the easy development of a new generation of tools and languages.

Book ChapterDOI
11 Oct 2015
TL;DR: The GeoLink modular ontology consists of an interlinked collection of ontology design patterns engineered as the result of a collaborative modeling effort, and it is discussed how data integration can be achieved using the patterns while respecting the existing heterogeneity within the participating repositories.
Abstract: GeoLink is one of the building block projects within EarthCube, a major effort of the National Science Foundation to establish a next-generation knowledge infrastructure for geosciences. As part of this effort, GeoLink aims to improve data retrieval, reuse, and integration of seven geoscience data repositories through the use of ontologies. In this paper, we report on the GeoLink modular ontology, which consists of an interlinked collection of ontology design patterns engineered as the result of a collaborative modeling effort. We explain our design choices, present selected modeling details, and discuss how data integration can be achieved using the patterns while respecting the existing heterogeneity within the participating repositories.

Journal ArticleDOI
TL;DR: Affective applications require a common way to represent emotions so it can be more easily integrated, shared and reused by applications improving user experience, and this proposal is to use rich semantic models based on ontology.

Journal ArticleDOI
TL;DR: The proposed ontology proved to be a valuable link between separate ICT systems involving equipment from various vendors, both on syntax and semantic level, thus offering the facility managers the ability to retrieve high-level information regarding the performance of significant energy consumers.

Proceedings ArticleDOI
01 Jun 2015
TL;DR: A smart home sensor ontology is developed that is a specialized ontology based on the Semantic Sensor Networks (SSN) ontology and a simulation environment for a smart home case is presented using this ontological and early performance results are presented.
Abstract: Internet of Things (IoT) is a network that consists of embedded objects communicating with each other, sense their environment and interact with it. The number of connected devices is increasing day by day and it is expected to reach 26 billion by 2020. There is a huge potential for the development of many applications in IoT. Up to now, the communication of agents in IoT is solved in different ways: non-IP solutions, IP-based solutions and recently by high level, middleware solutions. The diversity of sensors and the complexity of data heterogeneity are solved by use of ontologies in recent works. In this paper, we present a smart home sensor ontology we developed that is a specialized ontology based on the Semantic Sensor Networks (SSN) ontology. We also present a simulation environment we developed for a smart home case using our ontology and present early performance results.

Proceedings Article
05 Nov 2015
TL;DR: The DQ ontology serves as an unambiguous vocabulary, which defines concepts more precisely than natural language; it provides a mechanism to automatically compute data quality measures; and is reusable across domains and use cases.
Abstract: The secondary use of EHR data for research is expected to improve health outcomes for patients, but the benefits will only be realized if the data in the EHR is of sufficient quality to support these uses. A data quality (DQ) ontology was developed to rigorously define concepts and enable automated computation of data quality measures. The healthcare data quality literature was mined for the important terms used to describe data quality concepts and harmonized into an ontology. Four high-level data quality dimensions ("correctness", "consistency", "completeness" and "currency") categorize 19 lower level measures. The ontology serves as an unambiguous vocabulary, which defines concepts more precisely than natural language; it provides a mechanism to automatically compute data quality measures; and is reusable across domains and use cases. A detailed example is presented to demonstrate its utility. The DQ ontology can make data validation more common and reproducible.

Journal ArticleDOI
TL;DR: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns.
Abstract: Background: It is time-consuming to build an ontology with many terms and axioms. Thus it is desired to automate the process of ontology development. Ontology Design Patterns (ODPs) provide a reusable solution to solve a recurrent modeling problem in the context of ontology engineering. Because ontology terms often follow specific ODPs, the Ontology for Biomedical Investigations (OBI) developers proposed a Quick Term Templates (QTTs) process targeted at generating new ontology classes following the same pattern, using term templates in a spreadsheet format. Results: Inspired by the ODPs and QTTs, the Ontorat web application is developed to automatically generate new ontology terms, annotations of terms, and logical axioms based on a specific ODP(s). The inputs of an Ontorat execution include axiom expression settings, an input data file, ID generation settings, and a target ontology (optional). The axiom expression settings can be saved as a predesigned Ontorat setting format text file for reuse. The input data file is generated based on a template file created by a specific ODP (text or Excel format). Ontorat is an efficient tool for ontology expansion. Different use cases are described. For example, Ontorat was applied to automatically generate over 1,000 Japan RIKEN cell line cell terms with both logical axioms and rich annotation axioms in the Cell Line Ontology (CLO). Approximately 800 licensed animal vaccines were represented and annotated in the Vaccine Ontology (VO) by Ontorat. The OBI team used Ontorat to add assay and device terms required by ENCODE project. Ontorat was also used to add missing annotations to all existing Biobank specific terms in the Biobank Ontology. A collection of ODPs and templates with examples are provided on the Ontorat website and can be reused to facilitate ontology development. Conclusions: With ever increasing ontology development and applications, Ontorat provides a timely platform for generating and annotating a large number of ontology terms by following design patterns. Availability: http://ontorat.hegroup.org/

Proceedings ArticleDOI
17 Dec 2015
TL;DR: This paper aligned the Haystack tagging ontology with the wide-spreaded Semantic Sensor Network upper ontology and designed a configuration environment for Building Automation systems based on semantic data to illustrate, so as to discuss the added-value of semantics in automation.
Abstract: Modeling devices has become a crucial task in the Internet of Things (IoT) and Semantic Web technologies are seen as a promising tool for this purpose. However, as it may be arduous to manipulate semantic models, industrial solutions often re-define non-standard, simplified semantics. This is the case with Project Haystack, a framework to tag devices with labels from a predefined vocabulary in the field of Building Automation. In order to make Project Haystack standard and fully semantic, we wrapped its vocabulary in an ontology. In this paper, we present the general strucure of this ontology, along with a method to turn tag sets into a Semantic Web model and back. The whole results in a reusable ontology design pattern. We aligned our Haystack tagging ontology with the wide-spreaded Semantic Sensor Network upper ontology and we designed a configuration environment for Building Automation systems based on semantic data to illustrate, so as to discuss the added-value of semantics in automation.

Journal ArticleDOI
TL;DR: The recently built process ontology contributes to enrich semantically the terms for the (previously developed) measurement and evaluation domain ontology by means of stereotypes, and the augmented conceptual framework impacts on the verifiability of GOCAME process and method specifications.
Abstract: In this paper, we specify a generic ontology for the process domain considering the related state-of-the-art research literature. As a result, the recently built process ontology contributes to enrich semantically the terms for the (previously developed) measurement and evaluation domain ontology by means of stereotypes. One of the underlying hypothesis in this research is that the generic ontology for process can be seen as a reusable artifact which can be used to enrich semantically not only the measurement and evaluation domain ontology but also to other domains involved in different organizational endeavors. For instance, for the measurement domain, now is explicit that the measurement term has the semantic of task, the measure term has the meaning of outcome, and the metric term has the semantic of method, from the process terminological base standpoint. The augmented conceptual framework, i.e. measurement and evaluation concepts plus process concepts, has also a positive impact on the GOCAME (Goal-Oriented Context-Aware Measurement and Evaluation) strategy capabilities since ensures terminological uniformity, consistency and verifiability to its process and method specifications. In order to illustrate how the augmented conceptual framework impacts on the verifiability of GOCAME process and method specifications in addition to the consistency and comparability of results in measurement and evaluation projects, an ICT (Information and Communications Technology) security and risk evaluation case study is used.

Journal ArticleDOI
TL;DR: The domain of Sports is considered for creating both Low-level visual ontology for certain sport event images and also for building a high-level domain ontology from the information on web that is integrated using Fuzzy concepts.