Showing papers on "Ontology-based data integration published in 2017"

Understanding Data Heterogeneity in the Context of Cyber-Physical Systems Integration

Abstract: The current gradual adoption of the Industry 4.0 is the research trend that includes more intensive utilization of cyber-physical systems (CPSs). The computerization of manufacturing will bring many advantages but it is needed to face the heterogeneity problem during an integration of various CPSs for enabling this progress. In this paper, we describe various types of heterogeneity with emphasis to a semantic heterogeneity. The CPSs integration problem is classified into two different challenges. Next, we introduce the approach and the implementation of the semantic heterogeneity reduction with the focus on using Semantic Web technologies for a data integration. Then, the Big Data approach is described for facilitating the implementation. Finally, the possible solution is demonstrated on our proposed semantic Big Data historian.

Data Integration: After the Teenage Years

Abstract: The field of data integration has expanded significantly over the years, from providing a uniform query and update interface to structured databases within an enterprise to the ability to search, ex- change, and even update, structured or unstructured data that are within or external to the enterprise. This paper describes the evolution in the landscape of data integration since the work on rewriting queries using views in the mid-1990's. In addition, we describe two important challenges for the field going forward. The first challenge is to develop good open-source tools for different components of data integration pipelines. The second challenge is to provide practitioners with viable solutions for the long-standing problem of systematically combining structured and unstructured data.

Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration.

Abstract: Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies.

Distance learning techniques for ontology similarity measuring and ontology mapping

Abstract: Recent years, a large amount of ontology learning algorithms have been applied in different disciplines and engineering. The ontology model is presented as a graph and the key of ontology algorithms is similarity measuring between concepts. In the learning frameworks, the information of each ontology vertex is expressed as a vector, thus the similarity measuring can be determined via the distance of the corresponding vector. In this paper, we study how to get an optimal distance function in the ontology setting. The tricks we presented are divided into two parts: first, the ontology distance learning technology in the setting that the ontology data have no labels; then, the distance learning approaches in the setting that the given ontology data are carrying real numbers as their labels. The result data of the four simulation experiments reveal that our new ontology trick has high efficiency and accuracy in ontology similarity measure and ontology mapping in special engineering applications.

Probabilistic ontology based activity recognition in smart homes using Markov Logic Network

Abstract: Designing an activity recognition system that models various activities of an occupant is the fundamental task in creating a smart home. Activity Recognition (AR) modeling, has witnessed a comprehensive range of research, that focuses independently on probabilistic approaches and on ontology based models as well. The research presented in this paper introduces an innovative approach in AR system design that integrates probabilistic inference with the represented domain ontology. Data obtained from sensors are uncertain in nature and mapping uncertainty over ontology will not yield good accuracy in the context of AR. The proposed system augments ontology based activity recognition with probabilistic reasoning through Markov Logic Network (MLN) which is a statistical relational learning approach. The proposed system utilizes the model theoretic semantic property of description logic, to convert the represented ontology activity model to its corresponding first order rules. MLN is constructed by learning weighted first order rules that enable probabilistic reasoning within a knowledge representation framework. The experiments based on datasets obtained from smart home prototypes illustrate the effectiveness of integrating probabilistic reasoning over domain ontology and the result analysis shows enhanced recognition accuracy in comparison with existing approaches.

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation

Abstract: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).

An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

Abstract: Geographic Object-Based Image Analysis (GEOBIA) techniques have become increasingly popular in remote sensing. GEOBIA has been claimed to represent a paradigm shift in remote sensing interpretation. Still, GEOBIA—similar to other emerging paradigms—lacks formal expressions and objective modelling structures and in particular semantic classification methods using ontologies. This study has put forward an object-based semantic classification method for high resolution satellite imagery using an ontology that aims to fully exploit the advantages of ontology to GEOBIA. A three-step workflow has been introduced: ontology modelling, initial classification based on a data-driven machine learning method, and semantic classification based on knowledge-driven semantic rules. The classification part is based on data-driven machine learning, segmentation, feature selection, sample collection and an initial classification. Then, image objects are re-classified based on the ontological model whereby the semantic relations are expressed in the formal languages OWL and SWRL. The results show that the method with ontology—as compared to the decision tree classification without using the ontology—yielded minor statistical improvements in terms of accuracy for this particular image. However, this framework enhances existing GEOBIA methodologies: ontologies express and organize the whole structure of GEOBIA and allow establishing relations, particularly spatially explicit relations between objects as well as multi-scale/hierarchical relations.

Semantic Similarity in the Gene Ontology.

Abstract: Gene Ontology-based semantic similarity (SS) allows the comparison of GO terms or entities annotated with GO terms, by leveraging on the ontology structure and properties and on annotation corpora. In the last decade the number and diversity of SS measures based on GO has grown considerably, and their application ranges from functional coherence evaluation, protein interaction prediction, and disease gene prioritization.Understanding how SS measures work, what issues can affect their performance and how they compare to each other in different evaluation settings is crucial to gain a comprehensive view of this area and choose the most appropriate approaches for a given application.In this chapter, we provide a guide to understanding and selecting SS measures for biomedical researchers. We present a straightforward categorization of SS measures and describe the main strategies they employ. We discuss the intrinsic and external issues that affect their performance, and how these can be addressed. We summarize comparative assessment studies, highlighting the top measures in different settings, and compare different implementation strategies and their use. Finally, we discuss some of the extant challenges and opportunities, namely the increased semantic complexity of GO and the need for fast and efficient computation, pointing the way towards the future generation of SS measures.

From reference ontologies to ontology patterns and back

Abstract: Building proper reference ontologies is a hard task. There are a number of methods and tools that traditionally have been used to support this task. These include the use of foundational theories, the reuse of domain and core ontologies, the adoption of development methods, as well as the support of proper software tools. In this context, an approach that has gained increasing attention in recent years is the systematic application of ontology patterns . However, a pattern-based approach to ontology engineering requires: the existence of a set of suitable patterns that can be reused in the construction of new ontologies; a proper methodological support for eliciting these patterns, as well as for applying them in the construction of these new models. The goal of this paper is twofold: (i) firstly, we present an approach for deriving conceptual ontology patterns from ontologies. These patterns are derived from ontologies of different generality levels, ranging from foundational to domain ontologies; (ii) secondly, we present guidelines that describe how these patterns can be applied in combination for building reference domain ontologies in a reuse-oriented process. In summary, this paper is about the construction of ontology patterns from ontologies, as well as the construction of ontologies from ontology patterns.

Collaborative ontology matching based on compact interactive evolutionary algorithm

Abstract: Ontology is the kernel technology of semantic web, which plays a prominent role for achieving inter-operability across heterogeneous systems and applications by formally describing the semantics of data that characterize a particular application domain However, different ontology engineers might have potentially opposing world views which could yield the different descriptions on the same ontology entity, raising so called ontology heterogeneous problem Ontology matching, which aims at identifying the correspondences between the entities of heterogeneous ontologies, is recognized as an effective technology to solve the ontology heterogeneous problem Due to the complexity of ontology matching process, ontology alignments generated by the automatic ontology matchers should be validated by the users to ensure their qualities, and the technology that makes multiple users collaborate with each other to help the automatic tool create high quality matchings in a reasonable amount of time is called collaborative ontology matching Such a collaborative ontology matching poses a new challenge of how to reduce users’ workload, but at the same time, increase their involvement’s value To address this challenge, in this paper, we propose a Compact Interactive Memetic Algorithm (CIMA) based collaborative ontology matching technology, which can reduce users’ workload by adaptively determining the time of getting users involved, presenting the most problematic correspondences for users and helping users to automatic validate multiple conflict mappings, and increase user involvement’s value by propagating the collaborative validation and decreasing the negative effect brought by the error user validations The experimental results show that our proposal is able to efficiently exploit the collaborative validation to improve its non-interactive version, and the runtime and alignment quality of our approach both outperform state-of-the-art interactive ontology matching systems under different user error rate cases

Minimizing conservativity violations in ontology alignments: algorithms and evaluation

Abstract: In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings lead to undesired logical consequences, their usefulness may be diminished. In this paper, we present an approach to detect and minimize the violations of the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. The practical applicability of the proposed approach is experimentally demonstrated on the datasets from the Ontology Alignment Evaluation Initiative.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

Abstract: Background: Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. Objective: The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Methods: The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. Results: We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression. Conclusions: The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology.

Planned ETSI SAREF Extensions based on the W3C&OGC SOSA/SSN-compatible SEAS Ontology Paaerns

Abstract: Mid-June 2017, the ETSI SmartM2M working group voted two work items, DTS/SmartM2M-103548 and DTS/SmartM2M-103549, with the goal to enhance and augment the SAREF ontology with some of the design, development, and publication choices that have been made in the context of the ITEA2 SEAS (Smart Energy Aware Systems) project. This paper provides an overview of these choices and their rationale. In particular, we describe contributions regarding: (i) the design of the ontology as a set of simple core ontology patterns , that can then be instantiated for multiple engineering-related verticals; (ii) the design and publication of the SEAS modular and versioned ontology in conformance with the publication and meta-data best practices, with the additional constraint that every term is deened under a single namespace. These planned additions to SAREF will ease its adoption and extension by industrial stake-holder, while ensuring easy maintenance of its quality, coherence, and modularity. Finally, because the SEAS ontology generalizes the future W3C&OGC SOSA/SSN (Sensor, Observation, Sensing, Actuation / Semantic Sensor Network) ontology, these work items contribute to the convergence of the diierent reference ontologies relevant for the IoT domain.

Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Abstract: Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users' technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users' understanding. However, on the one hand, today the most of world's enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity--known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems.

Classification Methods of Text Documents Using Ontology Based Approach

Abstract: This article discusses an approach to classification of text documents using ontological approach. The method of text documents categorization based on metrics, which uses the rubric ontology specificity, is built.

Multiple Choice Question Generation Utilizing An Ontology

Abstract: Ontologies provide a structured representation of concepts and the relationships which connect them. This work investigates how a pre-existing educational Biology ontology can be used to generate useful practice questions for students by using the connectivity structure in a novel way. It also introduces a novel way to generate multiple-choice distractors from the ontology, and compares this to a baseline of using embedding representations of nodes. An assessment by an experienced science teacher shows a significant advantage over a baseline when using the ontology for distractor generation. A subsequent study with three science teachers on the results of a modified question generation algorithm finds significant improvements. An in-depth analysis of the teachers’ comments yields useful insights for any researcher working on automated question generation for educational applications.

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Abstract: Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE.

New ways of ontology

Abstract: Contemporary philosophy has reasserted the belief that philosophy has practical tasks. This turn reflects an understanding that the life of the individual and the community is not molded merely by personal needs and fortunes but also by the strength of dominant ideas. For Nicolai Hartmann, ideas are spiritual powers belonging to the realm of thought, but thought has its own strict discipline and critique of events. In his view, theory must include within its scope problems of the contemporary world and cooperation in work that needs doing.New Ways of Ontology stands in opposition to the tradition of Heidegger. With deep appreciation of the history of philosophical controversy, Hartmann divides mistakes of the old ontology into those related to its method and those concerning its content. Hartmann finds a common mistake behind methodological approaches inspired by late German romanticism in attempts to develop a complete systematic account of the categories of being—not only of the ideal, but of real being.The main task of New Ways of Ontology is to reveal and analyze interdependences and interconnections. The divisions of being and becoming, of the separation of existence and essence, as well as the old view that the real and the ideal exclude each other, require revision. For Hartmann, whose ideas take us close to modern social science research, ontology is the neutral category that includes subject and object, and gets beyond old realism and modern idealism alike.

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

Abstract: It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.