Showing papers on "Upper ontology published in 2017"

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.

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.

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

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.

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.

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.

Scholarly Ontology: modelling scholarly practices

Abstract: In this paper we present the Scholarly Ontology (SO), an ontology for modelling scholarly practices, inspired by business process modelling and Cultural-Historical Activity Theory. The SO is based on empirical research and earlier models and is designed so as to incorporate related works through a modular structure. The SO is an elaboration of the domain-independent core part of the NeDiMAH Methods Ontology addressing the scholarly ecosystem of Digital Humanities. It thus provides a basis for developing domain-specific scholarly work ontologies springing from a common root. We define the basic concepts of the model and their semantic relations through four complementary perspectives on scholarly work: activity, procedure, resource and agency. As a use case we present a modelling example and argue on the purpose of use of the model through the presentation of indicative SPRQL and SQWRL queries that highlight the benefits of its serialization in RDFS. The SO includes an explicit treatment of intentionality and its interplay with functionality, captured by different parts of the model. We discuss the role of types as the semantic bridge between those two parts and explore several patterns that can be exploited in designing reusable access structures and conformance rules. Related taxonomies and ontologies and their possible reuse within the framework of SO are reviewed.

A One Category Ontology

Abstract: An ontology is defined by its fundamental categories. Following Peter van Inwagen (2011), take divisions between fundamental categories to mark “real divisions between things” that determine the basic categorical structure of the world. Speaking metaphorically, the fundamental categorical structure of the world carves the world at its fundamental joints. The project of determining the fundamental categorical structure of the world descends (arguably) from Aristotle, who started by dividing the realm of being into at least two fundamental categories: the fundamental category of particulars, or the present-in, and the fundamental category of universals, or the said-of. This gives the world a certain sort of structure, built from things with two different natures, things with the nature of being a bearer of properties or being a particular, and things with the nature of being borne by particulars. This supports a two category ontology. The idea is that the world exhibits this fundamental distinction between natures, a distinction between individuals or bearers and the properties and relations these things bear. The notion of understanding the ontological structure of the world in terms of divisions between natures is also deeply embedded in the Lewisean notion of naturalness, and is extended in Theodore Sider’s (2012) view that the world has fundamental quantificational structure. I defend a one category ontology: an ontology that denies that we need more than one fundamental category to support the ontological structure of the world. Categorical fundamentality is understood in terms of the metaphysically prior, as that in which everything else in the world consists. One category ontologies are deeply appealing, because their ontological simplicity gives them an unmatched elegance and spareness. I’m a fan of a one category ontology that collapses the distinction between particular and property, replacing it with a single fundamental category of intrinsic characters or qualities. We may describe the qualities as qualitative characters

Evaluating the effect of annotation size on measures of semantic similarity

Abstract: Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products. Here, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of annotations of entities, difference in annotation size and to the depth or specificity of annotation classes. We find that most similarity measures are sensitive to the number of annotations of entities, difference in annotation size as well as to the depth of annotation classes; well-studied and richly annotated entities will usually show higher similarity than entities with only few annotations even in the absence of any biological relation. Our findings may have significant impact on the interpretation of results that rely on measures of semantic similarity, and we demonstrate how the sensitivity to annotation size can lead to a bias when using semantic similarity to predict protein-protein interactions.

A geo-ontology-based approach to decision-making in emergency management of meteorological disasters

Abstract: Ontology as a kind of method for knowledge representation is able to provide semantic integration for decision support in emergency management activities of meteorological disasters. We examine a meteorological disaster system as composed of four components: disastrous meteorological events, hazard-inducing environments, hazard-bearing bodies, and emergency management. The geospatial characteristics of these components can be represented with geographical ontology (geo-ontology). In this paper, we propose an ontology representation of domain knowledge of a meteorological disaster system descending from an adapted geospatial foundation ontology, designed to formally conceptualize the domain terms and establish relationships between those concepts. The class hierarchy and relationships of the proposed ontology are implemented finally at top level, domain level/task level, and application level. The potential application of the ontology is illustrated with a case study of prediction of secondary disasters and evacuation decision of a typhoon event. The multi-level ontology model can provide semantic support for before-, during-, after-event emergency management activities such as risk assessment, resource preparedness, and emergency response where the formed concepts and their relationships can be incorporated into reasoning sentences of these decision processes. Furthermore, the ontology model is realized with a universally used intermediate language OWL, which enables it to be used in popular environments. This work will underlie the semantic integration among human beings, between heterogeneous systems and between human beings and systems, enable spatial semantic reasoning, and will be useful in guiding advanced decision support in emergency management of meteorological disasters.

A segment-based approach for large-scale ontology matching

Abstract: The most ground approach to solve the ontology heterogeneous problem is to determine the semantically identical entities between them, so-called ontology matching. However, the correct and complete identification of semantic correspondences is difficult to achieve with the scale of the ontologies that are huge; thus, achieving good efficiency is the major challenge for large- scale ontology matching tasks. On the basis of our former work, in this paper, we further propose a scalable segment-based ontology matching framework to improve the efficiency of matching large-scale ontologies. In particular, our proposal first divides the source ontology into several disjoint segments through an ontology partition algorithm; each obtained source segment is then used to divide the target ontology by a concept relevance measure; finally, these similar ontology segments are matched in a time and aggregated into the final ontology alignment through a hybrid Evolutionary Algorithm. In the experiment, testing cases with different scales are used to test the performance of our proposal, and the comparison with the participants in OAEI 2014 shows the effectiveness of our approach.

The European Legal Taxonomy Syllabus: A multi-lingual, multi-level ontology framework to untangle the web of European legal terminology

Abstract: The final publication is available at IOS Press through http://dx.doi.org/10.3233/AO-170174. This paper describes a new concept of legal ontology together with an ontology development tool, called European Legal Taxonomy Syllabus (ELTS). The tool is used to model the legal terminology created by the Uniform Terminology project on EU consumer protection law as an ontology. ELTS is not a formal ontology in the standard sense, i.e., an axiomatic ontology formalized, for instance, in description logic. Rather, it is a lightweight ontology, i.e. a knowledge base storing low-level legal concepts, connected via low-level semantic relations, and related to linguistic patterns that denote legal concepts in several languages spoken in the European Union (EU). In other words, ELTS is a multi-lingual and multi-jurisdictional terminological vocabulary enriched with concepts denoted by vocabulary entries, with semantic relations between different concepts. The choice of such an architecture is based on past studies in comparative law and is motivated by the need to reveal the differences between national systems within the EU. Past literature in comparative law highlights that axiomatic ontologies freeze legal knowledge in an unreal steadiness, i.e., they render it disconnected from legal practice. Much more flexibility is needed to make the knowledge base acceptable to legal practitioners. ELTS was developed together with legal practitioners on the basis of the comparative view of European law. The ontology framework is designed to help professionals study the meaning of national and European legal terms and how they inter-relate in the transposition of European Directives into national laws. The structure and user interface of ELTS is suitable for building multi-lingual, multi-jurisdictional legal ontologies in a bottom-up and collaborative manner, starting from the description of legal terms by legal experts. It also takes into account the interpretation of norms, the dynamic character of norms and the contextual character of legal concepts in that they are linked to their legal sources (legislation, case law and doctrine).

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

Abstract: In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

Ontology-based information extraction for juridical events with case studies in Brazilian legal realm

Abstract: The number of available legal documents has presented an enormous growth in recent years, and the digital processing of such materials is prompting the necessity of systems that support the automatic relevant information extraction. This work presents a system for ontology-based information extraction from natural language texts, able to identify a set of legal events. The system is based on an innovative methodology based on domain ontology of legal events and a set of linguistic rules, integrated through inference mechanism, resulting in a flexible approach and scalable approach. A case study with the use of documents from the Superior Court in Brazil is related, with satisfactory results in precision and recall.

Multi-layer ontology based information fusion for situation awareness

Abstract: Originated from the military domain, Situation Awareness (SAW) is proposed with the aim to obtain information superiority through information fusion and thus to achieve decision superiority. It requires not only the perception of the environment, but also the reasoning of the implicit or implicated meaning under the explicit phenomenon. The principal goal of this paper is to exploit the semantic web technologies to enhance the situation awareness through autonomous information fusion and inference. Recently, ontology has played a significant role in the representation and integration of domain knowledge for high-level reasoning. The multi-level ontology merging paradigm is followed in this work for the conceptual modeling and knowledge representation. Firstly, Military Scenario Ontology (MSO) and Battle Management Ontology (BMO) are defined according to corresponding reputable standards as the domain ontology. We propose the Situation Awareness Ontology (SAO) as the core ontology to integrate MSO, BMO and even other publicly defined ontology for higher-level information fusion. The SAO is composed of objects representations, relations and events that are necessary to capture the information for further cognition, reasoning and decision-making about the situation evolving over time. Military doctrines and domain knowledge are expressed as Horn clause type rules for reasoning and inference. Multi-layered semantic information fusion that integrates ontologies, semantic web technologies and rule-based reasoning can therefore be conducted. An experimental scenario is presented to demonstrate the feasibility of this architecture.

Ontology based heterogeneous materials database integration and semantic query

Abstract: Materials digital data, high throughput experiments and high throughput computations are regarded as three key pillars of materials genome initiatives. With the fast growth of materials data, the integration and sharing of data is very urgent, that has gradually become a hot topic of materials informatics. Due to the lack of semantic description, it is difficult to integrate data deeply in semantic level when adopting the conventional heterogeneous database integration approaches such as federal database or data warehouse. In this paper, a semantic integration method is proposed to create the semantic ontology by extracting the database schema semi-automatically. Other heterogeneous databases are integrated to the ontology by means of relational algebra and the rooted graph. Based on integrated ontology, semantic query can be done using SPARQL. During the experiments, two world famous First Principle Computational databases, OQMD and Materials Project are used as the integration targets, which show the av...