The Distributed Ontology Language (DOL): Use Cases, Syntax, and Extensibility

The Distributed Ontology Language (DOL) is currently being standardized within the OntoIOp (Ontology Integration and Interoperability) activity of ISO/TC 37/SC 3. It aims at providing a unified framework for (1) ontologies formalized in heterogeneous logics, (2) modular ontologies, (3) links between ontologies, and (4) annotation of ontologies. This paper presents the current state of DOL's standardization. It focuses on use cases where distributed ontologies enable interoperability and reusability. We demonstrate relevant features of the DOL syntax and semantics and explain how these integrate into existing knowledge engineering environments.Comment: Terminology and Knowledge Engineering Conference (TKE) 2012-06-20 to 2012-06-21 Madrid, Spai

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

Benefits and challenges of applying Semantic Web Services in the e-Government domain

Joining up services in e-Government usually implies governmental agencies acting in concert without a central control regime. This requires the sharing of scattered and heterogeneous data. Semantic Web Service (SWS) technology can help to integrate, mediate and reason between these datasets. However, since few real-world applications have been developed, it is still unclear which are the actual benefits and issues of adopting such a technology in the e-Government domain. In this paper, we contribute to raising awareness of the potential benefits in the e-Government community by analyzing motivations, requirements, and expected results, before proposing a reusable SWS-based framework. We demonstrate the application of this framework by a compelling use case: a GIS-based emergency planning system. We illustrate the obtained benefits and the key challenges which remain to be addressed

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

NSF-CNPq Collaborative Research on Integrating Geospatial Information

Under this project, researchers at the University of Maine and the Brazilian National Institute for Space Research (INPE) are collaborating to study new models for integrating geographic information. The two research teams complement each other, providing a unique synergy in conceptual modeling and systems development. Long-term visits by Brazilian researchers assure a tight cooperation, and provide for integrated research and education. The work focuses on the semantics of spatial data collections that are stored in geographic information systems or spatial databases. Such collections often have diverse database schemas and lack conventions that would make them easily compatible, so that they could be combined to perform an integrated analysis. The goal of this research is to develop computational models that will allow us to compare, harmonize, and integrate geographic information across different ontologies and different spatial data models. The approach consists of a new integrated method to assess similarity based on common parts, functions, and attributes. In these similarity assessments, different contexts are considered through the operations a user intents to perform with the harmonized data. The results of this project will provide meaningful comparisons and integration of geospatial information, enabling better interoperation among geographic information systems. http://www.spatial.maine.edu/~max/CNPq.htm

An Ontology-Based Expert System for the Systematic Design of Humanoid Robots

Die Entwicklung humanoider Roboter ist eine zeitaufwendige, komplexe und herausfordernde Aufgabe. Daher stellt diese Thesis einen neuen, systematischen Ansatz vor, der es erlaubt, Expertenwissen zum Entwurf humanoider Roboter zu konservieren, um damit zukünftige Entwicklungen zu unterstützen. Der Ansatz kann in drei aufeinanderfolgende Schritte unterteilt werden. Im ersten Schritt wird Wissen zum Entwurf humanoider Roboter durch die Entwicklung von Roboterkomponenten und die Analyse verwandter Arbeiten gewonnen. Dieses Wissen wird im zweiten Schritt formalisiert und in Form einer ontologischen Wissensbasis gespeichert. Im letzten Schritt wird diese Wissensbasis von einem Expertensystem verwendet, um Lösungsvorschläge zum Entwurf von Roboterkomponenten auf Grundlage von Benutzeranforderungen zu generieren. Der Ansatz wird anhand von Fallstudien zu Komponenten des humanoiden Roboters ARMAR-6 evaluiert: Sensor-Aktor-Controller-Einheiten für Robotergelenke und Roboterhände

A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

Sensors play an increasingly critical role in capturing and distributing observations of phenomena in our environment. The vision of the semantic sensor web is to enable the interoperability of various applications that use sensor data provided by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping and that can adapt to the dynamic metadata of sensors are required. Semantic mapping for the sensor web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few tasks. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial, and temporal elements that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support the resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings

Microtheories for SDI - Accounting for diversity of local conceptualisations at a global level

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.The categorization and conceptualization of geographic features is fundamental to cartography, geographic information retrieval, routing applications, spatial decision support and data sharing in general. However, there is no standard conceptualization of the world. Humans conceptualize features based on numerous factors including cultural background, knowledge, motivation and particularly space and time. Thus, geographic features are prone to multiple, context-dependent conceptualizations reflecting local conditions. This creates semantic heterogeneity and undermines interoperability. Standardization of a shared definition is often employed to overcome semantic heterogeneity. However, this approach loses important local diversity in feature conceptualizations and may result in feature definitions which are too broad or too specific. This work proposes the use of microtheories in Spatial Data Infrastructures, such as INSPIRE, to account for diversity of local conceptualizations while maintaining interoperability at a global level. It introduces a novel method of structuring microtheories based on space and time, represented by administrative boundaries, to reflect variations in feature conceptualization. A bottom-up approach, based on non-standard inference, is used to create an appropriate global-level feature definition from the local definitions. Conceptualizations of rivers, forests and estuaries throughout Europe are used to demonstrate how the approach can improve the INSPIRE data model and ease its adoption by European member states

Geospatial Semantics

Geospatial semantics is a broad field that involves a variety of research areas. The term semantics refers to the meaning of things, and is in contrast with the term syntactics. Accordingly, studies on geospatial semantics usually focus on understanding the meaning of geographic entities as well as their counterparts in the cognitive and digital world, such as cognitive geographic concepts and digital gazetteers. Geospatial semantics can also facilitate the design of geographic information systems (GIS) by enhancing the interoperability of distributed systems and developing more intelligent interfaces for user interactions. During the past years, a lot of research has been conducted, approaching geospatial semantics from different perspectives, using a variety of methods, and targeting different problems. Meanwhile, the arrival of big geo data, especially the large amount of unstructured text data on the Web, and the fast development of natural language processing methods enable new research directions in geospatial semantics. This chapter, therefore, provides a systematic review on the existing geospatial semantic research. Six major research areas are identified and discussed, including semantic interoperability, digital gazetteers, geographic information retrieval, geospatial Semantic Web, place semantics, and cognitive geographic concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova, and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information Systems, Elsevier. Oxford, U

Geovisual Analytics Environment for Supporting the Resilience of Maritime Surveillance System

International audienceThis paper presents an original approach for supporting the resilience in Maritime Domain Awareness, based on geovisual analytics. While many research projects focus on developing rules for detecting anomalies at by automated means, there is no support to visual exploration led by human operators. We investigate the use of visual methods for analyzing mobility data of ships. Behaviors of interest can be known (modeled) or unknown, asking for various ways of visualizing and studying the information. We assume that supporting the use of geovisual analytics will make the exploration and the analysis process easier, reducing the cognitive load of the tasks led by the actors of maritime surveillance. The detection and the identification of threats at sea are improved by using adequate visualization methods, regarding the context of use. Our suggested framework is based on ontologies for maritime domain awareness and geovisual analytics environments, coupled to rules