Gary Berg-Cross

A Geo-ontology Design Pattern for Semantic Trajectories

Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.

Semantic Web and Big Data meets Applied Ontology: The Ontology Summit 2014

Leo Obrst a,∗, Michael Gruninger b, Ken Baclawski c, Mike Bennett d, Dan Brickley e, Gary Berg-Cross f, Pascal Hitzler g, Krzysztof Janowicz h, Christine Kapp i, Oliver Kutz j, Christoph Lange k, Anatoly Levenchuk l, Francesca Quattri m, Alan Rector n, Todd Schneider o, Simon Spero p, Anne Thessen q, Marcela Vegetti r, Amanda Vizedom s, Andrea Westerinen t, Matthew West u and Peter Yim v a The MITRE Corporation, McLean, VA, USA b University of Toronto, Toronto, Canada c Northeastern University, Boston, MA, USA d Hypercube Ltd., London, UK e Google, London, UK f Knowledge Strategies, Washington, DC, USA g Wright State University, Dayton, OH, USA h University of California, Santa Barbara, Santa Barbara, CA, USA i JustIntegration, Inc., Kissimmee, FL, USA j Otto von Guericke University Magdeburg, Magdeburg, Germany k University of Bonn, Bonn, Germany; Fraunhofer IAIS, Sankt Augustin, Germany l TechInvestLab.ru, Moscow, Russia m The Hong Kong Polytechnic University, Hong Kong n University of Manchester, Manchester, UK o PDS, Inc., Arvada, CO, USA p University of North Carolina, Chapel Hill, NC, USA q Arizona State University, Phoenix, AZ, USA r INGAR (CONICET/UTN), Santa Fe, Argentina s Criticollab, LLC, Durham, NC, USA t Nine Points Solutions, LLC, Potomac, MD, USA u Information Junction, Fareham, UK v CIM Engineering, Inc., San Mateo, CA, USA

Internet of Things: Toward Smart Networked Systems and Societies

Mark Underwood a, Michael Gruninger b, Leo Obrst c,∗, Ken Baclawski d, Mike Bennett e, Gary Berg-Cross f, Torsten Hahmann g and Ram Sriram h a Krypton Brothers, Port Washington, NY, USA b University of Toronto, Toronto, Canada c The MITRE Corporation, McLean, VA, USA d Northeastern University, Boston, MA, USA e Hypercube Ltd, London, UK f Knowledge Strategies, Washington, DC, USA g University of Maine, Orono, ME, USA h National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA

Ontology Summit 2017 communiqué – AI, learning, reasoning and ontologies

There are many connections among artificial intelligence, learning, reasoning and ontologies. The Ontology Summit 2017 explored, identified and articulated the relationships among these areas. As part of the general advocacy of the Ontolog Forum to bring ontology science and engineering into the mainstream, we endeavored to abstract a conversational toolkit from the Ontology Summit sessions that may facilitate discussion and knowledge sharing amongst stakeholders concerned with the topic. Our findings are supported with examples from the various domains of interest. The results were captured in the form of this Communiqué, with expanded supporting material provided on the web.

SSO 2011 Workshop Report: 1st ACM SIGSPATIAL International Workshop on Spatial Semantics and Ontologies (SSO) 2011: (Chicago, IL - November 1, 2011)

We were pleased to organize the 1st Spatial Semantics and Ontologies (SSO) international workshop as part of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2011). Semantic technologies are an emerging topic. This workshop was motivated by the need for semantics-related research for geospatial data and to provide a place for discussion and distribution of the work in this area. ACM SIGSPATIAL GIS 2011 offered a perfect venue to bring together computer scientists with spatial/geospatial domain experts, and this workshop was designed to help engage and create a synergy with computer and spatial scientists interested in the semantic area. We anticipated that technical advancements would be made by computer specialists focusing on the spatial area in collaboration with domain specialists who know the semantic problems they face. Having this workshop at ACM GIS opened the research to include wide areas of computer science.

Panel Discussion on Ontologies

A discussion on the topic of ontologies in agent-based systems had Drs. Sidney Bailin, Gary Berg-Cross, and Tim Finin as panel members. Bailin, who chaired the session, led off with a series of questions for consideration: 1. Can we properly speak of an ontology (an artifact, as opposed to just ontology as a field of study)? Can we properly speak of multiple ontologies? 2. Is an ontology different from a dictionary? A taxonomy? A class hierarchy? A domain model? 3. Will the semantic web be an ontology? 4. Are ontologies needed now more than ever? Bailin answered his first question by noting that his Microsoft Word grammar checker continually objects to the phrase an ontology as well as to the plural ontologies. Nevertheless, he finds it impossible to do without these constructions given current conventions. He noted that ontologies have characteristics in common with dictionaries, taxonomies, class hierarchies, and domain models, and that the term is sometimes used synonymously with one or another of these alternates. It tends to connote things that the alternates do not imply, however. Ontologies typically have more structure than dictionaries, e.g., in their arrangement of concepts into an is-a hierarchy (that is, into a taxonomy). They may have more precise semantics than taxonomies, e.g., by identifying attributes associated with a given concept, and possibly rules governing the values that the attributes assume. They are, in this respect, similar to class hierarchies, but they tend to be used at the problem-domain level while classes are often viewed as solution domain (i.e., design or implementation) constructs. In this respect they are like domain models; but a domain model may contain, in addition, a variety of other kinds of information. One participant pointed out that a domain model may use an ontology.