Leo Obrst

Ontologies for semantically interoperable systems

In this paper, we discuss the use of ontologies for semantic interoperability and integration. We argue that information technology has evolved into a world of largely loosely coupled systems and as such, needs increasingly more explicit, machine-interpretable semantics. Ontologies in the form of logical domain theories and their knowledge bases offer the richest representations of machine-interpretable semantics for systems and databases in the loosely coupled world, thus ensuring greater semantic interoperability and integration. Finally, we discuss how ontologies support semantic interoperability in the real, commercial and governmental world.

The Evaluation of Ontologies

Recent years have seen rapid progress in the development of ontologies as semantic models intended to capture and represent aspects of the real world. There is, however, great variation in the quality of ontologies. If ontologies are to become progressively better in the future, more rigorously developed, and more appropriately compared, then a systematic discipline of ontology evaluation must be created to ensure quality of content and methodology. Systematic methods for ontology evaluation will take into account representation of individual ontologies, performance (in terms of accuracy, domain coverage and the efficiency and quality of automated reasoning using the ontologies) on tasks for which the ontology is designed and used, degree of alignment with other ontologies and their compatibility with automated reasoning. A sound and systematic approach to ontology evaluation is required to transform ontology engineering into a true scientific and engineering discipline. This chapter discusses issues and problems in ontology evaluation, describes some current strategies, and suggests some approaches that might be useful in the future.

Ontologies for corporate web applications

In this article, we discuss some issues that arise when ontologies are used to support corporate application domains such as electronic commerce (e-commerce) and some technical problems in deploying ontologies for real-world use. In particular, we focus on issues of ontology integration and the related problem of semantic mapping, that is, the mapping of ontologies and taxonomies to reference ontologies to preserve semantics. Along the way, we discuss what typically constitutes an ontology architecture. We situate the discussion in the domain of business-to-business (B2B) e-commerce. By its very nature, B2B e-commerce must try to interlink buyers and sellers from multiple companies with disparate product-description terminologies and meanings, thus serving as a paradigmatic case for the use of ontologies to support corporate applications.

Ontological engineering for B2B E-commerce

In this paper we discuss the nature of our overall enterprise tocreate ontologies in the product and service knowledge space forBusiness-to-Business (B2B) electronic commerce. We describe onecrucial problem: the mapping problem, i.e., mapping amongontologies, taxonomies, and classification systems, some of whichare more semantically sound and coherent than others. This problemwe consider to be in need of a sustained research program iftenable solutions are to be found, since the lack of a solutionwill preclude widespread adoption of ontologies by the commercialworld. Finally, we summarize the general issues we faced andindicate prospective future research.

Translating owl and semantic web rules into prolog: Moving toward description logic programs

We are researching the interaction between the rule and the ontology layers of the Semantic Web, by comparing two options: 1) using OWL and its rule extension SWRL to develop an integrated ontology/rule language, and 2) layering rules on top of an ontology with RuleML and OWL. Toward this end, we are developing the SWORIER system, which enables efficient automated reasoning on ontologies and rules, by translating all of them into Prolog and adding a set of general rules that properly capture the semantics of OWL. We have also enabled the user to make dynamic changes on the fly, at run time. This work addresses several of the concerns expressed in previous work, such as negation, complementary classes, disjunctive heads, and cardinality, and it discusses alternative approaches for dealing with inconsistencies in the knowledge base. In addition, for efficiency, we implemented techniques called extensionalization, avoiding reanalysis, and code minimization.

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

Towards ontology evaluation across the life cycle: The Ontology Summit 2013

The goal of the Ontology Summit 2013 was to create guidance for ontology developers and users on how to evaluate ontologies. Over a period of four months a variety of approaches were discussed by participants, who represented a broad spectrum of ontology, software, and system developers and users. We explored how established best practices in systems engineering and in software engineering can be utilized in ontology development.

Unpacking the semantics of source and usage to perform semantic reconciliation in large-scale information systems

Semantic interoperability is a growing challenge in the United States Department of Defense (DoD). In this paper, we describe the basis of an infrastructure for the reconciliation of relevant, but semantically heterogeneous attribute values. Three types of information are described which can be used to infer the context of attributes, making explicit hidden semantic conflicts and making it possible to adjust values appropriately. Through an extended example, we show how an automated integration agent can derive the transformations necessary to perform four tasks in a simple semantic reconciliation.

Ontology Summit 2007 - Ontology, taxonomy, folksonomy: Understanding the distinctions

Under the appellation of “ontology” are found many different types of artifacts created and used in different communities to represent entities and their relationships for purposes including annotating datasets, supporting natural language understanding, integrating information sources, semantic interoperability and to serve as a background knowledge in various applications. The Ontology Summit 2007 “Ontology, taxonomy, folksonomy: Understanding the distinctions”,1 was an attempt to bring together various communities (computer scientists, information scientists, philosophers, domain experts) having a different understanding of what is an ontology, and to foster dialog and cooperation among these communities. In practice, ontologies cover a spectrum of useful artifacts, from formal upper-level ontologies expressed in first order logic, such as Basic Formal Ontology (BFO), Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), Suggested Upper Merged Ontology (SUMO), and Process Specification Language (PSL), to folksonomies (the simple lists of user-defined keywords to annotate resources on the Web). In between these two extremities of the ontology spectrum are taxonomies, conceptual models and controlled vocabularies such as Medical Subject Headings (MeSH), often used for information indexing and retrieval, and whose organization is mostly hierarchical. Finally, there are ontologies which represent not only subsumption, but also other kinds of relationships among entities (e.g., functional, physical), often based on formalisms such as frames or description logics. Examples of such ontologies in the biomedical domain include the Foundational Model of Anatomy, SNOMED CT and the NCI Thesaurus. The goal of the Ontology Summit was not to establish a definitive definition of the word “ontology”, which has proven to be extremely challenging due to the diversity of artifacts it can refer to. Rather, the results of the Summit identified a limited number of key dimensions along which ontologies can

The Interplay Between Ontology as Categorial Analysis and Ontology as Technology

The notion of ontology today comes with two perspectives: one traditionally from philosophy and one more recently from computer science. The philosophical perspective of ontology focuses on categorial analysis, i.e., what are the entities of the world and what are the categories of entities? Prima facie, the intention of categorial analysis is to inventory reality. The computer science perspective of ontology, i.e., ontology as technology, focuses on those same questions but the intention is distinct: to create engineering models of reality, artifacts which can be used by software, and perhaps directly interpreted and reasoned over by special software called inference engines, to imbue software with human level semantics. Philosophical ontology arguably begins with the Greek philosophers, more than 2,400 years ago. Computational ontology (sometimes called “ontological” or “ontology” engineering) began about 15 years ago.