This special issue of the Applied Artificial Intelligence Journal addresses research issues on ontologies, an area that is receiving increased attention from researchers in diverse fields. Ontologies provide an abstract, simplified view of the world that includes the objects, concepts in a domain of interest, as well as the relationships between them. The use of formal ontologies in knowledge systems has many advantages. It allows for an unambiguous specification of the structure of knowledge in a domain, enables knowledge-sharing and reuse, and consequently, allows for a variety of (automated) reasoning services over ontologies. In recent years there has been a marked increase in the use of ontologies, both in industry and in research laboratories. This special issue presents the recent advances, both in theory and in practical applications, of ontologies to a general audience and provides an opportunity for the broader artificial intelligence community to be kept up to date on the current trends in ontology research.
Submissions for this special issue were based on eight invitations extended to the authors of the best papers presented at the Australasian Ontology Workshop (AOW 2007) and the Knowledge Representation Ontology Workshop (KROW 2008). AOW 2007 was held on 2 December 2007 in conjunction with the 20th Australian Joint Conference on Artificial Intelligence in Surfers Paradise, Queensland, Australia. KROW 2008 was held on 17 September 2008 in Sydney, Australia, as part of the 11th Conference on Principles of Knowledge Representation and Reasoning. Invitations went to the authors of six workshop papers accepted at KROW 2008, with the remaining two invitations going to authors of papers accepted at AOW 2007. The invited authors were from Austria, Italy, Slovakia, Argentina, and Australia and they were asked to submit substantially extended versions of their workshop papers. Each submission was sent to at least two reviewers who are experts in ontology research and closely related areas. Of the eight papers submitted to the special issue, five high quality papers were accepted for publication. For one of the remaining three papers, the consensus was that it had to go through another round of reviewing, with the expectation that it may appear in a future regular issue of Applied Artificial Intelligence.
Both AOW 2007 and KROW 2008 form part of the ongoing annual AOW workshop series, which started in 2005 in conjunction with the 18th Australian Joint Conference on Artificial Intelligence in Sydney, Australia. AOW 2009, the next workshop in the series, will be held in conjunction with the 22nd Australian Joint Conference on Artificial Intelligence on 1 December 2009 in Melbourne, Australia. The purpose of the AOW workshop series is to bring together ontology researchers from academia and industry in the Australasian region for interaction, discussion, sharing of results, the initiation of new projects, and raising the awareness of the Australasian Artificial Intelligence community to state-of-the-art ontology research conducted in the region. This special issue is further testament to the vibrant ontology research conducted in the Australasian region and its strong connections with the international ontology community. We trust that the breadth and diversity of the articles published in this special issue will foster further research on ontologies ranging from theoretical to practical issues and to applications.
The special issue commences with an article on learning with gene ontology annotation using feature selection and construction. In their article, Akand, Bain, and Temple propose an alternative to the standard approach for determining whether gene sets share common function that avoids problems in overrepresentation analysis due to statistical dependencies between ontology categories. They apply methods of feature construction and selection to preprocess the gene ontology terms used for the annotation of gene sets and incorporate these features as input to a standard supervised machine-learning algorithm. The approach is shown to allow the straightforward use of an ontology in the context of data sourced from multiple experiments to learn classifiers predicting gene function as part of cellular response to an environmental stress.
This is followed by an article on the augmentation of subsumption propagation in distributed description logics (DDLs). DDLs provide for reasoning with multiple ontologies interconnected by so-called bridge rules, which map concepts of a source ontology into concepts of a target ontology. Concept subsumptions of the source ontology can be propagated according to a propagation pattern expressed by bridge rules into concept subsumptions of the target ontology. In the basic formulation of DDL, such a propagation is mostly limited to cases when pairs of ontologies are directly linked by means of bridge rules. When more than two ontologies are involved, the expectation is that subsumption propagates along chains of ontologies linked by bridge rules. However, the semantics of DDL is too weak to support this behavior. In their article, Homola and Serafini take a wider perspective, and propose a study of several different extensions of the DDL semantics. Based on a study of the formal properties, they choose the one that is most appropriate, and develop a sound and complete tableaux decision procedure for it.
Next is an article on ecosystems research based on modeling a reusable ontology framework, in which Myers, Atkinson, and Johnstone develop a set of reusable ontologies as separate components within a knowledge representation system to generically model a reef system. The ontology design, ranging from light to heavyweight, aims to leverage from the scalable characteristics of semantic technologies to allow for flexibility when posing domain and locality-specific hypotheses, such as predicting coral bleaching. Their intention is to develop an automated data processing, problem-solving, and knowledge discovery system that will assist in developing our understanding and management of coral reef ecosystems. Remote environmental monitoring (including sensor networks) is being widely developed and used for collecting real-time data across widely distributed locations. As the volume of raw data increases, it is envisaged that bottlenecks will develop in the data analysis phases because current data processing procedures still involve manual manipulation and will soon become unfeasible to manage. Ontologies provide a new approach and methodology for modulating this data overflow whilst also improving the ability to extract knowledge from the data collected.
The fourth article is concerned with reasoning with inconsistent ontologies through argumentation. Standard approaches to reasoning with ontologies require them to be consistent. However, as ontologies are complex entities and sometimes built upon other imported ontologies, inconsistencies can arise. In their article, Gomez, Chesňevar, and Simari present δ-ontologies, a framework for reasoning with inconsistent ontologies. The proposal involves expressing ontologies as Defeasible Logic Programs. Given a query posed w.r.t. an inconsistent ontology, a dialectical analysis will be performed on the program obtained from such an ontology, where all arguments in favor and against the final answer of the query will be taken into account. They also present an application to ontology integration based on the global-as-view approach.
The fifth and final article deals with the creation and querying formal ontologies via controlled natural language. Formal ontologies are difficult to read and understand for domain experts. This severely restricts the ability of this user group to determine whether an ontology conforms to the engineering expectations and requirements or not. In his article, Schwitter argues that a formal ontology, in this case a description logic knowledge base, should be created in a linguistically motivated way so that domain experts who do not have a background in formal logic can understand and query the ontology. He first shows that the readability of an ontology can be improved to some extent on the level of the formal notation with the help of a naming convention that is based on those linguistic expressions that occur in the application domain. He then goes further to illustrate that ABox and TBox statements that obey a linguistically motivated naming convention can be written directly in a controlled natural language, and that the same controlled natural language can be used to query the description logic knowledge base. Both ABox and TBox statements written in controlled natural language are translated automatically into the Knowledge Representation System Specification (KRSS) syntax and questions are translated into RacerPro's new query language nRQL and answered over the description logic knowledge base. Using a controlled natural language as a high-level interface language abstracts away from any formal notation and allows for true collaboration between humans and machines.
Many individuals contributed to this special issue. First, we would like to thank the Editor-in-Chief of Applied Artificial Intelligence, Robert Trappl, for his support for the special issue, and the Editorial Assistant, Sabine Payr, for her enthusiasm and support. We are also indebted to the authors of the eight submissions who accepted the invitation to submit. This special issue would not have been possible without their submissions. Last but not the least, we would like express our appreciation to our reviewers. They generously donated their time and expertise in reading the submissions and providing very detailed and constructive comments for the authors. They are:
Michael Bain (UNSW, Australia), Arina Britz (Meraka Institute, South Africa), R. Cenk Erdur (Ege University, Turkey), Joerg Evermann (Victoria University of Wellington, New Zealand), Aurona Gerber (Meraka Institute, South Africa), Manolis Gergatsoulis (Ionian University, Greece), Dennis Hooijmaijers (Qinetic Consulting, Australia), Bo Hu (SAP Research, UK), Renato Iannella (NICTA, Australia), Natalya Keberle (Zaporozhye National University, Ukraine), Laurent Lefort (CSIRO, Australia), Sergei Obiedkov (Russian State University for the Humanities, Russia), Maurice Pagnucco (UNSW, Australia), Rolf Schwitter (Macquarie University, Australia), Barry Smith (SUNY Buffalo, USA), Boontawee Suntisrivaraporn (Thammasat University, Thailand), Kerry Taylor (CSIRO, Australia), Sergio Tessaris (Free University of Bolzano, Italy), Nwe Ni Tun (National University of Singapore, Singapore), Ivan Varzinczak (Meraka Institute, South Africa), Kewen Wang (Griffith University, Australia), Pınar Yolum (Boğaziçi University, Turkey), and Antoine Zimmermann (DERI, National Unversity of Ireland, Ireland).