The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering tools stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by engineers such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for nonexperts to visualize, modify, and create OWL complex classes.
The first result about converting AML to OWL appeared in 2009 by in their German paper @cite_2 . Two approaches were proposed and discussed. The represents the CAEX vocabulary directly as OWL classes in the ontology and transforms CAEX classes, objects and attributes as individuals of these OWL classes. The generates an OWL class for each CAEX class with an annotation about its original type in the CAEX schema. For example, an AML role class @math will be converted to an OWL class with the annotation @math . Subsequent researches generally follow either the abstract or the concrete approach. For example, proposed a lightweight ontology for covering core concepts of CAEX using the abstract approach @cite_4 , while followed the concrete approach for learning unknown engineering concepts from AML data @cite_0 .
@cite_0 @cite_4 @cite_2
2575901507 1543506259 1678959094 2000773008
At OWLED2007 a task force was formed to work towards a common Controlled Natural Language Syntax for OWL 1.1. In this paper members of the task force compare three controlled natural languages (CNLs) — Attempto Controlled English (ACE), Ordnance Survey Rabbit (Rabbit), and Sydney OWL Syntax (SOS) — that have been designed to express the logical content of OWL 1.1 ontologies. The common goal of these three languages is to make OWL ontologies accessible to people with no training in formal logics. We briefly introduce these three CNLs and discuss a number of requirements to an OWL-compatible CNL that have emerged from the present work. We then summarise the similarities and differences of the three CNLs and make some preliminary recommendations to an OWL-compatible CNL. With OWL (Web Ontology Language) established as a standard for encoding ontologies on the Semantic Web, interest has begun to focus on the task of verbalising OWL code in controlled English (or other natural language). Current approaches to this task assume that axioms in OWL can be mapped to sentences in English. We examine three potential problems with this approach (concerning logical sophistication, information structure, and size), and show that although these could in theory lead to insuperable difficulties, in practice they seldom arise, because ontology developers use OWL in ways that favour a transparent mapping. This result is evidenced by an analysis of patterns from a corpus of over 600,000 axioms in about 200 ontologies. We describe an extension of the description logic underlying OWL-DL, SHOIN, with a number of expressive means that we believe will make it more useful in practice. Roughly speaking, we extend SHOIN with all expressive means that were suggested to us by ontology developers as useful additions to OWL-DL, and which, additionally, do not affect its decidability and practicability. We consider complex role inclusion axioms of the form R o S ⊑ R or S o R ⊑ R to express propagation of one property along another one, which have proven useful in medical terminologies. Furthermore, we extend SHOIN with reflexive, antisymmetric, and irreflexive roles, disjoint roles, a universal role, and constructs ∃R. Self, allowing, for instance, the definition of concepts such as a "narcist". Finally, we consider negated role assertions in Aboxes and qualified number restrictions. The resulting logic is called SROIQ. We present a rather elegant tableau-based reasoning algorithm: it combines the use of automata to keep track of universal value restrictions with the techniques developed for SHOIQ. The logic SROIQ has been adopted as the logical basis for the next iteration of OWL, OWL 1.1. In recent years, there has been a growing interest in the adoption of ontologies and ontological reasoning to automatically recognize complex context data such as human activities. In particular, the Web Ontology Language (OWL) emerged as the language of choice, being a standard for the Semantic Web, and supported by a number of tools for knowledge engineering and reasoning. However, the limitations of OWL 1 in terms of expressiveness have been recognized in various fields, and important research efforts have been made to extend the language while preserving decidability of its OWL 1 DL fragment. The result of such work is OWL 2. In this paper we investigate the use of OWL 2 for modeling complex activities and reasoning with them. We show that the new language constructors of OWL 2 overcome the main limitations of OWL 1 for the representation of activities; OWL 2 axioms can be used to represent certain rules and rule-based reasoning previously demanded to hybrid approaches, with the advantage of having a unique semantics, avoiding potential inconsistencies. Then, we propose a system architecture showing the integration of a novel OWL 2 activity ontology and reasoning modules with distributed modules for sensor data aggregation and reasoning. The feasibility of our solution is shown by an extensive experimental evaluation with simulations of different intelligent environments.