There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

/pdf/ontology-development-101-a-guide-to-creating-your-first-2c6x0j449d.pdf

Ontology Development 101: A Guide to Creating Your First Ontology

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

/pdf/yago-a-core-of-semantic-knowledge-13k3nmj6l2.pdf

Yago: a core of semantic knowledge

Reasoning for expressive DLs implemented in state-of-the-art systems has high worst case complexity. The hope/claim is, however, that these systems perform well in “realistic” applications. In practice, this means in ontology applications. To check the validity of this claim it is necessary to test the performance of these systems with (the widest possible range of) ontologies derived from applications. In addition, testing is useful in order to check the correctness of implementations. In small examples, it may be easy to check the correctness of a system’s reasoning. However, for typical real-world examples, manual checking is not feasible. In these instances, the best (perhaps the only) way to check correctness is often by checking for consistency with the reasoning of other existing systems. Real-world ontologies vary considerably in their size and expressivity. While they are all valuable test cases, it is still important to understand each ontology’s properties in order to provide efficient and relevant testing. System developers find this particularly useful, as it helps them to identify weaknesses in their systems and to devise and test new optimisations. Finally, testing is also useful for the developers and users of applications as they can use benchmarking results to determine if (the performance of) a DL reasoner is likely to satisfy their requirements, and if so which reasoner is likely to perform best in their application.

/pdf/automated-benchmarking-of-description-logic-reasoners-1rlxeqcg5a.pdf

Automated benchmarking of description logic reasoners

Semantic Web (SW) languages are supposed to be compatible with each other in a meaningful way, so as to facilitate machine understanding. Recent research, however, shows that the semantics of the standard SW annotation language RDF (as well as its ontological extension RDFS) and that of the standard SW ontology language OWL DL are not compatible with each other. This paper investigates some issues behind this incompatibility and proposes a novel modification of RDF(S) as a firm semantic foundation for many of the latest description logics-based SW ontology languages, including OWL DL. Furthermore, the bidirectional one-to-one mapping between RDFS(FA) axioms in strata 0-2 and OWL DL axioms has been established, which enables RDFS(FA)-agents and OWL DL-agents to communicate with each other more easily. As a result, the introduction of RDFS(FA) clarifies the vision of the semantic Web and solidifies RDF(S)'s proposed role as the base of the semantic Web

/pdf/rdfs-fa-connecting-rdf-s-and-owl-dl-330xocp87u.pdf

RDFS(FA): Connecting RDF(S) and OWL DL

OWL is an ontology language developed by the W3C, and although initially developed for the Semantic Web, OWL has rapidly become a de facto standard for ontology development in general. The design of OWL was heavily influenced by research in description logics, and the specification includes a formal semantics. One of the goals of this formal approach was to provide interoperability: different OWL reasoners should provide the same results when processing the same ontologies. In this paper we present a system that allows users: (a) to test and compare OWL reasoners using an extensible library of real-life ontologies; (b) to check the “correctness” of the reasoners by comparing the computed class hierarchy; (c) to compare the performance of the reasoners when performing this task; and (d) to use SQL queries to analyse and present the results in any way they see fit.

/pdf/framework-for-an-automated-comparison-of-description-logic-3fjzds84mz.pdf

Framework for an automated comparison of description logic reasoners

This paper describes the outcomes of an ongoing collaboration between Siemens and the University of Oxford, with the goal of facilitating the design of ontologies and their deployment in applications. Ontologies are often used in industry to capture the conceptual information models underpinning applications. We start by describing the role that such models play in two use cases in the manufacturing and energy production sectors. Then, we discuss the formalisation of information models using ontologies, and the relevant reasoning services. Finally, we present SOMM—a tool that supports engineers with little background on semantic technologies in the creation of ontology-based models and in populating them with data. SOMM implements a fragment of OWL 2 RL extended with a form of integrity constraints for data validation, and it comes with support for schema and data reasoning, as well as for model integration. Our preliminary evaluation demonstrates the adequacy of SOMM’s functionality and performance.

/pdf/capturing-industrial-information-models-with-ontologies-and-2ldqpcglf7.pdf

Capturing Industrial Information Models with Ontologies and Constraints

Classical logics and Datalog-related logics have both been proposed as underlying formalisms for the Semantic Web. Although these two different formalism groups have some commonalities, and look similar in the context of expressively-impoverished languages like RDF, their differences become apparent at more expressive language levels. After considering some of these differences, we argue that, although some of the characteristics of Datalog have their utility, the open environment of the Semantic Web is better served by standard logics.

https://www.ra.ethz.ch/CDstore/www2006/devel-www2006.ecs.soton.ac.uk/programme/files/pdf/4015.pdf

Ian Horrocks

Papers

Automated benchmarking of description logic reasoners

RDFS(FA): Connecting RDF(S) and OWL DL

Framework for an automated comparison of description logic reasoners

Capturing Industrial Information Models with Ontologies and Constraints

Position paper: a comparison of two modelling paradigms in the Semantic Web