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

{DL} {R}easoner vs. {F}irst-{O}rder {P}rover

The OWL [2] datatype formalism (or simply OWL datatyping) presents some new requirements for DL reasoning services, in terms of semantics (to allow the use of so-called 'un-supported' datatypes), expressive power (to support enumerated datatypes) and datatype construction mechanism (both datatypes and datatype expressions). On the other hand, OWL datatyping is expected to be extended to include more expressive power. E.g., OWL datatyping does not provide a general framework for user-defined datatypes, such as XML Schema derived datatypes, nor does it support n-ary datatype predicates (such as the binary predicate > for integers), not to mention user-defined datatype predicates (such as the binary predicate > for non-negative integers). In this poster, we explain why it is necessary to extend the existing datatype approaches to the datatype group approach, in order to meet the above new requirements. It was Baader and Hanschke [1] who first presented a rigorous treatment of datatype predicates (or simply predicates). In their approach, a concrete domain [1, 4] is composed of a set of datatype values (such as integers) and a set of n-ary predicates (such as '<') defined over these values with obvious (fixed) extensions. Horrocks and Sattler [3] proposed the so called 'type system approach', which can be seen as a simplified version of the concrete domain approach, where the datatype domain (of a datatype interpretation) is regarded as a universal concrete domain and datatypes are treated as unary predicates in the universal concrete domain. In short, in the above two approaches, datatypes are nothing but unary predicates. In OWL datatyping, however, people take another view. A Datatype d distinguishes from a predicate in that it is characterised not only by the value spaces V (d), but also a lexical space, L(d), which is a set of Unicode strings, and a total mapping L2V (d) from the lexical space to the value space. E.g., boolean is a datatype with value space {true, f alse}, lexical space {T,F,1,0} and lexical-to-value mapping {T → true, F → f alse, 1 → true, 0 → f alse}. Data values can be represented by typed literals or plain literals, where typed literals are combinations of string and datatype URIs, while plain literals are simply strings, with optional language tag. E.g., \" 1 \" ˆˆxsd:boolean is a typed literal, while \" 1 \" is a plain literal. Therefore, when we extend OWL datatyp-ing to support predicates, we should not …

/pdf/extending-dl-reasoning-support-for-the-owl-datatyping-or-why-1dsux6masb.pdf

Extending DL Reasoning Support for the OWL Datatyping (or "Why Datatype Groups?").

DAML+OIL Walkthru

/pdf/rsacomb-combined-approach-for-cq-answering-in-rsa-2cnj7qq9s0.pdf

RSAComb: Combined Approach for CQ Answering in RSA.

Although OWL is rather expressive, it has a very serious limitation on datatypes; i.e., it does not support customised datatypes. It has been pointed out that many potential users will not adopt OWL unless this limitation is overcome, and the W3C Semantic Web Best Practices and Deployment Working Group has set up a task force to address this issue. This paper provides a solution for this issue by presenting two decidable datatype extensions of OWL DL, namely OWL-Eu and OWL-E. OWL-Eu provides a minimal extension of OWL DL to support customised datatypes, while OWL-E extends OWL DL with both customised datatypes and customised datatype predicates.

/pdf/introducing-customised-datatypes-and-datatype-predicates-1dcuwvse6k.pdf

Ian Horrocks

Papers

{DL} {R}easoner vs. {F}irst-{O}rder {P}rover

Extending DL Reasoning Support for the OWL Datatyping (or "Why Datatype Groups?").

DAML+OIL Walkthru

RSAComb: Combined Approach for CQ Answering in RSA.

Introducing Customised Datatypes and Datatype Predicates into OWL.