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

Owl web ontology language 1

We present a novel reasoning calculus for Description Logics (DLs)--knowledge representation formalisms with applications in areas such as the Semantic Web. In order to reduce the nondeterminism due to general inclusion axioms, we base our calculus on hypertableau and hyperresolution calculi, which we extend with a blocking condition to ensure termination. To prevent the calculus from generating large models, we introduce "anywhere" pairwise blocking. Our preliminary implementation shows significant performance improvements on several well-known ontologies. To the best of our knowledge, our reasoner is currently the only one that can classify the original version of the GALEN terminology.

/pdf/optimized-reasoning-in-description-logics-using-12435c13p7.pdf

Optimized Reasoning in Description Logics Using Hypertableaux

We present a novel approach to parallel materialisation (i.e., fixpoint computation) of datalog programs in centralised, main-memory, multi-core RDF systems. Our approach comprises an algorithm that evenly distributes the workload to cores, and an RDF indexing data structure that supports efficient, 'mostly' lock-free parallel updates. Our empirical evaluation shows that our approach parallelises computation very well: with 16 physical cores, materialisation can be up to 13.9 times faster than with just one core.

/pdf/parallel-materialisation-of-datalog-programs-in-centralised-1t9joyh8ab.pdf

Parallel materialisation of datalog programs in centralised, main-memory RDF systems

Recently, a widespread interest has emerged in using ontologies on the Web. Resource Description Framework Schema (RDFS) is a basic tool that enables users to define vocabulary, structure and constraints for expressing meta data about Web resources. However, it includes no provisions for formal semantics, and its expressivity is not sufficient for full-fledged ontological modeling and reasoning. In this paper, we will show how RDFS can be extended to include a more expressive knowledge representation language. That, in turn, would enrich it with the required additional expressivity and the semantics of that language. We do this by describing the ontology language Ontology Inference Layer (OIL) as an extension of RDFS. An important advantage to our approach is that it ensures maximal sharing of meta data on the Web: even partial interpretation of an OIL ontology by less semantically aware processors will yield a correct partial interpretation of the meta data. � 2002 Elsevier Science B.V. All rights reserved.

/pdf/enabling-knowledge-representation-on-the-web-by-extending-ylcc3ahnfn.pdf

Enabling knowledge representation on the Web by extending RDF schema

Logic programming (LP) is often seen as a way to overcome several shortcomings of the Web Ontology Language (OWL), such as the inability to model integrity constraints or perform closed-world querying However, the open-world semantics of OWL seems to be fundamentally incompatible with the closed-world semantics of LP This has sparked a heated debate in the Semantic Web community, resulting in proposals for alternative ontology languages based entirely on logic programming To help resolving this debate, we investigate the practical use cases which seem to be addressed by logic programming In fact, many of these requirements have already been addressed outside the Semantic Web By drawing inspiration from these existing formalisms, we present a novel logic of hybrid MKNF knowledge bases, which seamlessly integrates OWL with LP We are thus capable of addressing the identified use cases without a radical change in the architecture of the Semantic Web

/pdf/can-owl-and-logic-programming-live-together-happily-ever-4jjqoj83pn.pdf

Ian Horrocks

Papers

Owl web ontology language 1

Optimized Reasoning in Description Logics Using Hypertableaux

Parallel materialisation of datalog programs in centralised, main-memory RDF systems

Enabling knowledge representation on the Web by extending RDF schema

Can OWL and logic programming live together happily ever after