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.

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Yago: a core of semantic knowledge

Description Logics (DLs) are a family of logic based Knowledge Representation formalisms descended from semantic networks and KL-ONE. They are distinguished by having formal (model theoretic) semantics, and by the provision of (sound and complete) inference services, with several highly optimised implementations now being available. DLs have a wide range of applications, but are perhaps best know as ontology languages (they provide the basis for recent "Semantic Web" ontology languages such as OIL, DAML+OIL and OWL). In this talk I will give a brief history of DLs and of DL applications, in particular their application in the context of the Semantic Web. If time permits, I will then give an overview of the reasoning techniques that are employed by state of the art DL implementations, and which enable them to be effective in realistic applications, in spite of the high worst case complexity of their basic inference problems. Finally, I will point out some interesting areas for future research, in particular those related to the Semantic Web application area.

Reasoning with expressive description logics: logical foundations for the semantic web

We describe a hybrid approach to conjunctive query answering over OWL 2 ontologies that combines a datalog reasoner with a fully-fledged OWL 2 reasoner in order to provide scalable “pay as you go” performance. Our approach delegates the bulk of the computation to the highly scalable datalog engine and resorts to expensive OWL 2 reasoning only as necessary to fully answer the query. We have implemented a prototype system that uses RDFox as a datalog reasoner, and HermiT as an OWL 2 reasoner. Our evaluation over both benchmark and realistic ontologies and datasets suggests the feasibility of our approach.

/pdf/pay-as-you-go-ontology-query-answering-using-a-datalog-4c7n77iv4f.pdf

Pay−as−you−go Ontology Query Answering Using a Datalog Reasoner

EU-IST Network of Excellence (NoE) IST-2004-507482 KWEB Deliverable D2.5.2 (WP2.5) In the progress of realising the Semantic Web, developing and supporting Semantic Web query languages are among the most useful and important research problems. In [PFT + 04], we have provided a unified framework for OWL-based rule and query lang uages. In this report, we focus on the problems of query answering for Semantic Web query languages (such as RDF, OWL DL and OWL-E) in the OWL-QL specification. Keyword list: description logics, ontology language, query language, RDF, OWL DL, OWL-E

/pdf/d2-5-2-report-on-query-language-design-and-standardisation-1y61oswz92.pdf

D2.5.2 Report on Query Language Design and Standardisation

In this paper we extend the evaluation of the OAEI 2012 Large BioMed track, which involves the matching of the semantically rich ontologies FMA, NCI and SNOMED CT. Concretely, we report about the differences and similarities among the mappings computed by the participant ontology matching systems.

/pdf/is-my-ontology-matching-system-similar-to-yours-58sww8h888.pdf

Is my ontology matching system similar to yours

A cluster of servers is often used to reason over RDF graphs whose size exceeds the capacity of a single server. While many distributed approaches to reasoning have been proposed, the problem of data partitioning has received little attention thus far. In practice, data is usually partitioned by a variant of hashing, which is very simple, but it does not pay attention to data locality. Locality-aware partitioning approaches have been considered, but they usually process the entire dataset on a single server. In this paper, we present two new RDF partitioning strategies. Both are inspired by recent streaming graph partitioning algorithms, which partition a graph while keeping only a small subset of the graph in memory. We have evaluated our approaches empirically against hash and min-cut partitioning. Our results suggest that our approaches can significantly improve reasoning performance, but without unrealistic demands on the memory of the servers used for partitioning.

/pdf/streaming-partitioning-of-rdf-graphs-for-datalog-reasoning-2f14dybqqh.pdf

Ian Horrocks

Papers

Reasoning with expressive description logics: logical foundations for the semantic web

Pay−as−you−go Ontology Query Answering Using a Datalog Reasoner

D2.5.2 Report on Query Language Design and Standardisation

Is my ontology matching system similar to yours

Streaming Partitioning of RDF Graphs for Datalog Reasoning