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

We present the results obtained by our ontology matching system LogMap and its 'lightweight' variant called LogMapLt within the OAEI 2012 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is our third participation in the OAEI and the experience has so far been very positive.

/pdf/logmap-and-logmaplt-results-for-oaei-2012-44aafd7jf0.pdf

LogMap and LogMapLt results for OAEI 2012

Graph querying and pattern matching is becoming an important feature of graph processing as it allows data analysts to easily collect and understand information about their graphs in a way similar to SQL for databases. One of the key challenges in graph pattern matching is to process increasingly large graphs that often do not fit in the memory of a single machine. In this paper, we present PGX.D/Async, a scalable distributed pattern matching engine for property graphs that is able to handle very large datasets. PGX.D/Async implements pattern matching operations with asynchronous depth-first traversal, allowing for a high degree of parallelism and precise control over memory consumption. In PGX.D/Async, developers can query graphs with PGQL, an SQL-like query language for property graphs. Essentially, PGX.D/Async provides an intuitive, distributed, in-memory pattern matching engine for very large graphs.

PGX.D/Async: A Scalable Distributed Graph Pattern Matching Engine

Consequence-based calculi are a family of reasoning algorithms for description logics (DLs), and they combine hyper-tableau and resolution in a way that often achieves excellent performance in practice. Up to now, however, they were proposed for either Horn DLs (which do not support disjunction), or for DLs without counting quantifiers. In this paper we present a novel consequence-based calculus for SRIQ— a rich DL that supports both features. This extension is non-trivial since the intermediate consequences that need to be derived during reasoning cannot be captured using DLs themselves. The results of our preliminary performance evaluation suggest the feasibility of our approach in practice.

/pdf/extending-consequence-based-reasoning-to-sriq-1bd2867n8q.pdf

Extending consequence-based reasoning to SRIQ

Invited Papers.- Semantic Web Enabled Web Services.- The Grid, Grid Services and the Semantic Web: Technologies and Opportunities.- Research Papers.- Matching RDF Graphs.- Layering the Semantic Web: Problems and Directions.- Notions of Indistinguishability for Semantic Web Languages.- The Usable Ontology: An Environment for Building and Assessing a Domain Ontology.- Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema.- A Formal Model for Topic Maps.- Towards High-Precision Service Retrieval.- Automatic Generation of Java/SQL Based Inference Engines from RDF Schema and RuleML.- Ontology-Based Integration of XML Web Resources.- Benchmarking RDF Schemas for the Semantic Web.- Building the Semantic Web on XML.- Trusting Information Sources One Citizen at a Time.- Querying the Semantic Web: A Formal Approach.- Semantic Configuration Web Services in the CAWICOMS Project.- Integrating Vocabularies: Discovering and Representing Vocabulary Maps.- OntoEdit: Collaborative Ontology Development for the Semantic Web.- Towards a Modification Exchange Language for Distributed RDF Repositories.- Representing Disjunction and Quantifiers in RDF.- Towards Semantic Web Mining.- Bringing Together Semantic Web and Web Services.- Global vs. Community Metadata Standards: Empowering Users for Knowledge Exchange.- Taking the RDF Model Theory Out for a Spin.- Concurrent Execution Semantics of DAML-S with Subtypes.- Semantic Matching of Web Services Capabilities.- DAML-S: Web Service Description for the Semantic Web.- TRIPLE-A Query, Inference, and Transformation Language for the Semantic Web.- A Data Integration Framework for e-Commerce Product Classification.- Position Papers.- Nonmonotonic Rule Systems on Top of Ontology Layers.- An RDF NetAPI.- A Mini-experiment in Semantic Annotation.- SWAD-Europe: Semantic Web Advanced Development in Europe A Position Paper.- Preemptive Reification.- Four Steps Towards the Widespread Adoption of a Semantic Web.- System Descriptions.- Three Implementations of SquishQL, a Simple RDF Query Language.- ClaiMaker: Weaving a Semantic Web of Research Papers.- Business and Enterprise Ontology Management with SymOntoX.- Is Participation in the Semantic Web Too Difficult?.- Consistency Checking of Semantic Web Ontologies.- WebTheme (TM): Understanding Web Information through Visual Analytics.- Browsing Schedules - An Agent-Based Approach to Navigating the Semantic Web.

The semantic Web -- ISWC 2002 : First International Semantic Web Conference, Sardinia, Italy, June 9-12, 2002 : proceedings

Ontologies form the backbone of the Semantics Web. They provide explicit descriptions of the conceptualization underlying available information. The effort involved in creating and maintaining ontologies is one of the most severe obstacles to a wide adoption of semantic web technology. In this paper, we address the problem of creating ontologies for the semantic and describe experiences with an approach that combines automated extraction of conceptual models from existing information sources with state-of-the-art methodologies and tools for ontological engineering. We further discuss tool interoperation based on semantic web technology as an enabler for successful ontology maintenance.

Ian Horrocks

Papers

LogMap and LogMapLt results for OAEI 2012

PGX.D/Async: A Scalable Distributed Graph Pattern Matching Engine

Extending consequence-based reasoning to SRIQ

The semantic Web -- ISWC 2002 : First International Semantic Web Conference, Sardinia, Italy, June 9-12, 2002 : proceedings

Tackling the Ontology Acquisition Bottleneck: An Experiment in Ontology Re-Engineering