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

This work constitutes an advance in the direction of the development of Description logic systems providing efficient and powerful reasoning in presence of individuals. We present the empirical evaluation of an algorithm for checking the satisfiability of Description logic knowledge bases. The experiments we performed show that a modular algorithm, which separates terminological and hybrid reasoning, can provide as good performance as an hybrid Description logic reasoner. We are experimenting with the expressive Description logic SHf, which extends the standard Description logicALC with transitive roles, role hierarchy, and attributes.

/pdf/evaluating-a-modular-abox-algorithm-3tlvn3rav9.pdf

Evaluating a modular abox algorithm

Industry today employs rule-based diagnostic systems to minimize the maintenance cost and downtime of equipment. Rules are typically used to process signals from sensors installed in equipment by filtering, aggregating, and combining sequences of time-stamped measurements recorded by the sensors. Such rules are often data-dependent in the sense that they rely on specific characteristics of individual sensors and equipment. This dependence poses significant challenges in rule authoring, reuse, and maintenance by engineers especially when the rules require domain knowledge. In this work we propose an approach to address these problems by relying on the well-known Ontology-Based Data Access approach: we propose to use ontologies to mediate the sensor signals and the rules. To this end, we propose a semantic rule language, SDRL, where signals are first class citizens. Our language offers a balance of expressive power, usability, and efficiency: it captures most of Siemens data-driven diagnostic rules, significantly simplifies authoring of diagnostic tasks, and allows to efficiently rewrite semantic rules from ontologies to data and execute over data. We implemented our approach in a semantic diagnostic system and evaluated it. For evaluation we developed a use case of rail systems at Siemens and conducted experiments to demonstrate both usability and efficiency of our solution.

Diagnostics of Trains with Semantic Diagnostics Rules

Query answering for the QL profile of OWL 2 and a substantial fragment of the EL profile can be implemented via query rewriting. In our previous work, we presented RQR--a rewriting algorithm for OWL QL that can also deal with most of the EL profile. In order to test the likely practicality of RQR, we have implemented it in a query rewriting system that we call REQUIEM. A recent empirical evaluation of REQUIEM, in which we considered OWL 2 QL ontologies, indicates that it produces significantly smaller rewritings than existing approaches in most cases. However, our results suggest that typical queries over realistic ontologies can still lead to very large rewritings (e.g., containing many thousands of queries). In this paper, we describe query rewriting, briefly present the results of our empirical evaluation, and discuss various optimization techniques aimed at reducing the size of the rewritings. We additionally discuss the consequences of rewriting queries w.r.t. OWL 2 EL ontologies and present results from a preliminary empirical evaluation of REQUIEM in which we consider realistic OWL 2 EL ontologies.

/pdf/practical-aspects-of-query-rewriting-for-owl-2-2wspvrwgs3.pdf

Practical aspects of query rewriting for OWL 2

In large companies such as Siemens and Statoil monitoring tasks are of great importance, e.g., Siemens does monitoring of turbines and Statoil of oil behaviour in wells. This tasks bring up importance of both streaming and historical (temporal) data in the Big Data challenge for industries. We present the Optique project that addresses this problem by developing an Ontology Based Data Access (OBDA) system that incorporates novel tools and methodologies for processing and analyses of temporal and streaming data. In particular, we advocate for modelling time time aware data by temporal RDF and reduce monitoring tasks to knowledge discovery and data mining.

Addressing Streaming and Historical Data in OBDA Systems: Optique's Approach.

This is a system description of the Description Logic reasoner FaCT++ The reasoner implements a tableaux decision procedure for the well known $\mathcal{SHOIQ}$ description logic, with additional support for datatypes, including strings and integers The system employs a wide range of performance enhancing optimisations, including both standard techniques (such as absorption and model merging) and newly developed ones (such as ordering heuristics and taxonomic classification) FaCT++ can, via the standard DIG interface, be used to provide reasoning services for ontology engineering tools supporting the OWL DL ontology language

http://www.cs.man.ac.uk/~horrocks/Publications/download/2006/TsHo06a.pdf

Ian Horrocks

Papers

Evaluating a modular abox algorithm

Diagnostics of Trains with Semantic Diagnostics Rules

Practical aspects of query rewriting for OWL 2

Addressing Streaming and Historical Data in OBDA Systems: Optique's Approach.

FaCT++ Description Logic Reasoner: System Description