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

Publisher Summary This chapter examines computational approaches to modal logics. A variety of computational approaches and reasoning problems are considered, the use of translation-based and tableau-based algorithms for deciding the satisfiability of a formula is focused, both with and without reference to a background theory. The dominance of translation-based and tableau-based approaches in implemented systems, and by the importance of satisfiability testing in applications such as the verification of multi-agent systems and ontology engineering is examined. The reason for the dominance of these two approaches is that they have proved amenable to implementation and optimization techniques that dramatically improve typical case performance; the use of such techniques is crucial if reasoning systems are to be effective in applications. The applicability and effectiveness of optimization techniques and refinements is highly dependent on the logic under consideration and on the class of problem being solved. The choice of approach depend on the logic in question: tableau-based methods seem to have some advantages in the presence of graded modalities (counting), whereas translation-based methods can handle and may be better for Boolean modal logics.

/pdf/4-computational-modal-logic-2tbf23oohx.pdf

4 Computational modal logic

Today quite a lot of multimedia systems and applications use knowledge representation formalisms to encode and reason with knowledge that exists within the multimedia documents. The goal of this direction is to narrow the semantic gab between the content of a multimedia object, as perceived by a human being, and as “viewed” by an information system. Ontologies are quite often used to capture such a knowledge. Ontology languages are based on Description Logics, which though they are expressive enough, they lack the ability to encode and reason with imprecise knowledge. To this end we extend the DL language SI with fuzzy set theory and provide sound and complete reasoning algorithms for the extended language.

/pdf/a-fuzzy-description-logic-for-multimedia-knowledge-10wonftj4w.pdf

A Fuzzy Description Logic for Multimedia Knowledge Representation

Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity correspondences of cells in the KB, and may fail to deal with growing web tables with incomplete meta information. In this paper we propose a neural network based column type annotation framework named ColNet which is able to integrate KB reasoning and lookup with machine learning and can automatically train Convolutional Neural Networks for prediction. The prediction model not only considers the contextual semantics within a cell using word representation, but also embeds the semantics of a column by learning locality features from multiple cells. The method is evaluated with DBPedia and two different web table datasets, T2Dv2 from the general Web and Limaye from Wikipedia pages, and achieves higher performance than the state-of-the-art approaches.

/pdf/colnet-embedding-the-semantics-of-web-tables-for-column-type-425c8opzib.pdf

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

Recent years have seen the emergence of a new generation of heavily-optimised modal decision procedures. Several systems based on such procedures are now available and have proved to be much more effective than the previous generation of modal decision procedures. As both computational complexity and algorithm complexity are generally unchanged, neither is useful in analysing and comparing these new systems and their various optimisations. Instead, empirical testing has been widely used, both for comparison and as a tool for tuning systems and identifying their strengths and weaknesses. However, the very effectiveness of the new systems has revealed serious weaknesses in existing empirical test suites and methodologies. This paper provides a detailed survey of empirical testing methodologies, analyses the current state of the art and presents new results obtained with a recently developed test method.

/pdf/an-analysis-of-empirical-testing-for-modal-decision-3hf28v882r.pdf

An analysis of empirical testing for modal decision procedures

This chapter will discuss the implementation of the reasoning services which form the core of DL-based knowledge representation systems. To be useful in realistic applications, such systems need both expressive logics and fast reasoners. As expressive logics inevitably have high worst-case complexities, this can only be achieved by employing highly optimized implementations of suitable reasoning algorithms. Systems based on such implementations have demonstrated that they can perform well with problems that occur in realistic applications, including problems where unoptimized reasoning is hopelessly intractable.

Ian Horrocks

Papers

4 Computational modal logic

A Fuzzy Description Logic for Multimedia Knowledge Representation

ColNet: Embedding the Semantics of Web Tables for Column Type Prediction

An analysis of empirical testing for modal decision procedures

Implementation and optimization techniques