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

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems.

/pdf/datalog-reasoning-over-compressed-rdf-knowledge-bases-3u6emoth5o.pdf

Datalog Reasoning over Compressed RDF Knowledge Bases

The combination of transitive and inverse roles is important in a range of applications, and is crucial for the adequate representation of aggregated objects, allowing the simultaneous description of parts by means of the whole to which they belong and of wholes by means of their constituent parts. In this paper we present tableaux algorithms for deciding concept satisfiability and subsumption in Description Logics that extend ALC with both transitive and inverse roles, a role hierarchy, and functional restrictions. In contrast to earlier algorithms for similar logics, those presented here are well-suited for implementation purposes: using transitive roles and role hierarchies in place of the transitive closure of roles enables sophisticated blocking techniques to be used in place of the cut rule, a rule whose high degree of non-determinism strongly discourages its use in an implementation. As well as promising superior computational behaviour, this new approach is shown to be sufficiently powerful to allow subsumption and satisfiability with respect to a (possibly cyclic) knowledge base to be reduced to concept subsumption and satisfiability, and to support reasoning in a Description Logic that no longer has the finite model property.

A Description Logic with Transitive and Converse Roles and Role Hierarchies

Ontologies and ontology based systems are becoming increasingly important in meeting the demand for more powerful and flexible information systems. Requirements for such systems include the need to deal with incomplete and semi-structured information, to integrate information from heterogeneous sources, to employ richer and more flexible schemas, and for query answers to reflect both knowledge and data. Provision of such enhanced capabilities must, however, be in addition to, and not instead of, the well-established features of existing database systems, in particular their robust scalability. Achieving this is, of course, extremely challenging. In this talk I will present some recent research efforts that tackle this problem, including investigations of tractable fragments, new algorithmic techniques, new optimisations and the exploitation of relational database technology.

/pdf/scalable-ontology-based-information-systems-3kucbouzyx.pdf

Scalable ontology-based information systems

/pdf/efficient-upper-bound-computation-of-query-answers-in-4bjwuf1fui.pdf

Efficient Upper Bound Computation of Query Answers in Expressive Description Logics

To efficiently answer queries, datalog systems often materialise all consequences of a datalog program, so the materialisation must be updated whenever the input facts change. Several solutions to the materialisation update problem have been proposed. The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries. We show that this can be a considerable source of overhead even on very small updates. In contrast, the Counting algorithm does not evaluate the rules 'backwards', but it can handle only nonrecursive rules. We present two hybrid approaches that combine DRed and B/F with Counting so as to reduce or even eliminate 'backward' rule evaluation while still handling arbitrary datalog programs. We show empirically that our hybrid algorithms are usually significantly faster than existing approaches, sometimes by orders of magnitude.

Ian Horrocks

Papers

Datalog Reasoning over Compressed RDF Knowledge Bases

A Description Logic with Transitive and Converse Roles and Role Hierarchies

Scalable ontology-based information systems

Efficient Upper Bound Computation of Query Answers in Expressive Description Logics

Optimised Maintenance of Datalog Materialisations