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

The goal of Semantic Web research is to transform the Web from a linked document repository into a distributed knowledge base and application platform, thus allowing the vast range of available information and services to be more effectively exploited. As a first step in this transformation, languages such as OWL have been developed; these languages are designed to capture the knowledge that will enable applications to better understand Web accessible resources, and to use them more intelligently. Although fully realising the Semantic Web still seems some way off, OWL has already been very successful, and has rapidly become a de facto standard for ontology development in fields as diverse as geography, geology, astronomy, agriculture, defence and the life sciences. An important factor in this success has been the availability of sophisticated tools with built in reasoning support. The use of OWL in large scale applications has brought with it new challenges, both with respect to expressive power and scalability, but recent research has also shown how the OWL language and OWL tools can be extended and adapted to meet these challenges.

Semantic Web: The Story So Far

Taxonomy plays a key role in e-commerce, categorising items and facilitating both search and inventory management. Concept subsumption prediction is critical for taxonomy curation, and has been the subject of several studies, but they do not fully utilise the categorical information available in e-commerce settings. In this paper, we study the characteristics of e-commerce taxonomies, and propose a new subsumption prediction method based on the pre-trained language model BERT that is well adapted to the e-commerce setting. The proposed model utilises textual and structural semantics in a taxonomy, as well as the rich and noisy instance (item) information. We show through extensive evaluation on two large-scale e-commerce taxonomies from eBay and AliOpenKG, that our method offers substantial improvement over strong baselines. 

Subsumption Prediction for E-Commerce Taxonomies

/pdf/mv-index-an-efficient-index-for-graph-query-containment-c5qmdijh1m.pdf

Mv-Index: An Efficient Index for Graph-Query Containment.

Nowadays there exists an abundance of heterogeneous Semantic Web data coming from multiple sources. As a result, matching Linked Data has become a tedious and non-transparent task. One way to facilitate entity matching across datasets is to provide human-readable explanations that highlight what the two entities have in common, as well as what differentiates the two entities. Entity comparison is an important information exploration problem that has recently gained considerable research attention [1, 2, 4]. In this paper we propose a solution towards explainable entity matching in Linked Data where entity comparison is used as a subroutine that assists in debugging and validation of matchings. To this end, we adopt the entity comparison framework in which explanations are modelled as unary conjunctive queries of restricted form [3, 4]. We concentrate on the data model where a dataset is an RDF graph—that is, a set of triples of IRIs and literals, jointly called entities. The basic building block of a query is a triple pattern, which is a triple of entities and variables. Then, a query is a non-empty finite set of triple patterns in which one variable, usually denoted by X, is an answer variable. The set Q(D) of answer entities to a query Q on a dataset D is defined as usual in databases. The two main notions of the framework are the similarity and difference queries for pairs of entities, which are defined as follows: a similarity query for entities a and b in a dataset D is a query Q satisfying {a, b} ⊆ Q(D); a difference query for a relative to b is a query Q satisfying a ∈ Q(D) and b 6∈ Q(D). In our prior work we proposed an algorithm for computing comparison queries that can be repurposed for similarity and difference queries [3]. The algorithm is based on the computation of a similarity tree—a data structure that represents commonalities and discrepancies in data for input entities a and b. It is a directed rooted tree with nodes and edges labelled by pairs of sets of entities such that the root is labelled by ({a}, {b}) and every edge labelled (E1, E2) between nodes labelled (N1, N2) and (N ′ 1, N ′ 2) is justified in the sense that for every entity n in Ni, i ∈ {1, 2}, there is a triple (n, e, n′) in the dataset with e ∈ Ei and n′ ∈ N ′ i . For instance, suppose there are 3 entities, Emma_Watson, Emily_Watson and E_Watson, that need to be either matched or disambiguated, and a data fragment given in Figure 1. Then the similarity trees for Emma_Watson and E_Watson, and for Emily_Watson and E_Watson are depicted in Figure 2 (where singleton sets {`} and pairs ({`}, {`}) are both written as ` for readability).

/pdf/towards-explainable-entity-matching-via-comparison-queries-57lru97vdl.pdf

Towards Explainable Entity Matching via Comparison Queries.

To investigate the effect of possible changes to decarbonise the
economy, a detailed picture of the current production system is needed. Material/energy flow analysis (MEFA) allows for building such a model. There are, however, prohibitive barriers to the integration and use of the diverse datasets necessary for a system-wide yet technically-detailed MEFA study. Here we present a demonstration of the “Physical Resources Observatory” (PRObs) system, which exploits Semantic Web technologies to integrate and reason on top of this diverse production system data. We demonstrate how this system supports domain experts to load datasets, map them to the ontology, and query the extended data to meet several modelling use cases.

/pdf/use-of-semantic-technologies-to-inform-progress-toward-zero-4zfkjjve5z.pdf

Ian Horrocks

Papers

Semantic Web: The Story So Far

Subsumption Prediction for E-Commerce Taxonomies

Mv-Index: An Efficient Index for Graph-Query Containment.

Towards Explainable Entity Matching via Comparison Queries.

Use of semantic technologies to inform progress toward zero-carbon economy: Extended Abstract