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Classification of linked data sources using semantic scoring

Linked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs:comment and rdfs:label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results

Prematüre Retinopatisi, Tanisi ve Degerlendirmede Standardizasyon

Retinopathy of prematurity (ROP) has been one of the issues about which the specialists have most argued but not compromised to that extent since its first definition. Restricted examination area of the eyes of the premature babies and difficulty of performing examination, and difficulty in achieving sufficient pupil dilatation have precluded the standardization of diagnosis and treatment. Plus disease, defined in 1980 has been used as an important indicator in the diagnosis and treatment of ROP. With a later revision, concepts such as preplus or threshold have been defined, and changes in posterior pole have gained much more importance. Desire of the ROP specialists in using technology towards the diagnosis of plus disease and consensus about the diagnosis has significantly increased in the last 10 years. In this article it was aimed to review the up-to-date studies about the diagnosis and treatment of ROP and plus disease

SpEnD: Linked data SPARQL endpoints discovery using search engines

Linked data endpoints are online query gateways to semantically annotated linked data sources. In order to query these data sources, SPARQL query language is used as a standard. Although a linked data endpoint (i.e. SPARQL endpoint) is a basic Web service, it provides a platform for federated online querying and data linking methods. For linked data consumers, SPARQL endpoint availability and discovery are crucial for live querying and semantic information retrieval. Current studies show that availability of linked datasets is very low, while the locations of linked data endpoints change frequently. There are linked data respsitories that collect and list the available linked data endpoints or resources. It is observed that around half of the endpoints listed in existing repositories are not accessible (temporarily or permanently offline). These endpoint URLs are shared through repository websites, such as Datahub.io, however, they are weakly maintained and revised only by their publishers. In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a “search keyword” discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, the collected search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. We analyze our findings in comparison to Datahub collection in detail

A Discovery and Analysis Engine for Semantic Web

The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods

Additive Effect of Mesenchymal Stem Cells and Defibrotide in An Arterial Rat Thrombosis Model

Scopu

Analysis and Design Optimization of Blunt Bodies In Hypersonic Flow

The purpose of this study is to model hypersonic flow around blunt body especially in atmospheric reentry of Earth. The more detailed model contains each energy transformation between each energy modes and all reactions. To simulate flow field region, thermal and chemical nonequilibrium must be considered all together. For the chemical nonequilibrium, species masses production of reactions must be characterized with suitable model. In this study, flow analysis based on the finite rate chemical reaction equations. Flow field region is assumed as continuum. Also flow is considered as inviscid and there is no diffusion. Computation of flow field is based on the axisymmetric Euler code. Coupled equations, chemical and thermal nonequilibrium equations are solved by using Newton's method. Jacobian matrices are calculated analytically. In the design part, aim is to obtain reduced pressure drag while keeping the body as blunt. Optimization results for various situations are represented