WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Conference Object Citation - WoS: 16Citation - Scopus: 21Spend: Linked Data Sparql Endpoints Discovery Using Search Engines(Ieice-inst Electronics information Communication Engineers, 2017) Yumusak, Semih; Dogdu, Erdogan; Kodaz, Halife; Kamilaris, Andreas; Vandenbussche, Pierre-YvesLinked 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.Conference Object Citation - Scopus: 2A Discovery and Analysis Engine for Semantic Web(Assoc Computing Machinery, 2018) Kamilaris, Andreas; Dogdu, Erdogan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre; Yumusak, SemihThe 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.Conference Object Citation - WoS: 30Citation - Scopus: 51An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework(Assoc Computing Machinery, 2017) Ozbayoglu, A. Murat; Dogdu, Erdogan; Sezer, Omer BeratIn this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.Conference Object Classification of Linked Data Sources Using Semantic Scoring(Ieice-inst Electronics information Communication Engineers, 2018) Dogdu, Erdogan; Kodaz, Halife; Yumusak, SemihLinked 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.Conference Object Citation - WoS: 57Citation - Scopus: 92A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters(Elsevier Science Bv, 2017) Ozbayoglu, Murat; Dogdu, Erdogan; Sezer, Omer BeratIn this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models. (c) 2017 The Authors. Published by Elsevier B.V.