Browsing by Author "Yumusak, Semih"

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Classification of Linked Data Sources Using Semantic Scoring
(Ieice-inst Electronics information Communication Engineers, 2018) Yumusak, Semih; Doğdu, Erdoğan; Dogdu, Erdogan; Kodaz, Halife; 142876
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.
Low-diameter topic-based pub/sub overlay Network Construction with minimum–maximum node Degree
(2021) Hassanpour, Reza; Layazali, Sina; Öztoprak, Kasım; Hassanpour, Reza
In the construction of effective and scalable overlay networks, publish/subscribe (pub/sub) network designers prefer to keep the diameter and maximum node degree of the network low. However, existing algorithms are not capable of simultaneously decreasing the maximum node degree and the network diameter. To address this issue in an overlay network with various topics, we present herein a heuristic algorithm, called the constant-diameter minimum–maximum degree (CD-MAX), which decreases the maximum node degree and maintains the diameter of the overlay network at two as the highest. The proposed algorithm based on the greedy merge algorithm selects the node with the minimum number of neighbors. The output of the CD-MAX algorithm is enhanced by applying a refinement stage through the CD-MAX-Ref algorithm, which further improves the maximum node degrees. The numerical results of the algorithm simulation indicate that the CD-MAX and CD-MAX-Ref algorithms improve the maximum node-degree by up to 64% and run up to four times faster than similar algorithms.
Sentiment Analysis for the Social Media: A Case Study for Turkish General Elections
(Assoc Computing Machinery, 2017) Uysal, Elif; Doğdu, Erdoğan; Yumusak, Semih; Oztoprak, Kasim; Dogdu, Erdogan
The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specific keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results.