Deniz Kılınç

An accurate toponym-matching measure based on approximate string matching

Approximate string matching (ASM) is a challenging problem, which aims to match different string expressions representing the same object. In this paper, detailed experimental studies were conducted on the subject of toponym matching, which is a new domain where ASM can be performed, and the creation of a single string-matching measure that can perform toponym matching process regardless of the language was attempted. For this purpose, an ASM measure called DAS, which comprises name similarity, word similarity and sentence similarity phases, was created. Considering the experimental results, the retrieval performance and system accuracy of DAS were much better than those of other well-known five measures that were compared on toponym test datasets. In addition, DAS had the best metric values of mean average precision in six languages, and precision/recall graphs confirm this result.

TTC-3600

Owing to the rapid growth of the World Wide Web, the number of documents that can be accessed via the Internet explosively increases with each passing day. Considering news portals in particular, sometimes documents related to categories such as technology, sports and politics seem to be in the wrong category or documents are located in a generic category called others. At this point, text categorization (TC), which is generally addressed as a supervised learning task is needed. Although there are substantial number of studies conducted on TC in other languages, the number of studies conducted in Turkish is very limited owing to the lack of accessibility and usability of datasets created. In this paper, a new dataset named TTC-3600, which can be widely used in studies of TC of Turkish news and articles, is created. TTC-3600 is a well-documented dataset and its file formats are compatible with well-known text mining tools. Five widely used classifiers within the field of TC and two feature selection methods are evaluated on TTC-3600. The experimental results indicate that the best accuracy criterion value 91.03% is obtained with the combination of Random Forest classifier and attribute ranking-based feature selection method in all comparisons performed after pre-processing and feature selection steps. The publicly available TTC-3600 dataset and the experimental results of this study can be utilized in comparative experiments by other researchers.

Multi-level reranking approach for bug localization

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programmes goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval IR based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi-level re-ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi-level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision MAP and Mean Reciprocal Rank MRR values for all datasets.

Regression Analysis Based Software Effort Estimation Method

Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

Twitter fake account detection

Social networking sites such as Twitter and Facebook attracts millions of users across the world and their interaction with social networking has affected their life. This popularity in social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content. This situation can result to a huge damage in the real world to the society. In our study, we present a classification method for detecting the fake accounts on Twitter. We have preprocessed our dataset using a supervised discretization technique named Entropy Minimization Discretization (EMD) on numerical features and analyzed the results of the Naïve Bayes algorithm.

The average scattering number of graphs

The scattering number of a graph is a measure of the vulnerability of a graph. In this paper we investigate a refinement that involves the average of a local version of the parameter. If v is a vertex in a connected graph G , then s c v (G ) = max { ω (G − S v ) − | S v | }, where the maximum is taken over all disconnecting sets S v of G that contain v . The average scattering number of G denoted by s c av (G ), is defined as scav (G) = Σv ∈ V(G) scv(G) / n , where n will denote the number of vertices in graph G . Like the scattering number itself, this is a measure of the vulnerability of a graph, but it is more sensitive. Next, the relations between average scattering number and other parameters are determined. The average scattering number of some graph classes are obtained. Moreover, some results about the average scattering number of graphs obtained by graph operations are given.

Semantic RDF Based Integration Framework for Heterogeneous XML Data Sources

A significant amount of data on the Web is in the XML format or may easily be converted to XML or to its variations. XML is still the most appropriate language for data interchange and serialization. In this paper, a new framework which can integrate any heterogeneous XML data sources is presented. Each data source is translated into semantically meaningful regular expressions without changing original data source. Proposed framework has two major phases for data preparation. In the first phase, each data source is processed to obtain regular expressions which accommodate with the design choices that made in target by utilizing known global semantic vocabulary as an input. The second phase combines these regular expressions to get a global schema by preserving the original source data. A regular expression generator tool which produces regular expressions by regarding vocabulary and an integrator tool box which integrates and processes regular expressions, are also introduced.