Subscriber Profiling for Connection Service Providers by Considering Individuals and Different Timeframes

IEICE Trans. Inf. & Syst.

Kodaz

2023

Companies listed on the stock exchange are required to share their annual reports with the U.S. Securities and Exchange Commission (SEC) within the first three months following the fiscal year. These reports, namely 10-K Filings, are presented to public interest by the SEC through an Electronic Data Gathering, Analysis, and Retrieval database. 10-K Filings use standard file formats (xbrl, html, pdf) to publish the financial reports of the companies. Although the file formats propose a standard structure, the content and the meta-data of the financial reports (e.g. tag names) is not strictly bound to a pre-defined schema. This study proposes a data collection and data preprocessing method to semantify the financial reports and use the collected data for further analysis (i.e. machine learning). The analysis of eight different datasets, which were created during the study, are presented using the proposed data transformation methods. As a use case, based on the datasets, five different machine learning algorithms were utilized to predict the existence of the corresponding company in the S&P 500 index. According to the strong machine learning results, the dataset generation methodology is successful and the datasets are ready for further use.

Section: Discussionmentioning

confidence: 99%

A Methodology on Converting 10-K Filings into a Machine Learning Dataset and Its Applications

KACAR

IEICE Trans. Inf. & Syst.

Kodaz

2023

“…The next steps in this research involve a semantic analysis [33] and a semantic ranking [34] of the collected SPARQL endpoints. This will help to have a better understanding about the content in the underlying linked data sources, and it will be possible to classify SPARQL endpoints according to their domain or context [35], [36].…”

Section: Discussionmentioning

confidence: 99%

SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

IEICE Trans. Inf. & Syst.

Doğdu

Kodaz

et al. 2017

SUMMARYLinked 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.

“…As future work, our next step will be a semantic analysis [38,41], and ranking [18] of the collected SPARQL endpoints. This will help to get a better understanding about the contents of the discovered Linked Data sources, making it possible to classify SPARQL endpoints according to their domain or context [31][32][33]. Moreover, since it was observed that a small number of endpoints was not discovered by SpEnD (due to search engines' limitations) but were still available on the static repositories, we will put some effort into improving our overall approach to better include, in an automatic way, endpoints and datasets listed only on those repositories.…”

Section: Comparison Of Spend With Existing Sparql Endpoints Repositoriesmentioning

confidence: 99%

A Discovery and Analysis Engine for Semantic Web

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

Kamilaris

Doğdu

et al. 2018

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. CCS CONCEPTS • Information systems → Web searching and information discovery;