Bridging the gap for retrieving DBpedia data (original) (raw)
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Qsense - Learning Semantic Web Concepts by Querying DBpedia
Proceedings of the 4th International Conference on Data Communication Networking, 10th International Conference on e-Business and 4th International Conference on Optical Communication Systems, 2013
This paper describes Qsense-an educational and research Web application, developed to ease the interrogation of DBpedia for users intending to learn the most important concepts regarding the Semantic Web, especially to increase the knowledge about DBpedia in a pragmatic way. Additionally, by providing a compelling Web interface, Qsense offers the possibility to explore the DBpedia's ontology structure and to practice various SPARQL queries by expert and especially nonexpert users.
DBpedia SPARQL Benchmark–Performance Assessment with Real Queries on Real Data
2011
Triple stores are the backbone of increasingly many Data Web applications. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in general. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triple store implementations.
DBpedia and the live extraction of structured data from Wikipedia
2012
Purpose–DBpedia extracts structured information from Wikipedia, interlinks it with other knowledge bases and freely publishes the results on the web using Linked Data and SPARQL. However, the DBpedia release process is heavyweight and releases are sometimes based on several months old data. DBpedia-Live solves this problem by providing a live synchronization method based on the update stream of Wikipedia. This paper seeks to address these issues.
A Survey Paper on Effective Query Processing for Semantic Web Data using Hadoop Components
International Journal For Research In Applied Science & Engineering Technology, 2020
The combination of the two quick creating logical exploration regions Semantic Web and Web Mining is called Semantic Web Mining. The immense increment in the measure of Semantic Web information turned into an ideal objective for some specialists to apply Data-Mining methods on it. Semantic Web Data is an extra for the WorldWide Web, and the primary goal of this is to make the web information machine-comprehensible. Resource Description Framework (RDF) is one of the advancements used to encode and speak to the semantics information as metadata. It's most likely to host the semantic web data on cloud due to its vast requirement, and also it can be managed well in terms of storage and evaluation. Map-reduce is a programming model that is well known for its scalability, flexibility, parallel processing, and cost-effective solution. Hadoop and Spark are the popular open-source tools for handling (Map-Reduce) and storing (HDFS) a huge-amount of data. Semantic web data can be processed using the SPARQL, which is a primary query language for processing the RDF. In terms of performance, SPARQL has a significant drawback comparing to Map-reduce. For Querying the RDF data, we use Spark and Hadoop components (PIG and HIVE). Where Considering Directed Acyclic Graph (DAG) scheduler as a specific feature for In-memory processing in spark. In this paper, evaluate and analyse performance results using RDF data, which contains 5000 triples by executing the benchmark queries in PIG, HIVE, and SPARK. A scalable and faster framework can be obtained based on practical evaluation and analysis.
Efficient Parallel Processing of Analytical Queries on Linked Data
Lecture Notes in Computer Science, 2013
Linked data has become one of the most successful movements of the Semantic Web community. RDF and SPARQL have been established as de-facto standards for representing and querying linked data and there exists quite a number of RDF stores and SPARQL engines that can be used to work with the data. However, for many types of queries on linked data these stores are not the best choice regarding query execution times. For example, users are interested in analytical tasks such as profiling or finding correlated entities in their datasets. In this paper we argue that currently available RDF stores are not optimal for such scan-intensive tasks. In order to address this issue, we discuss query evaluation techniques for linked data exploiting the features of modern hardware architectures such as big memory and multi-core processors. Particularly, we describe parallelization techniques as part of our CameLOD system. Furthermore, we compare our system with the well-known linked data stores Virtuoso and RDF-3X by running different analytical queries on the DBpedia dataset and show that we can outperform these systems significantly.
2012
Purpose – DBpedia extracts structured information from Wikipedia, interlinks it with other knowledge bases and freely publishes the results on the Web using Linked Data and SPARQL. However, the DBpedia release process is heavy-weight and releases are sometimes based on several months old data. DBpedia-Live solves this problem by providing a live synchronization method based on the update stream of Wikipedia. Design/methodology/approach – Wikipedia provides DBpedia with a continuous stream of updates, i.e. a stream of recently updated articles. DBpedia-Live processes that stream on the fly to obtain RDF data and stores the extracted data back to DBpedia. DBpedia-Live publishes the newly added/deleted triples in files, in order to enable synchronization between our DBpedia endpoint and other DBpedia mirrors. Findings – During the realization of DBpedia-Live we learned, that it is crucial to process Wikipedia updates in a priority queue. Recently-updated Wikipedia articles should have ...
Semantic wonder cloud: Exploratory search in DBpedia
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
Inspired by the Google Wonder Wheel 3 , in this paper we present Semantic Wonder Cloud (SWOC): a tool that helps users in knowledge exploration within the DBpedia dataset by adopting a hybrid approach. We describe both the architecture and the user interface. The system exploits not only pure semantic connections in the underlying RDF graph but it mixes the meaning of such information with external non-semantic knowledge sources, such as web search engines and tagging systems. Semantic Wonder Cloud allows the user to explore the relations between resources of knowledge domain via a simple and intuitive graphical interface.
LOQUS: Linked Open Data SPARQL Querying System
2010
Abstract The LOD cloud is gathering a lot of momentum, with the number of contributors growing manifold. Many prominent data providers have submitted and linked their data to other dataset with the help of manual mappings. The potential of the LOD cloud is enormous ranging from challenging AI issues such as open domain question answering to automated knowledge discovery. We believe that there is not enough technology support available to effectively query the LOD cloud.