Towards a general web rule language

2007

In this paper we describe a distributed architecture consisting of a combination of scripting tools that interact with each other in order to help to find and query decentralized RDF data. Thanks to this architecture, anyone can participate in the collaborative discovery of Semantic Web documents by simply browsing the web. This system is useful for dynamic discovery of RDF content and can provide a useful source of RDF documents for other Semantic Web applications. Key components of this architecture are the Semantic Radar plugin used for discovery of Semantic Web data, Ping The Semantic Web service for aggregating locations of RDF data, and services using RDF data illustrated here with the example of doap:store. 4 http://foaf-project.org/ 5

2020

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is a huge adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ different mechanisms to implement key components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is key for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a key optimization component of the RDF storage solutions. Fi...

International Journal of Computer and Information Engineering Vol:13, No:5, 2019

Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.

Lecture Notes in Computer Science, 2006

This article is firstly an introduction into query languages for the Semantic Web, secondly an in-depth comparison of the languages introduced. Only RDF query languages are considered because, as of the writing of this paper, query languages for other Semantic Web data modeling formalisms, especially OWL, are still an open research issue, and only a very small number of, furthermore incomplete, proposals for querying Semantic Web data modeled after other formalisms than RDF exist. The limitation to a few RDF query languages is motivated both by the objective of an in-depth comparison of the languages addressed and by space limitations. During the three years before the writing of this article, more than three dozen proposals for RDF query languages have been published! Not only such a large number, but also the often immature nature of the proposals makes the focus on few, but representative languages a necessary condition for a non-trivial comparison. For this article, the following RDF query languages have been, admittedly subjectively, selected: Firstly, the "relational" or "pattern-based" query languages SPARQL, RQL, TRIPLE, and Xcerpt; secondly the reactive rule query language Algae; thirdly and last the "navigational access" query language Versa. Although subjective, this choice is arguably a good coverage of the diverse language paradigms considered for querying RDF data. It is the authors' hope and expectation, that this comparison will motivate and trigger further similar studies, thus completing the present article and overcoming its limitation. 5 Sections 2 and 3 are shortend versions of corresponding sections of [5]. 7 The same example is used in [5].

World Wide Web Conference Series, 2002

Real-scale Semantic Web applications, such as Knowledge Portals and E-Marketplaces, require the management of large volumes of metadata, i.e., information describing the available Web content and services. Better knowledge about their meaning, usage, accessibility or quality will considerably facilitate an automated processing of Web resources. The Resource Description Framework (RDF) enables the creation and exchange of metadata as normal Web

Services Transactions on Big Data, 2015

The processing of large volumes of RDF data requires an efficient storage and query processing engine that can scale well with the volume of data. The initial attempts to address this issue focused on optimizing native RDF stores as well as conventional relational database management systems. But as the volume of RDF data grew to exponential proportions, the limitations of these systems became apparent and researchers began to focus on using big data analysis tools, most notably Hadoop, to process RDF data. Various studies and benchmarks that evaluate these tools for RDF data processing have been published. In the past two and half years, however, heavy users of big data systems, like Facebook, noted limitations with the query performance of these big data systems and began to develop new distributed query engines for big data that do not rely on map-reduce. Facebook's Presto is one such example. This paper proposes an architecture based on Presto, Presto-RDF, that can be used to process big RDF data. We evaluate the performance of Presto in processing big RDF data against Apache Hive. A comparative analysis was also conducted against 4store, a native RDF store. To evaluate the performance Presto for big RDF data processing, a map-reduce program and a compiler, based on Flex and Bison, were implemented. The map-reduce program loads RDF data into HDFS while the compiler translates SPARQL queries into a subset of SQL that Presto (and Hive) can understand. The evaluation was done with RDF datasets of size 10, 20, and 30 million triples. The results of the experiments show that Presto-RDF has a much higher performance than Hive and can be used to process big RDF data.

International Journal of Computer and Information Engineering, Vol:13, No:5, 2019

Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.

HAL (Le Centre pour la Communication Scientifique Directe), 2018

Web browsers represent an under-exploited data deposit. In this paper, we propose Snob, a decentralized query execution engine for SPARQL query execution over RDF data hosted in a P2P network of web browsers.

World Wide Web

The Semantic Web is gaining increasing interest to fulfill the need of sharing, retrieving, and reusing information. In this context, the Resource Description Framework (RDF) has been conceived to provide an easy way to represent any kind of data and metadata, according to a lightweight model and syntaxes for serialization (RDF/XML, N3, etc.). Despite RDF has the advantage of being general and simple, it cannot be used as a storage model as it is, since it can be easily shown that even simple management operations involve serious performance limitations. In this paper we present a framework which provides a flexible and persistent layer relying on a novel storage model that guarantees good scalability and performance of query evaluation. The approach is based on the notion of construct, that represents a concept of the domain of interest. This makes the approach easily extensible and independent from the specific knowledge representation language. Based on this representation, reasoning capabilities are supported by a rule-based engine. Finally we present experimental results over real world scenarios to demonstrate the feasibility of the approach.

1999