Dynamic Discovery of Type Classes and Relations in Semantic Web Data

In the Semantic Web, items are represented by uniform resource identifiers (URIs) on the Web in order to describe relationships between things in resource description framework (RDF) graphs. Large scale open RDF data (linked data) describing information about various things are available. Hence, relational discovery research has been conducted using RDF data to explore relationships between things. The SPARQL protocol and RDF query language (SPARQL) is usually employed to process RDF data; however, SPARQL is not suitable for relational discovery using linked data because it is difficult to describe unknown vocabularies and relationships in SPARQL queries. In this paper, we propose an aggregation path search for RDF graphs as an extension of shortest path keyword search. The aggregation path search is devised to find RDF paths by merging multiple resource URIs into commonly reachable nodes in an RDF graph. This search method can discover new relationships between items that are connected by a common node. We present a method for treating equivalent resource URIs as a single resource URI; these equivalent URIs essentially represent the same thing based on equivalence relations in different RDF datasets. In our experiments, we show in the integration of multiple large scale RDF datasets that the integration of equivalent resources enables richer retrieval of aggregated paths.

Fixing the inconsistencies in fuzzy spatiotemporal RDF graph

k-Nearest Keyword Search in RDF Graphs

Internet search is done by exploring the link and keyword frequency. In 2012, Google released Knowledge Graph --Semantic Web. The human reasoning can be enhanced by the use semantic web an emerging area. Most of the current applications link open data views due to which there is huge flow of data in semantic web, particularly Resource Description Framework (RDF) data. In the semantic web research community this leads to design and development of scalable data processing techniques for RDF data. The aim of semantic web is to make available semantically connected data across the globe. This is a review paper giving analysis of techniques implemented to achieve the aim of semantic web, various approaches to processes RDF data. Within the semantic web community, RDF is a common acronym because it forms one of the basic building blocks for forming the web of semantic data, called a graph database. This paper compares various methodologies followed by different researchers along with the results analysis of implemented techniques over different datasets.

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. Since the edges between the entities and the objects in the frequent star pattern are replaced by edges between these entities and the surrogate entity of the compact RDF molecule, the size of the RDF graph is reduced. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude. Additionally, RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved.

The main objective of this paper is to present arguments for and recommendations to representing UNIMARC formats for bibliographic and authority data in RDF (Resource Description Framework), the W3C standard for structuring data in Semantic Web and Linked Data environment. This is a continuation of the work already started by IFLA’s respective groups in representing International Standard Bibliographic Description, and conceptual models Functional Requirements for Bibliographic Records, Functional Requirements for Authority Data and Functional Requirements for Subject Authority Data. The authors highly recommend that the Permanent UNIMARC Committee propose to IFLA the funding of the development of UNIMARC representation in RDF as a research and development project.

Resource Description Framework (RDF) and SPARQL are some of the common terms we hear with the Semantic Web. RDF is the de-facto standard for data representation on the Semantic Web. The universal adoption of this standard can be attributed to the fact that it has a flexible model. Due to the widespread adoption of Semantic web, a sudden steep increase in the amount of RDF data has been witnessed. This massive volume of RDF data can't be handled by the traditional Centralized RDF management systems. Thus comes into the picture the need for Distributed RDF data management systems which can distribute this RDF data on a cluster of nodes. So, the bottlenecks of the Centralized RDF systems regarding volume and efficient querying can be overcome. This paper presents a detailed description and comparison of the different Centralized and Distributed RDF data management systems.

The biological data is growing up to an unprecedented scale, such as microorganism knowledge graph organized by biologists, which is represented by Resource Description Framework (RDF) data model. In this paper, GCM-Bench, a new benchmark to evaluate the performance of general-purpose RDF data management systems on microorganism RDF data is proposed, which consists of microorganism RDF data generator, SPARQL query workloads and automatic test system, that can execute the testing workloads automatically and monitor the resource utilization. Five RDF data management systems are selected for evaluation on different sizes of data using automatic test system. We think GCM-Bench will help microbiologists and system developers to select their proper RDF data management system.

Even initial feedback vertex set problem is NP-complete

Web Semantics is one of the prominent domain in the current era of web. This web comprises of linked open data, which is an interconnection of related data over web and the base of data representation is Resource Description Framework (RDF). Applying machine learning algorithms will enhance the capabilities of semantic web over the concepts to visualize and analyze data over web. It will integrate the capabilities of web semantics and machine learning. In this paper, classification on a sample RDF dataset has been implemented and their results have been analyzed. This will give researcher a baseline to work with machine learning algorithms on semantic web data. The machine learning algorithms like decision tree classifier, random forest classifier along with artificial neural network classification has been analyzed in this paper along with their accuracy measures. The integration of machine learning and semantic web, based on RDF based knowledge graphs is the singularity of the proposed work and represented using experimental results.

Answering top-K query combined keywords and structural queries on RDF graphs

Maximal structure generation of superstructure for semantic triple generated by DEVS ontology in the process industry

PG-explorer: Resource Description Framework data exploration with property graphs

With the wide application of RDF(Resource Description Framework) data, the data volume grows rapidly.Therefore, RDF storage has become a hot research issue in data storage field currently.Distributed storage is an effective way to solve the storage and query of RDF data, and data partition is the premise of data distributed storage.In this paper we use graph clustering idea to realize the effective partition of RDF data.RDF can be described as a directed graph, so in this paper we use P-Rank (Penetrating Rank) algorithm to calculate the similarity of RDF graph node pairss, and then the improved K-means clustering algorithm is implemented to cluster the similarity results, so as to realize the distributed storage of RDF data.The experimental results show that, this method can complete the RDF data partition effectively, makes the intra-cluster similarity is smaller, and the larger the inter-cluster similarity.

Resource Description Framework (RDF) is increasingly being used for representing information on the web. This popularity has made storage of large RDF data a difficult task. To overcome these issues many distributed RDF systems are being proposed that can store and efficiently process Big RDF data. Hadoop framework is widely being used for storing and handling a large amount of RDF data. One of the major obstacles faced while handling this large amount of RDF data is query processing on such large datasets. In this paper, we present JOTR: a SPARQL query optimization technique for Big RDF data using triple pattern reordering on a distributed Hadoop based RDF system. The proposed technique is based on selectivity calculation and has been tested on one of the popular RDF benchmark datasets, LUBM dataset. We have tested JOTR on large sized RDF datasets and compared it with other optimization approaches in respect to the query execution time. From the results, it can be concluded that our approach gives a notable performance on distributed RDF systems and thus is applicable to centralized systems as well.