Related papers: S2RDF: RDF Querying with SPARQL on Spark
The use of large-scale machine learning methods is becoming ubiquitous in many applications ranging from business intelligence to self-driving cars. These methods require a complex computation pipeline consisting of various types of…
In this paper we define a new algorithm to convert an input relational database to an output set of RDF triples. The algorithm can be used to e.g. load CSV data into a financial OWL ontology such as FIBO. The algorithm takes as input a set…
Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level,…
Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose…
The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be "FAIR"…
We present an experimental study of large-scale RDF federations on top of the Bio2RDF data sources, involving 29 data sets with more than four billion RDF triples deployed in a local federation. Our federation is driven by FedX, a highly…
Knowledge graph is an important cornerstone of artificial intelligence. The construction and release of large-scale knowledge graphs in various fields pose new challenges to knowledge graph data management. Due to the maturity and…
In today's world data is being generated at a high rate due to which it has become inevitable to analyze and quickly get results from this data. Most of the relational databases primarily support SQL querying with a limited support for…
With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random…
We propose a visual query language for interactively exploring large-scale knowledge graphs. Starting from an overview, the user explores bar charts through three interactions: class expansion, property expansion, and subject/object…
Massive RDF data sets are becoming commonplace. RDF data is typically generated in social semantic domains (such as personal information management) wherein a fixed schema is often not available a priori. We propose a simple Three-way…
Today's international corporations such as BASF, a leading company in the crop protection industry, produce and consume more and more data that are often fragmented and accessible through Web APIs. In addition, part of the proprietary and…
During the recent years, a number of efficient and scalable frequent itemset mining algorithms for big data analytics have been proposed by many researchers. Initially, MapReduce-based frequent itemset mining algorithms on Hadoop cluster…
In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large…
Embedding tensors in databases has recently gained in significance, due to the rapid proliferation of machine learning methods (including LLMs) which produce embeddings in the form of tensors. To support emerging use cases hybridizing…
We propose a new approach for generating SPARQL queries on RDF knowledge graphs from natural language questions or keyword queries, using a large language model. Our approach does not require fine-tuning. Instead, it uses the language model…
In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms.…
In this paper, we present a novel approach -- called WaterFowl -- for the storage of RDF triples that addresses some key issues in the contexts of big data and the Semantic Web. The architecture of our prototype, largely based on the use of…
Many fields of science rely on relational database management systems to analyze, publish and share data. Since RDBMS are originally designed for, and their development directions are primarily driven by, business use cases they often lack…
Hadoop is emerging as the primary data hub in enterprises, and SQL represents the de facto language for data analysis. This combination has led to the development of a variety of SQL-on-Hadoop systems in use today. While the various…