相关论文: SDM-RDFizer: An RML Interpreter for the Efficient …
Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability…
Resource Description Framework (RDF) can seen as a solution in today's landscape of knowledge representation research. An RDF language has symmetrical features because subjects and objects in triples can be interchangeably used. Moreover,…
RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles…
In this paper, we introduce AutoRDF2GML, a framework designed to convert RDF data into data representations tailored for graph machine learning tasks. AutoRDF2GML enables, for the first time, the creation of both content-based features --…
The RDF Mapping Language (RML) allows to map semi-structured data to RDF knowledge graphs. Besides CSV, JSON and XML, this also includes the mapping of spreadsheet tables. Since spreadsheets have a complex data model and can become rather…
In this paper, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML. We look into details on their differences…
Developing and testing modern RDF-based applications often requires access to RDF datasets with certain characteristics. Unfortunately, it is very difficult to publicly find domain-specific knowledge graphs that conform to a particular set…
Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query…
Stream-reasoning query languages such as CQELS and C-SPARQL enable query answering over RDF streams. Unfortunately, there currently is a lack of efficient RDF stream generators to feed RDF stream reasoners. State-of-the-art RDF stream…
Increasing amounts of scientific and social data are published in the Resource Description Framework (RDF). Although the RDF data can be queried using the SPARQL language, even the SPARQL-based operation has a limitation in implementing…
Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one…
Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental challenge arises from the substantial differences in graph…
BACKGROUND: Modern distributed systems replicate data across multiple execution sites. Business requirements and resource constraints often necessitate mixing different languages across replica sites. To facilitate the management of…
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…
Current approaches for knowledge graph construction with RML focus on full RDF graph materialization without considering user queries. As a result, mapping engines are inefficient in dynamic query environments, materializing large graphs…
Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various semantic and other applications requires Data Management Systems (DMSs) to efficiently store and process the content of KGs. However, the increases in the…
Relational databases (RDBs) are widely regarded as the gold standard for storing structured information. Consequently, predictive tasks leveraging this data format hold significant application promise. Recently, Relational Deep Learning…
Cloud systems generate large, heterogeneous log data containing critical infrastructure, application, and security information. Transforming these logs into RDF triples enables their integration into knowledge graphs, improving…
Imbalanced data are commonly present in real-world applications. While data synthesis can effectively mitigate data scarcity for rare classes, and LLMs have revolutionized text generation, the application of LLMs to the synthesis of…
The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new…