Related papers: RDFFrames: Knowledge Graph Access for Machine Lear…

Deep Learning has made a great progress for these years. However, it is still difficult to master the implement of various models because different researchers may release their code based on different frameworks or interfaces. In this…

Sharing and reusing research artifacts, such as datasets, publications, or methods is a fundamental part of scientific activity, where heterogeneity of resources and metadata and the common practice of capturing information in unstructured…

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of…

Updates in RDF stores have recently been standardised in the SPARQL 1.1 Update specification. However, computing answers entailed by ontologies in triple stores is usually treated orthogonal to updates. Even the W3C's recent SPARQL 1.1…

The emerging Web of Data utilizes the web infrastructure to represent and interrelate data. The foundational standards of the Web of Data include the Uniform Resource Identifier (URI) and the Resource Description Framework (RDF). URIs are…

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive…

Natural language question-answering over RDF data has received widespread attention. Although there have been several studies that have dealt with a small number of aggregate queries, they have many restrictions (i.e., interactive…

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…

This vision paper proposes KGNet, an on-demand graph machine learning (GML) as a service on top of RDF engines to support GML-enabled SPARQL queries. KGNet automates the training of GML models on a KG by identifying a task-specific…

Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model,…

Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling…

Procedural knowledge describes how to accomplish tasks and mitigate problems. Such knowledge is commonly held by domain experts, e.g. operators in manufacturing who adjust parameters to achieve quality targets. To the best of our knowledge,…

Knowledge Graphs (KGs) contain vast amounts of linked resources that encode knowledge in various domains, which can be queried and searched for using specialized languages like SPARQL, a query language developed to query KGs. Existing…

Relational Deep Learning (RDL) is a promising approach for building state-of-the-art predictive models on multi-table relational data by representing it as a heterogeneous temporal graph. However, commonly used Graph Neural Network models…

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of…

Keyword search provides ordinary users an easy-to-use interface for querying RDF data. Given the input keywords, in this paper, we study how to assemble a query graph that is to represent user's query intention accurately and efficiently.…

Relational databases are extensively utilized in a variety of modern information system applications, and they always carry valuable data patterns. There are a huge number of data mining or machine learning tasks conducted on relational…

Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (edge et al., 2024). Despite decades…

Graph traversals are a basic but fundamental ingredient for a variety of graph algorithms and graph-oriented queries. To achieve the best possible query performance, they need to be implemented at the core of a database management system…

Knowledge graph construction (KGC) from (semi-)structured data is challenging, and facilitating user involvement is an issue frequently brought up within this community. We cannot deny the progress we have made with respect to (declarative)…