Related papers: Managing FAIR Knowledge Graphs as Polyglot Data En…
RDF and property graph models have many similarities, such as using basic graph concepts like nodes and edges. However, such models differ in their modeling approach, expressivity, serialization, and the nature of applications. RDF is the…
Knowledge-intensive NLP tasks can benefit from linking natural language text with facts from a Knowledge Graph (KG). Although facts themselves are language-agnostic, the fact labels (i.e., language-specific representation of the fact) in…
Drug repurposing is often framed as a candidate identification task, but existing approaches provide limited guidance for distinguishing biologically plausible candidates from historically well-connected ones. Here we introduce DrugKLM, a…
Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various…
Digital research data management is increasingly integrated across universities and research institutions, addressing the handling of research data throughout its lifecycle according to the FAIR data principles (Findable, Accessible,…
The RDF-to-text task has recently gained substantial attention due to continuous growth of Linked Data. In contrast to traditional pipeline models, recent studies have focused on neural models, which are now able to convert a set of RDF…
The class of queries for detecting path is an important as those can extract implicit binary relations over the nodes of input graphs. Most of the path querying languages used by the RDF community, like property paths in W3C SPARQL 1.1 and…
Data integration is one of the main problems in distributed data sources. An approach is to provide an integrated mediated schema for various data sources. This research work aims at developing a framework for defining an integrated schema…
Multi-view multi-label feature selection aims to identify informative features from heterogeneous views, where each sample is associated with multiple interdependent labels. This problem is particularly important in machine learning…
Knowledge graphs offer an excellent solution for representing the lexical-semantic structures of lexicographic data. However, working with the SPARQL query language represents a considerable hurdle for many non-expert users who could…
Resource allocation in business process management involves assigning resources to open tasks while considering factors such as individual roles, aptitudes, case-specific characteristics, and regulatory constraints. Current information…
In recent years, there have been valuable efforts and contributions to make the process of RDF knowledge graph creation traceable and transparent; extending and applying declarative mapping languages is an example. One challenging step is…
In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph model by only transmitting model parameters. A pain point of…
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…
Long Chain-of-Thought (LCoT), achieved by Reinforcement Learning with Verifiable Rewards (RLVR), has proven effective in enhancing the reasoning capabilities of Large Language Models (LLMs). However, reasoning in current LLMs is primarily…
The Resource Description Framework (RDF) is a W3C standard for representing graph-structured data, and SPARQL is the standard query language for RDF. Recent advances in Information Extraction, Linked Data Management and the Semantic Web…
Enterprises rely on RDF knowledge graphs and SPARQL to expose operational data through natural language interfaces, yet public KGQA benchmarks do not reflect proprietary schemas, prefixes, or query distributions. We present PIPE-RDF, a…
Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance…
We present a collaborative agentic GraphRAG framework for expert analysis of commercial registry data. Public registries are often formally accessible, yet difficult to use in practice because they combine structured records with large…
This paper reviews the changes for database technology represented by the current development of the draft international standard ISO 39075 (Database Languages - GQL), which seeks a unified specification for property graphs and knowledge…