Quantitative data is important in many domains. Information extraction methods draw
structured data from documents. However, the extraction of quantities and their contexts has …

In a hyper-relational knowledge graph, a triplet can be associated with a set of qualifiers,
where a qualifier is composed of a relation and an entity, providing auxiliary information for …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …

Learning rule-based systems plays a pivotal role in knowledge graph completion (KGC).
Existing rule-based systems restrict the input of the system to structural knowledge only …

Quantities are more than numeric values. They represent measures for entities, expressed in
numbers with associated units. Search queries often include quantities, such as athletes …

To strengthen public trust and counter disinformation, computational fact-checking,
leveraging digital data sources, attracts interest from the journalists and the computer …

Vinh Thinh Ho is an applied scientist at Amazon Development Center, working in Alexa AI-
NLU team. He completed his PhD at Max Planck Institute for Informatics, under the …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves the far more complex …

Graph databases are the primary workhorse for storing and organizing structured
information over real-world entities. The core task on graph databases is query answering …

Extracting information from the Web remains a critical component in knowledge harvesting
systems for building curated knowledge structures, such as Knowledge Bases (KBs), and …