12 citations · 38 across the 12 of their papers we have counts for
15 papers
EvoIdeator: Evolving Scientific Ideas through Checklist-Grounded Reinforcement Learning
Andreas Sauter, Yuyue Zhao, Jacopo Urbani +5
Scientific idea generation is a cornerstone of autonomous knowledge discovery, yet the iterative evolution required to transform initial concepts into high-quality research proposa…
EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery
Yougang Lyu, Xi Zhang, Xinhao Yi +11
The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform complex end-to-end scientific discovery tasks requiring coordination of specialized rol…
Probabilistic Reasoning at Scale: Trigger Graphs to the Rescue
Efthymia Tsamoura, Jaehun Lee, Jacopo Urbani
The role of uncertainty in data management has become more prominent than ever before, especially because of the growing importance of machine learning-driven applications that pro…
Chasing Streams with Existential Rules
Jacopo Urbani, Markus Krötzsch, Thomas Eiter
We study reasoning with existential rules to perform query answering over streams of data. On static databases, this problem has been widely studied, but its extension to rapidly c…
Tribrid: Stance Classification with Neural Inconsistency Detection
Song Yang, Jacopo Urbani
We study the problem of performing automatic stance classification on social media with neural architectures such as BERT. Although these architectures deliver impressive results,…
Tab2Know: Building a Knowledge Base from Tables in Scientific Papers
Benno Kruit, Hongyu He, Jacopo Urbani
Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present T…
Materializing Knowledge Bases via Trigger Graphs
Efthymia Tsamoura, David Carral, Enrico Malizia +1
The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs), like Knowledge Graphs (KGs), to tackle important tasks like query answering under de…
Adaptive Low-level Storage of Very Large Knowledge Graphs
Jacopo Urbani, Ceriel Jacobs
The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Triden…
Checking Chase Termination over Ontologies of Existential Rules with Equality
David Carral, Jacopo Urbani
The chase is a sound and complete algorithm for conjunctive query answering over ontologies of existential rules with equality. To enable its effective use, we can apply acyclicity…
Datalog Reasoning over Compressed RDF Knowledge Bases
Pan Hu, Jacopo Urbani, Boris Motik +1
Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all…
Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version)
Benno Kruit, Peter Boncz, Jacopo Urbani
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the…
Word Sense Disambiguation with LSTM: Do We Really Need 100 Billion Words?
Minh Le, Marten Postma, Jacopo Urbani
Recently, Yuan et al. (2016) have shown the effectiveness of using Long Short-Term Memory (LSTM) for performing Word Sense Disambiguation (WSD). Their proposed technique outperform…
Expressive Stream Reasoning with Laser
Hamid R. Bazoobandi, Harald Beck, Jacopo Urbani
An increasing number of use cases require a timely extraction of non-trivial knowledge from semantically annotated data streams, especially on the Web and for the Internet of Thing…
KOGNAC: Efficient Encoding of Large Knowledge Graphs
Jacopo Urbani, Sourav Dutta, Sairam Gurajada +1
Many Web applications require efficient querying of large Knowledge Graphs (KGs). We propose KOGNAC, a dictionary-encoding algorithm designed to improve SPARQL querying with a judi…
Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)
Jacopo Urbani, Ceriel Jacobs, Markus Krötzsch
The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, wh…