12 citations · 52 across the 13 of their papers we have counts for
14 papers
Evidence-Supported Credit Risk Report Generation Using News-Centric Financial Knowledge Graphs
Rocio Jimenez-Villen, Ziwei Xu, Ying Chen +2
Financial markets evolve in response to real-world events reported in news, yet these drivers often remain implicit in text. To better explain market dynamics, event-market relatio…
A Unified Benchmark for Evaluating Knowledge Graph Construction Methods and Graph Neural Networks
Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2
Knowledge graphs automatically constructed from text are increasingly used in real-world applications. However, their inherent noise, fragmentation, and semantic inconsistencies si…
Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs
Othmane Kabal, Mounira Harzallah, Fabrice Guillet +2
Graph Self-Supervised Learning (GSSL) offers a powerful paradigm for learning graph representations without labeled data. However, existing work assumes clean, manually curated gra…
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation
Ying Chen, Ziwei Xu, Kotaro Inoue +1
Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confou…
Negative Sampling in Knowledge Graph Representation Learning: A Review
Tiroshan Madushanka, Ryutaro Ichise
Knowledge Graph Representation Learning (KGRL), or Knowledge Graph Embedding (KGE), is essential for AI applications such as knowledge construction and information retrieval. These…
TabEAno: Table to Knowledge Graph Entity Annotation
Phuc Nguyen, Natthawut Kertkeidkachorn, Ryutaro Ichise +1
In the Open Data era, a large number of table resources have been made available on the Web and data portals. However, it is difficult to directly utilize such data due to the ambi…
MTab: Matching Tabular Data to Knowledge Graph using Probability Models
Phuc Nguyen, Natthawut Kertkeidkachorn, Ryutaro Ichise +1
This paper presents the design of our system, namely MTab, for Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2019). MTab combines the voting algorithm…
Combination of Unified Embedding Model and Observed Features for Knowledge Graph Completion
Takuma Ebisu, Ryutaro Ichise
Knowledge graphs are useful for many artificial intelligence tasks but often have missing data. Hence, a method for completing knowledge graphs is required. Existing approaches inc…
Graph Pattern Entity Ranking Model for Knowledge Graph Completion
Takuma Ebisu, Ryutaro Ichise
Knowledge graphs have evolved rapidly in recent years and their usefulness has been demonstrated in many artificial intelligence tasks. However, knowledge graphs often have lots of…
EmbNum: Semantic labeling for numerical values with deep metric learning
Phuc Nguyen, Khai Nguyen, Ryutaro Ichise +1
Semantic labeling for numerical values is a task of assigning semantic labels to unknown numerical attributes. The semantic labels could be numerical properties in ontologies, inst…
Deep Reinforcement Learning Boosted by External Knowledge
Nicolas Bougie, Ryutaro Ichise
Recent improvements in deep reinforcement learning have allowed to solve problems in many 2D domains such as Atari games. However, in complex 3D environments, numerous learning epi…
TorusE: Knowledge Graph Embedding on a Lie Group
Takuma Ebisu, Ryutaro Ichise
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding model…
Integrating Know-How into the Linked Data Cloud
Paolo Pareti, Benoit Testu, Ryutaro Ichise +2
This paper presents the first framework for integrating procedural knowledge, or "know-how", into the Linked Data Cloud. Know-how available on the Web, such as step-by-step instruc…
Sub-Classifier Construction for Error Correcting Output Code Using Minimum Weight Perfect Matching
Patoomsiri Songsiri, Thimaporn Phetkaew, Ryutaro Ichise +1
Multi-class classification is mandatory for real world problems and one of promising techniques for multi-class classification is Error Correcting Output Code. We propose a method…