activity
20202024
most citedPractical Reasoning in DatalogMTL

2 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LO2024★ 2 cited

Practical Reasoning in DatalogMTL

Dingmin Wang, Przemysław A. Wałęga, Pan Hu +1

DatalogMTL is an extension of Datalog with metric temporal operators that has found an increasing number of applications in recent years. Reasoning in DatalogMTL is, however, of hi…

cs.AI2023★ 1 cited

Working Memory Capacity of ChatGPT: An Empirical Study

Dongyu Gong, Xingchen Wan, Dingmin Wang

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In thi…

cs.DB2022

Seminaive Materialisation in DatalogMTL

Dingmin Wang, Przemysław Andrzej Wałęga, Bernardo Cuenca Grau

DatalogMTL is an extension of Datalog with metric temporal operators that has found applications in temporal ontology-based data access and query answering, as well as in stream re…

cs.LG2022

An Empirical Study of Retrieval-enhanced Graph Neural Networks

Dingmin Wang, Shengchao Liu, Hanchen Wang +5

Graph Neural Networks (GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby th…

cs.AI2022

MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

Dingmin Wang, Pan Hu, Przemysław Andrzej Wałęga +1

DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge repres…

cs.CL2021★ 1 cited

A Template-guided Hybrid Pointer Network for Knowledge-basedTask-oriented Dialogue Systems

Dingmin Wang, Ziyao Chen, Wanwei He +3

Most existing neural network based task-oriented dialogue systems follow encoder-decoder paradigm, where the decoder purely depends on the source texts to generate a sequence of wo…

cs.CL2020★ 2 cited

Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition

Dingmin Wang, Chenghua Lin, Qi Liu +1

We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design f…