no code implementations • 18 Dec 2023 • Xinyue Zhang, Pan Hu, Yavor Nenov, Ian Horrocks
Materialisation facilitates Datalog reasoning by precomputing all consequences of the facts and the rules so that queries can be directly answered over the materialised facts.
1 code implementation • 10 Nov 2023 • Yifei Xu, Yuning Chen, Xumiao Zhang, Xianshang Lin, Pan Hu, Yunfei Ma, Songwu Lu, Wan Du, Zhuoqing Mao, Ennan Zhai, Dennis Cai
We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios.
no code implementations • 11 May 2023 • Xinyue Zhang, Pan Hu, Yavor Nenov, Ian Horrocks
In this paper, we provide algorithms that exploit hypertree decompositions for the materialisation and incremental evaluation of Datalog programs.
1 code implementation • 12 Jan 2022 • Dingmin Wang, Pan Hu, Przemysław Andrzej Wałęga, Bernardo Cuenca Grau
DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years.
1 code implementation • 30 Sep 2020 • Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter, Denvar Antonyrajah, Ian Horrocks
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web.
no code implementations • 28 Jan 2020 • Xiaoli Liu, Pan Hu, Zhi Mao, Po-Chih Kuo, Peiyao Li, Chao Liu, Jie Hu, Deyu Li, Desen Cao, Roger G. Mark, Leo Anthony Celi, Zhengbo Zhang, Feihu Zhou
This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.
no code implementations • 27 Aug 2019 • Pan Hu, Jacopo Urbani, Boris Motik, Ian Horrocks
Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules.
no code implementations • 6 Nov 2018 • Pan Hu, Boris Motik, Ian Horrocks
The semina\"ive algorithm can materialise all consequences of arbitrary datalog rules, and it also forms the basis for incremental algorithms that update a materialisation as the input facts change.
no code implementations • 10 Nov 2017 • Pan Hu, Boris Motik, Ian Horrocks
The Delete/Rederive (DRed) and the Backward/Forward (B/F) algorithms solve this problem for general datalog, but both contain steps that evaluate rules 'backwards' by matching their heads to a fact and evaluating the partially instantiated rule bodies as queries.