1 code implementation • COLING 2022 • Xixin Hu, Xuan Wu, Yiheng Shu, Yuzhong Qu
Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing.
no code implementations • 16 May 2024 • Jianhao Chen, Haoyuan Ouyang, Junyang Ren, Wentao Ding, Wei Hu, Yuzhong Qu
In addition, we evaluate the performance of LLMs for direct temporal fact extraction and get unsatisfactory results.
1 code implementation • 18 Mar 2024 • Xiang Huang, Sitao Cheng, Shanshan Huang, Jiayu Shen, Yong Xu, Chaoyun Zhang, Yuzhong Qu
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success.
1 code implementation • 3 Feb 2024 • Wentao Ding, Jinmao Li, Liangchuan Luo, Yuzhong Qu
We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction.
no code implementations • 18 Dec 2023 • Jianhao Chen, Junyang Ren, Wentao Ding, Haoyuan Ouyang, Wei Hu, Yuzhong Qu
Temporal facts, which are used to describe events that occur during specific time periods, have become a topic of increased interest in the field of knowledge graph (KG) research.
1 code implementation • 24 Oct 2023 • Xiang Huang, Sitao Cheng, Yuheng Bao, Shanshan Huang, Yuzhong Qu
We design a logic form in Python format called PyQL to represent the reasoning process of numerical reasoning questions.
1 code implementation • 13 Jun 2023 • Xiang Huang, Sitao Cheng, Yiheng Shu, Yuheng Bao, Yuzhong Qu
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA.
1 code implementation • 18 Apr 2023 • Jianhao Chen, Junyang Ren, Wentao Ding, Yuzhong Qu
Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities.
1 code implementation • 23 Nov 2022 • Xiao Li, Yin Zhu, Sichen Liu, Jiangzhou Ju, Yuzhong Qu, Gong Cheng
Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community.
2 code implementations • 24 Oct 2022 • Yiheng Shu, Zhiwei Yu, Yuhan Li, Börje F. Karlsson, Tingting Ma, Yuzhong Qu, Chin-Yew Lin
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
no code implementations • 10 Oct 2022 • Wentao Ding, Hao Chen, Huayu Li, Yuzhong Qu
Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years.
1 code implementation • ACL 2022 • Xiao Li, Gong Cheng, Ziheng Chen, Yawei Sun, Yuzhong Qu
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text.
1 code implementation • Findings (EMNLP) 2021 • Wentao Ding, Jianhao Chen, Jinmao Li, Yuzhong Qu
The understanding of time expressions includes two sub-tasks: recognition and normalization.
1 code implementation • Findings (EMNLP) 2021 • Zixian Huang, Ao Wu, Yulin Shen, Gong Cheng, Yuzhong Qu
Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description.
1 code implementation • 22 Apr 2020 • Zequn Sun, Jiacheng Huang, Wei Hu, Muchao Chen, Lingbing Guo, Yuzhong Qu
We refer to such contextualized representations of a relation as edge embeddings and interpret them as translations between entity embeddings.
1 code implementation • 31 Mar 2020 • Yawei Sun, Lingling Zhang, Gong Cheng, Yuzhong Qu
This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing.
no code implementations • 8 Mar 2020 • Qingxia Liu, Gong Cheng, Kalpa Gunaratna, Yuzhong Qu
In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark.
1 code implementation • 8 Mar 2020 • Qingxia Liu, Gong Cheng, Yuzhong Qu
Entity summarization has been a prominent task over knowledge graphs.
1 code implementation • 21 Feb 2020 • Jiacheng Huang, Wei Hu, Zhifeng Bao, Yuzhong Qu
Knowledge bases (KBs) store rich yet heterogeneous entities and facts.
1 code implementation • 20 Nov 2019 • Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei zhang, Yuzhong Qu
As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures.
Ranked #29 on Entity Alignment on DBP15k zh-en
no code implementations • 18 Oct 2019 • Qingxia Liu, Gong Cheng, Kalpa Gunaratna, Yuzhong Qu
This has motivated fruitful research on automated generation of summaries for entity descriptions to satisfy users' information needs efficiently and effectively.
no code implementations • 29 Aug 2019 • Jinchi Chen, Xiaxia Wang, Gong Cheng, Evgeny Kharlamov, Yuzhong Qu
Reusing published datasets on the Web is of great interest to researchers and developers.
no code implementations • IJCNLP 2019 • Jiwei Ding, Wei Hu, Qixin Xu, Yuzhong Qu
Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results.
no code implementations • IJCNLP 2019 • Zixian Huang, Yulin Shen, Xiao Li, Yuang Wei, Gong Cheng, Lin Zhou, Xin-yu Dai, Yuzhong Qu
Scenario-based question answering (SQA) has attracted increasing research attention.
no code implementations • 2 Jul 2019 • Xiaxia Wang, Jinchi Chen, Shuxin Li, Gong Cheng, Jeff Z. Pan, Evgeny Kharlamov, Yuzhong Qu
Reusing existing datasets is of considerable significance to researchers and developers.
1 code implementation • 6 Jun 2019 • Qingheng Zhang, Zequn Sun, Wei Hu, Muhao Chen, Lingbing Guo, Yuzhong Qu
Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs.
1 code implementation • 30 Oct 2018 • Lingbing Guo, Qingheng Zhang, Weiyi Ge, Wei Hu, Yuzhong Qu
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of $(subject, relation, object)$.
no code implementations • COLING 2018 • Yawei Sun, Gong Cheng, Yuzhong Qu
Complex questions in reading comprehension tasks require integrating information from multiple sentences.
no code implementations • 15 May 2018 • Seyedamin Pouriyeh, Mehdi Allahyari, Qingxia Liu, Gong Cheng, Hamid Reza Arabnia, Yuzhong Qu, Krys Kochut
Ontologies have been widely used in numerous and varied applications, e. g., to support data modeling, information integration, and knowledge management.
Information Retrieval