no code implementations • 22 Dec 2023 • Nico Potyka, Yuqicheng Zhu, Yunjie He, Evgeny Kharlamov, Steffen Staab
Large-language models (LLMs) can support a wide range of applications like conversational agents, creative writing or general query answering.
no code implementations • 2 Aug 2023 • Baifan Zhou, Nikolay Nikolov, Zhuoxun Zheng, Xianghui Luo, Ognjen Savkovic, Dumitru Roman, Ahmet Soylu, Evgeny Kharlamov
Industry 4. 0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety.
no code implementations • 2 Aug 2023 • Zhipeng Tan, Baifan Zhou, Zhuoxun Zheng, Ognjen Savkovic, Ziqi Huang, Irlan-Grangel Gonzalez, Ahmet Soylu, Evgeny Kharlamov
Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML).
1 code implementation • 4 May 2023 • Antonis Klironomos, Baifan Zhou, Zhipeng Tan, Zhuoxun Zheng, Gad-Elrab Mohamed, Heiko Paulheim, Evgeny Kharlamov
Many machine learning (ML) libraries are accessible online for ML practitioners.
2 code implementations • 10 Apr 2023 • Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang
Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data.
no code implementations • 21 Mar 2023 • Yunjie He, Mojtaba Nayyeri, Bo Xiong, Evgeny Kharlamov, Steffen Staab
However, the role of such patterns in answering FOL queries by query embedding models has not been yet studied in the literature.
no code implementations • 22 Sep 2022 • Dongzhuoran Zhou, Baifan Zhou, Jieying Chen, Gong Cheng, Egor V. Kostylev, Evgeny Kharlamov
One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology.
1 code implementation • 12 Mar 2022 • Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on Node Classification on MAG-scholar-C
1 code implementation • 2 Mar 2022 • Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang
We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.
no code implementations • NeurIPS 2021 • Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang
Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.
no code implementations • 29 Sep 2021 • Li Zeng, Baifan Zhou, Mohammad Al-Rifai, Evgeny Kharlamov
We propose a neural networks approach SegTime that finds precise breakpoints, obviates sliding windows, handles long-term dependencies, and it is insensitive to the label changing frequency.
no code implementations • 8 Sep 2021 • Martin Ringsquandl, Evgeny Kharlamov, Daria Stepanova, Steffen Lamparter, Raffaello Lepratti, Ian Horrocks, Peer Kröger
Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e. g., in the form of knowledge graphs.
2 code implementations • 17 Aug 2021 • Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, Jie Tang
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
1 code implementation • 17 Jun 2021 • Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang
We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.
1 code implementation • 12 Jun 2021 • Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang
In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.
1 code implementation • NeurIPS 2020 • Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
7 code implementations • 22 May 2020 • Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
1 code implementation • 1 May 2020 • Shuxin Li, Zixian Huang, Gong Cheng, Evgeny Kharlamov, Kalpa Gunaratna
A prominent application of knowledge graph (KG) is document enrichment.
1 code implementation • 1 May 2020 • Junyou Li, Gong Cheng, Qingxia Liu, Wen Zhang, Evgeny Kharlamov, Kalpa Gunaratna, Huajun Chen
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.
no code implementations • 25 Jan 2020 • Dmitriy Zheleznyakov, Evgeny Kharlamov, Werner Nutt, Diego Calvanese
Moreover, we show that well-known formula-based approaches are also not appropriate for DL-Lite expansion and contraction: they either have a high complexity of computation, or they produce logical theories that cannot be expressed in DL-Lite.
no code implementations • 14 Jan 2020 • Henrik Forssell, Evgeny Kharlamov, Evgenij Thorstensen
However, for Closed Powerset semantics we show that one can find, for any incomplete database, a unique finite set of its subinstances which are subinstances (up to renaming of nulls) of all instances semantically equivalent to the original incomplete one.
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 • 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.
no code implementations • 18 Jul 2016 • Evgeny Kharlamov, Yannis Kotidis, Theofilos Mailis, Christian Neuenstadt, Charalampos Nikolaou, Özgür Özcep, Christoforos Svingos, Dmitriy Zheleznyakov, Sebastian Brandt, Ian Horrocks, Yannis Ioannidis, Steffen Lamparter, Ralf Möller
Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens.
no code implementations • 24 Apr 2015 • Bernardo Cuenca Grau, Evgeny Kharlamov, Egor V. Kostylev, Dmitriy Zheleznyakov
We study confidentiality enforcement in ontologies under the Controlled Query Evaluation framework, where a policy specifies the sensitive information and a censor ensures that query answers that may compromise the policy are not returned.
no code implementations • 23 Apr 2013 • Diego Calvanese, Evgeny Kharlamov, Marco Montali, Ario Santoso, Dmitriy Zheleznyakov
Description Logic Knowledge and Action Bases (KABs) have been recently introduced as a mechanism that provides a semantically rich representation of the information on the domain of interest in terms of a DL KB and a set of actions to change such information over time, possibly introducing new objects.