The Experts below are selected from a list of 150969 Experts worldwide ranked by ideXlab platform
Zhiyuan Liu - One of the best experts on this subject based on the ideXlab platform.
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adapting meta knowledge Graph Information for multi hop reasoning over few shot relations
Empirical Methods in Natural Language Processing, 2019Co-Authors: Xu Han, Lei Hou, Zhiyuan LiuAbstract:Multi-hop knowledge Graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough triples for training, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms state-of-the-art methods in few-shot scenarios. In the future, our codes and datasets will also be available to provide more details.
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adapting meta knowledge Graph Information for multi hop reasoning over few shot relations
arXiv: Artificial Intelligence, 2019Co-Authors: Xu Han, Lei Hou, Zhiyuan LiuAbstract:Multi-hop knowledge Graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms the current state-of-the-art methods in few-shot scenarios. Our code and datasets can be obtained from this https URL THU-KEG/MetaKGR.
Xu Han - One of the best experts on this subject based on the ideXlab platform.
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adapting meta knowledge Graph Information for multi hop reasoning over few shot relations
Empirical Methods in Natural Language Processing, 2019Co-Authors: Xu Han, Lei Hou, Zhiyuan LiuAbstract:Multi-hop knowledge Graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough triples for training, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms state-of-the-art methods in few-shot scenarios. In the future, our codes and datasets will also be available to provide more details.
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adapting meta knowledge Graph Information for multi hop reasoning over few shot relations
arXiv: Artificial Intelligence, 2019Co-Authors: Xu Han, Lei Hou, Zhiyuan LiuAbstract:Multi-hop knowledge Graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms the current state-of-the-art methods in few-shot scenarios. Our code and datasets can be obtained from this https URL THU-KEG/MetaKGR.
Lei Hou - One of the best experts on this subject based on the ideXlab platform.
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adapting meta knowledge Graph Information for multi hop reasoning over few shot relations
Empirical Methods in Natural Language Processing, 2019Co-Authors: Xu Han, Lei Hou, Zhiyuan LiuAbstract:Multi-hop knowledge Graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough triples for training, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms state-of-the-art methods in few-shot scenarios. In the future, our codes and datasets will also be available to provide more details.
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adapting meta knowledge Graph Information for multi hop reasoning over few shot relations
arXiv: Artificial Intelligence, 2019Co-Authors: Xu Han, Lei Hou, Zhiyuan LiuAbstract:Multi-hop knowledge Graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous methods assume that every relation in KGs has enough training triples, regardless of those few-shot relations which cannot provide sufficient triples for training robust reasoning models. In fact, the performance of existing multi-hop reasoning methods drops significantly on few-shot relations. In this paper, we propose a meta-based multi-hop reasoning method (Meta-KGR), which adopts meta-learning to learn effective meta parameters from high-frequency relations that could quickly adapt to few-shot relations. We evaluate Meta-KGR on two public datasets sampled from Freebase and NELL, and the experimental results show that Meta-KGR outperforms the current state-of-the-art methods in few-shot scenarios. Our code and datasets can be obtained from this https URL THU-KEG/MetaKGR.
Amol Deshpande - One of the best experts on this subject based on the ideXlab platform.
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efficient snapshot retrieval over historical Graph data
International Conference on Data Engineering, 2013Co-Authors: Udayan Khurana, Amol DeshpandeAbstract:We present a distributed Graph database system to manage historical data for large evolving Information networks, with the goal to enable temporal and evolutionary queries and analysis. The cornerstone of our system is a novel, user-extensible, highly tunable, and distributed hierarchical index structure called DeltaGraph, that enables compact recording of the historical network Information, and that supports efficient retrieval of historical Graph snapshots for single-site or parallel processing. Our system exposes a general programmatic API to process and analyze the retrieved snapshots. Along with the original Graph data, DeltaGraph can also maintain and index auxiliary Information; this functionality can be used to extend the structure to efficiently execute queries like subGraph pattern matching over historical data. We develop analytical models for both the storage space needed and the snapshot retrieval times to aid in choosing the right construction parameters for a specific scenario. We also present an in-memory Graph data structure called GraphPool that can maintain hundreds of historical Graph instances in main memory in a non-redundant manner. We present a comprehensive experimental evaluation that illustrates the effectiveness of our proposed techniques at managing historical Graph Information.
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efficient snapshot retrieval over historical Graph data
arXiv: Databases, 2012Co-Authors: Udayan Khurana, Amol DeshpandeAbstract:We address the problem of managing historical data for large evolving Information networks like social networks or citation networks, with the goal to enable temporal and evolutionary queries and analysis. We present the design and architecture of a distributed Graph database system that stores the entire history of a network and provides support for efficient retrieval of multiple Graphs from arbitrary time points in the past, in addition to maintaining the current state for ongoing updates. Our system exposes a general programmatic API to process and analyze the retrieved snapshots. We introduce DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compactly recording the historical Information, and that supports efficient retrieval of historical Graph snapshots for single-site or parallel processing. Along with the original Graph data, DeltaGraph can also maintain and index auxiliary Information; this functionality can be used to extend the structure to efficiently execute queries like subGraph pattern matching over historical data. We develop analytical models for both the storage space needed and the snapshot retrieval times to aid in choosing the right parameters for a specific scenario. In addition, we present strategies for materializing portions of the historical Graph state in memory to further speed up the retrieval process. Secondly, we present an in-memory Graph data structure called GraphPool that can maintain hundreds of historical Graph instances in main memory in a non-redundant manner. We present a comprehensive experimental evaluation that illustrates the effectiveness of our proposed techniques at managing historical Graph Information.
Udayan Khurana - One of the best experts on this subject based on the ideXlab platform.
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efficient snapshot retrieval over historical Graph data
International Conference on Data Engineering, 2013Co-Authors: Udayan Khurana, Amol DeshpandeAbstract:We present a distributed Graph database system to manage historical data for large evolving Information networks, with the goal to enable temporal and evolutionary queries and analysis. The cornerstone of our system is a novel, user-extensible, highly tunable, and distributed hierarchical index structure called DeltaGraph, that enables compact recording of the historical network Information, and that supports efficient retrieval of historical Graph snapshots for single-site or parallel processing. Our system exposes a general programmatic API to process and analyze the retrieved snapshots. Along with the original Graph data, DeltaGraph can also maintain and index auxiliary Information; this functionality can be used to extend the structure to efficiently execute queries like subGraph pattern matching over historical data. We develop analytical models for both the storage space needed and the snapshot retrieval times to aid in choosing the right construction parameters for a specific scenario. We also present an in-memory Graph data structure called GraphPool that can maintain hundreds of historical Graph instances in main memory in a non-redundant manner. We present a comprehensive experimental evaluation that illustrates the effectiveness of our proposed techniques at managing historical Graph Information.
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efficient snapshot retrieval over historical Graph data
arXiv: Databases, 2012Co-Authors: Udayan Khurana, Amol DeshpandeAbstract:We address the problem of managing historical data for large evolving Information networks like social networks or citation networks, with the goal to enable temporal and evolutionary queries and analysis. We present the design and architecture of a distributed Graph database system that stores the entire history of a network and provides support for efficient retrieval of multiple Graphs from arbitrary time points in the past, in addition to maintaining the current state for ongoing updates. Our system exposes a general programmatic API to process and analyze the retrieved snapshots. We introduce DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compactly recording the historical Information, and that supports efficient retrieval of historical Graph snapshots for single-site or parallel processing. Along with the original Graph data, DeltaGraph can also maintain and index auxiliary Information; this functionality can be used to extend the structure to efficiently execute queries like subGraph pattern matching over historical data. We develop analytical models for both the storage space needed and the snapshot retrieval times to aid in choosing the right parameters for a specific scenario. In addition, we present strategies for materializing portions of the historical Graph state in memory to further speed up the retrieval process. Secondly, we present an in-memory Graph data structure called GraphPool that can maintain hundreds of historical Graph instances in main memory in a non-redundant manner. We present a comprehensive experimental evaluation that illustrates the effectiveness of our proposed techniques at managing historical Graph Information.
