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Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval

Zhenghao Liu, Chenyan Xiong, Maosong Sun, Zhiyuan Liu
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks.  ...  This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems.  ...  We thank Sogou for providing access to the search log.  ... 
doi:10.18653/v1/p18-1223 dblp:conf/acl/SunLXL18 fatcat:uoal5wyyzvhylgahyrnisfltpi

Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval [article]

Zhenghao Liu, Chenyan Xiong, Maosong Sun, Zhiyuan Liu
2018 arXiv   pre-print
The semantics from knowledge graphs are integrated in the distributed representations of their entities, while the ranking is conducted by interaction-based neural ranking networks.  ...  The two components are learned end-to-end, making EDRM a natural combination of entity-oriented search and neural information retrieval.  ...  We thank Sogou for providing access to the search log.  ... 
arXiv:1805.07591v2 fatcat:ns3hh65qhbbmvfmku6nm4ky6d4

Reliable Knowledge Graph Path Representation Learning

Seungmin Seo, Byungkook Oh, Kyong-Ho Lee
2020 IEEE Access  
A promising approach for this task is a knowledge graph representation learning, which aims to represent entities and relations into low-dimensional vector spaces.  ...  Specifically, we combine the representations of intermediate entities and relations on relation paths to learn more meaningful knowledge representations.  ...  TKRL [38] takes hierarchical entity types into considerations and makes projection matrices for entities with type encoders.  ... 
doi:10.1109/access.2020.2973923 fatcat:ux3lx3quwbcithpzwqtendqssu

Learning to Rank Target Types for Entity-Bearing Queries

Darío Garigliotti, Krisztian Balog
2017 International Conference on the Theory of Information Retrieval  
is paper revisits the learning-to-rank approach we proposed for automatically identifying the target entity types of queries [6] .  ...  Alternatively, a type-centric model presented in [2] ranks direct term-based representations (pseudo type description documents), built for each type, by aggregating descriptions of entities  ...  Our experiments were performed using Nordlys [7] , a toolkit for entity-oriented and semantic search. We employed the Random Forest algorithm for regression as the supervised ranking method.  ... 
dblp:conf/ictir/GarigliottiB17a fatcat:xsvr6nq4a5b63pjifmwepfgneq

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema [article]

Patrick Verga, Arvind Neelakantan, Andrew McCallum
2017 arXiv   pre-print
In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding).  ...  Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column.  ...  Arvind Neelakantan is supported by a Google PhD fellowship in machine learning.  ... 
arXiv:1606.05804v2 fatcat:xcvaurhnsnau5jihsy262wseqi

Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

Patrick Verga, Arvind Neelakantan, Andrew McCallum
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers  
In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding).  ...  Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column.  ...  Arvind Neelakantan is supported by a Google PhD fellowship in machine learning.  ... 
doi:10.18653/v1/e17-1058 dblp:conf/eacl/McCallumNV17 fatcat:3wbt6jj5ljbhbld6vr2gq67gha

Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types

Yanying Mao, Honghui Chen
2021 Mathematics  
information, and logic rules information for representation learning.  ...  Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation.  ...  entities have different type representations.  ... 
doi:10.3390/math9161978 fatcat:lhisjpmn6jcxbe7gxqlx3upjj4

Explore Entity Embedding Effectiveness in Entity Retrieval [article]

Zhenghao Liu, Chenyan Xiong, Maosong Sun, Zhiyuan Liu
2019 arXiv   pre-print
Our experiments demonstrate the effectiveness of entity embedding based model, which achieves more than 5\% improvement than the previous state-of-the-art learning to rank based entity retrieval model.  ...  query related entities and candidate entities for entity retrieval.  ...  The learning to rank models is utilized to combine all exact match features and entity soft match feature for the ranking score.  ... 
arXiv:1908.10554v1 fatcat:zepi5ixl3bfgfnqz5zriodjhtq

A Trio Neural Model for Dynamic Entity Relatedness Ranking

Tu Nguyen, Tuan Tran, Wolfgang Nejdl
2018 Proceedings of the 22nd Conference on Computational Natural Language Learning  
Our model is capable of learning rich and different entity representations in a joint framework.  ...  Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications.  ...  We thank the reviewers for the suggestions on the content and structure of the paper.  ... 
doi:10.18653/v1/k18-1004 dblp:conf/conll/NguyenTN18 fatcat:liasz2zhpnbnjmqh5dculu4lfa

Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs [article]

Daniel Oñoro-Rubio, Mathias Niepert, Alberto García-Durán, Roberto González, Roberto J. López-Sastre
2019 arXiv   pre-print
We explore novel machine learning approaches for answering visual-relational queries in web-extracted knowledge graphs.  ...  The resulting multi-relational grounding of unseen entity images into a knowledge graph serves as a semantic entity representation.  ...  We proposed several novel query types and introduce neural architectures suitable for probabilistic query answering.  ... 
arXiv:1709.02314v6 fatcat:txfttbx3prch7iflks3wvmkr74

Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference [article]

Guanglin Niu, Bo Li, Yongfei Zhang, Yongpan Sheng, Chuan Shi, Jingyang Li, Shiliang Pu
2020 arXiv   pre-print
The entity converter is designed to transform the entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations,  ...  in which the KGs both with and without type information are considered.  ...  KG and learn the distributed representations for entities and relations.  ... 
arXiv:2010.02602v1 fatcat:7n2rrv3xared5gd62vipfegota

Row-less Universal Schema [article]

Patrick Verga, Andrew McCallum
2016 arXiv   pre-print
Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types.  ...  types.  ...  This work was supported in part by the Center for Intelligent Information Retrieval and the Center for Data Science, in part by The Allen Institute for Artificial Intelligence, and in part by DARPA under  ... 
arXiv:1604.06361v1 fatcat:vaepmlgbkzc7hlchvfwloddxpm

Row-less Universal Schema

Patrick Verga, Andrew McCallum
2016 Proceedings of the 5th Workshop on Automated Knowledge Base Construction  
Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types.  ...  types.  ...  Acknowledgments We thank Emma Strubell, David Belanger, Luke Vilnis, and Arvind Neelakantan for helpful discussions and edits.  ... 
doi:10.18653/v1/w16-1312 dblp:conf/akbc/VergaM16 fatcat:v5qt5blwkrguhnvxvrprfc6vga

A Trio Neural Model for Dynamic Entity Relatedness Ranking [article]

Tu Ngoc Nguyen, Tuan Tran, Wolfgang Nejdl
2018 arXiv   pre-print
Our model is capable of learning rich and different entity representations in a joint framework.  ...  Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications.  ...  We thank the reviewers for the suggestions on the content and structure of the paper.  ... 
arXiv:1808.08316v3 fatcat:byi3hqroqbbgxmzd2pwha7sdve

Dynamic Collective Entity Representations for Entity Ranking

David Graus, Manos Tsagkias, Wouter Weerkamp, Edgar Meij, Maarten de Rijke
2016 Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - WSDM '16  
Dynamic collective entity representations for entity ranking Graus, D.P.; Tsagkias, E.; Weerkamp, W.; Meij, E.J.; de Rijke, M.  ...  Periodic retraining of the ranker enables higher ranking effectiveness for dynamic collective entity representations.  ...  Machine learning We apply machine learning for learning how to weight the different fields that make up an entity representation for optimal retrieval effectiveness.  ... 
doi:10.1145/2835776.2835819 dblp:conf/wsdm/GrausTWMR16 fatcat:ag72bi6djfembnfuzb2wcpldri
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