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Extracting Novel Facts from Tables for Knowledge Graph Completion (Extended version) [article]

Benno Kruit and Peter Boncz and Jacopo Urbani
2019 arXiv   pre-print
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables.  ...  Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts.  ...  Consequently, it extracts more novel facts for KG completion.  ... 
arXiv:1907.00083v2 fatcat:svq5v2jp4jbljegm3qdouijlce

Extracting Novel Facts from Tables for Knowledge Graph Completion [chapter]

Benno Kruit, Peter Boncz, Jacopo Urbani
2019 Lecture Notes in Computer Science  
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables.  ...  Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts.  ...  Consequently, it extracts more novel facts for KG completion.  ... 
doi:10.1007/978-3-030-30793-6_21 fatcat:ocfjoeu6nzfrnplfy7ibddhuv4

Joint Representation Learning of Text and Knowledge for Knowledge Graph Completion [article]

Xu Han, Zhiyuan Liu, Maosong Sun
2016 arXiv   pre-print
Joint representation learning of text and knowledge within a unified semantic space enables us to perform knowledge graph completion more accurately.  ...  In this model, both entity and relation embeddings are learned by taking knowledge graph and plain text into consideration.  ...  Typical large-scale knowledge graphs are usually far from complete. The task of knowledge graph completion aims to enrich KGs with novel facts.  ... 
arXiv:1611.04125v1 fatcat:eb2tchisbbbqvjvw3dl2jx7mh4

Collaborative Policy Learning for Open Knowledge Graph Reasoning

Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
Here we study open knowledge graph reasoning-a task that aims to reason for missing facts over a graph augmented by a background text corpus.  ...  The fact extraction agent generates fact triples from corpora to enrich the graph on the fly; while the reasoning agent provides feedback to the fact extractor and guides it towards promoting facts that  ...  We would like to thank all the collaborators for their constructive feedbacks.  ... 
doi:10.18653/v1/d19-1269 dblp:conf/emnlp/FuCQJR19 fatcat:4z4qoeow7nbudalhn7zlsqp2fy

Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning [article]

Fubang Zhao, Zhuoren Jiang, Yangyang Kang, Changlong Sun, Xiaozhong Liu
2021 arXiv   pre-print
Relational fact extraction aims to extract semantic triplets from unstructured text.  ...  In this work, we show that all of the relational fact extraction models can be organized according to a graph-oriented analytical perspective.  ...  Acknowledgments We are thankful to the anonymous reviewers for their helpful comments.  ... 
arXiv:2106.01559v1 fatcat:4gxdwewjynex7ngcr473doxqpi

Towards Monitoring of Novel Statements in the News [chapter]

Michael Färber, Achim Rettinger, Andreas Harth
2016 Lecture Notes in Computer Science  
facts.  ...  In this work, we propose a new semantic novelty measure that allows to retrieve statements, which are both novel and relevant, from natural-language sentences in news articles.  ...  for completeness.  ... 
doi:10.1007/978-3-319-34129-3_18 fatcat:2sat4rcikba7fp76e3oxykq7xi

Biomedical Information Extraction for Disease Gene Prioritization [article]

Jupinder Parmar, William Koehler, Martin Bringmann, Katharina Sophia Volz, Berk Kapicioglu
2020 arXiv   pre-print
We apply it to tens of millions of PubMed abstracts to extract protein-protein interactions (PPIs) and augment these extractions to a biomedical knowledge graph that already contains PPIs extracted from  ...  We show that, despite already containing PPIs from an established structured source, augmenting our own IE-based extractions to the graph allows us to predict novel disease-gene associations with a 20%  ...  Thus, there is a growing need for scalable methods that can both extract relevant knowledge from unstructured text and synthesize it to infer novel biomedical discoveries.  ... 
arXiv:2011.05188v2 fatcat:vcqujs264zcyrbbrlcqbw5ynxy

Domain Specific Facts Extraction Using Weakly Supervised Active Learning Approach

Vinay Pande, Tanmoy Mukherjee, Vasudeva Varma
2013 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)  
Generic Knowledge Bases like YAGO, Freebase, and DBPedia have made accessible huge collections of facts and their properties from various domains.  ...  In this paper, we focus on second problem: relation extraction from plain text.  ...  Results are as shown in Table 2 . From Table 2 , it is clear that our approach can extract sufficient number of facts even for smaller training data.  ... 
doi:10.1109/wi-iat.2013.36 dblp:conf/webi/PandeMV13 fatcat:kmz4asfdr5hn3lalrmvyd7j73u

Collaborative Policy Learning for Open Knowledge Graph Reasoning [article]

Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren
2019 arXiv   pre-print
Here we study open knowledge graph reasoning---a task that aims to reason for missing facts over a graph augmented by a background text corpus.  ...  The fact extraction agent generates fact triples from corpora to enrich the graph on the fly; while the reasoning agent provides feedback to the fact extractor and guides it towards promoting facts that  ...  We would like to thank all the collaborators for their constructive feedbacks.  ... 
arXiv:1909.00230v1 fatcat:c72pngv3gzf35c2zk5rbwammye

TransOMCS: From Linguistic Graphs to Commonsense Knowledge [article]

Hongming Zhang, Daniel Khashabi, Yangqiu Song, Dan Roth
2020 arXiv   pre-print
In this paper, we explore a practical way of mining commonsense knowledge from linguistic graphs, with the goal of transferring cheap knowledge obtained with linguistic patterns into expensive commonsense  ...  Commonsense knowledge acquisition is a key problem for artificial intelligence.  ...  support from DARPA grant FA8750-19-2-1004.  ... 
arXiv:2005.00206v1 fatcat:zgbyczzkprhtxoanvihdil2wk4

Learning Knowledge Graphs for Question Answering through Conversational Dialog

Ben Hixon, Peter Clark, Hannaneh Hajishirzi
2015 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
Our relation-based strategies complete more successful dialogs than a query expansion baseline, our taskdriven relations are more effective for solving science questions than relations from general knowledge  ...  Our system learns to relate concepts in science questions to propositions in a fact corpus, stores new concepts and relations in a knowledge graph (KG), and uses the graph to solve questions.  ...  The third author was supported by grants from the Allen Institute for AI (66-9175) and the NSF (IIS-1352249).  ... 
doi:10.3115/v1/n15-1086 dblp:conf/naacl/HixonCH15 fatcat:dboslo2gj5fn5ofq5bsdirhbkq

Entity Summarisation with Limited Edge Budget on Undirected and Directed Knowledge Graphs

Marcin Sydow, Mariusz Pikuła, Ralf Schenkel
2010 Investigationes Linguisticae  
Experimental user evaluation of the algorithm was conducted on real large semantic knowledge graphs extracted from the web.  ...  The paper concerns a novel problem of summarising entities with limited presentation budget on entity-relationship knowledge graphs and propose an efficient algorithm for solving this problem.  ...  An excerpt from a semantic knowledge graph extracted from the "Library Thing" database, concerning the books domain.  ... 
doi:10.14746/il.2010.21.5 fatcat:6yasdibtsjfrlcl3flw4cwsl5u

Sar-graphs: A language resource connecting linguistic knowledge with semantic relations from knowledge graphs

Sebastian Krause, Leonhard Hennig, Andrea Moro, Dirk Weissenborn, Feiyu Xu, Hans Uszkoreit, Roberto Navigli
2016 Journal of Web Semantics  
We believe sar-graphs will prove to be useful linguistic resources for a wide variety of natural language processing tasks, and in particular for information extraction and knowledge base population.  ...  Using these seeds, our method extracts, validates and merges relationspecific linguistic patterns from text to create sar-graphs.  ...  Correct Predicted Predicted Novel Facts 1171 5552 ⇡ 1314 Table 8 : Estimated number of novel "facts" found by sargraph-based relation extraction on a ClueWeb dataset sample.  ... 
doi:10.1016/j.websem.2016.03.004 fatcat:o33kiij265hhrgyggkeq4w3ycu

Sar-Graphs: A Language Resource Connecting Linguistic Knowledge with Semantic Relations from Knowledge Graphs

Sebastian Krause, Leonhard Hennig, Andrea Moro, Dirk Weissenborn, Feiyu Xu, Hans Uszkoreit, Roberto Navigli
2016 Social Science Research Network  
We believe sar-graphs will prove to be useful linguistic resources for a wide variety of natural language processing tasks, and in particular for information extraction and knowledge base population.  ...  Using these seeds, our method extracts, validates and merges relationspecific linguistic patterns from text to create sar-graphs.  ...  Correct Predicted Predicted Novel Facts 1171 5552 ⇡ 1314 Table 8 : Estimated number of novel "facts" found by sargraph-based relation extraction on a ClueWeb dataset sample.  ... 
doi:10.2139/ssrn.3199232 fatcat:qds3iq45tzfbxp5rzoauuvqgg4

Ten years of webtables

Michael Cafarella, Alon Halevy, Hongrae Lee, Jayant Madhavan, Cong Yu, Daisy Zhe Wang, Eugene Wu
2018 Proceedings of the VLDB Endowment  
In 2008, we wrote about WebTables, an effort to exploit the large and diverse set of structured databases casually published online in the form of HTML tables.  ...  We will also show how the progress over the past ten years sets up an exciting agenda for the future, and will draw upon many corners of the data management community.  ...  [6] initially implemented EXTEND() with a combination of schema matching and search engine results to find candidate completion values from extracted data tables.  ... 
doi:10.14778/3229863.3240492 fatcat:qkwoc7m27zfpdgrlbwtweaccim
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