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After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.", "language" : "English", "eprint" : "2003.02320", "bibsource" : "dblp computer science bibliography, https://dblp.org", "archiveprefix" : "arXiv", "bibtexKey": "journals/corr/abs-2003-02320" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f7dbf0a622ff3e694c009ec93060c47f/lschmelzeisen", "tags" : [ "@AnalyticComp","AnalyticComputing","dblp","myown" ], "intraHash" : "f7dbf0a622ff3e694c009ec93060c47f", "interHash" : "61c25eacd5f7a0ba141e22543fe91a8e", "label" : "Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper.", "user" : "lschmelzeisen", "description" : "", "date" : "2020-11-26 10:09:22", "changeDate" : "2020-11-26 09:23:09", "count" : 3, "pub-type": "article", "journal": "CoRR", "year": "2019", "url": "https://arxiv.org/abs/1902.02169", "author": [ "Lukas Schmelzeisen","Steffen Staab" ], "authors": [ {"first" : "Lukas", "last" : "Schmelzeisen"}, {"first" : "Steffen", "last" : "Staab"} ], "volume": "abs/1902.02169","abstract": "Taxonomies are semantic hierarchies of concepts. One limitation of current taxonomy learning systems is that they define concepts as single words. This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method to address this limitation. We outline a novel approach for taxonomy learning that (1) defines concepts as synsets, (2) learns density-based approximations of contextualized word representations, and (3) can measure similarity and hypernymy among them.", "ee" : "http://arxiv.org/abs/1902.02169", "language" : "English", "bibtexKey": "journals/corr/abs-1902-02169" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2b05149be0c2e6b80ac4e4d2ab3dfd193/lschmelzeisen", "tags" : [ "@AnalyticComp","AnalyticComputing","dblp","myown" ], "intraHash" : "b05149be0c2e6b80ac4e4d2ab3dfd193", "interHash" : "8ecf60543c716db98189eb98c772be4f", "label" : "CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors.", "user" : "lschmelzeisen", "description" : "", "date" : "2020-11-26 10:05:00", "changeDate" : "2020-11-26 09:24:09", "count" : 6, "pub-type": "article", "journal": "CoRR", "year": "2019", "url": "https://arxiv.org/abs/1904.03084", "author": [ "Ipek Baris","Lukas Schmelzeisen","Steffen Staab" ], "authors": [ {"first" : "Ipek", "last" : "Baris"}, {"first" : "Lukas", "last" : "Schmelzeisen"}, {"first" : "Steffen", "last" : "Staab"} ], "volume": "abs/1904.03084","abstract": "This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6%. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). We provide results and analysis of our system performance and present ablation experiments.", "ee" : "http://arxiv.org/abs/1904.03084", "language" : "English", "bibtexKey": "journals/corr/abs-1904-03084" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/242f0d00808b61a6cc0a7d69aa701b464/lschmelzeisen", "tags" : [ "@AnalyticComp","AnalyticComputing","dblp","myown" ], "intraHash" : "42f0d00808b61a6cc0a7d69aa701b464", "interHash" : "8ecf60543c716db98189eb98c772be4f", "label" : "CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors.", "user" : "lschmelzeisen", "description" : "", "date" : "2020-11-26 10:00:37", "changeDate" : "2020-11-26 09:24:59", "count" : 6, "pub-type": "inproceedings", "booktitle": "SemEval@NAACL-HLT","publisher":"Association for Computational Linguistics", "year": "2019", "url": "https://www.aclweb.org/anthology/S19-2193/", "author": [ "Ipek Baris","Lukas Schmelzeisen","Steffen Staab" ], "authors": [ {"first" : "Ipek", "last" : "Baris"}, {"first" : "Lukas", "last" : "Schmelzeisen"}, {"first" : "Steffen", "last" : "Staab"} ], "editor": [ "Jonathan May","Ekaterina Shutova","Aurélie Herbelot","Xiaodan Zhu","Marianna Apidianaki","Saif M. 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For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). 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