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Knowledge graph refinement: A survey of approaches and evaluation methods
2016
Semantic Web Journal
Preserved Fulltext
In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used. ...
In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces ...
/10151490531588920
Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods
Note that since Wikipedia categories are part of the DBpedia knowledge graph, we consider this approach ...
doi:10.3233/sw-160218
fatcat:uiicnh2nbnev5j4ixum23hzila
SCEF: A Support-Confidence-aware Embedding Framework for Knowledge Graph Refinement
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1902.06377v1 fatcat:hnrqdeozlrafrm3byfkfxxtjha
Knowledge graph (KG) refinement mainly aims at KG completion and correction (i.e., error detection). ...
essential for KG refinement.In this paper, we propose a novel support-confidence-aware KG embedding framework (SCEF), which implements KG completion and correction simultaneously by learning knowledge ...
Yuhua Li for providing the MATLAB code of the BEPS method. The authors would also like to thank the anonymous referees for their valuable comments and helpful suggestions. ...
arXiv:1902.06377v2
fatcat:7do3jx66e5cppih2eecyj6osiu
Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph
[article]
2022
arXiv
pre-print
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Such knowledge can be coded in a knowledge graph -- encompassing diseases, symptoms, and diagnosis paths. ...
At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent. In this paper, we present an approach using diagnosis paths in a medical knowledge graph. ...
We introduce the medicalvalues knowledge graph in section 3, and outline our refinement approach in section 4, followed by an evaluation in section 5. ...
arXiv:2204.13329v1
fatcat:dukz6agfh5hoveyox7hejrfliu
Knowledge Graphs
2021
ACM Computing Surveys
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We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs. ...
After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. ...
ACKNOWLEDGMENTS We thank the organisers and attendees of the Dagstuhl Seminar on "Knowledge Graphs" and those who provided feedback on this article. ...
doi:10.1145/3447772
fatcat:whwtefhsfjf4djcok7c5jrgtaa
Knowledge Graph Validation
[article]
2020
arXiv
pre-print
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In this paper, we provide an overview and review of the state-of-the-art approaches, methods and tools on knowledge validation for KGs, as well as an evaluation of them. ...
Knowledge graphs (KGs) have shown to be an important asset of large companies like Google and Microsoft. ...
INTRODUCTION Knowledge curation (aka knowledge refinement) [8] is the process of ensuring (or ideally improving) the quality of knowledge graphs (KGs). ...
arXiv:2005.01389v1
fatcat:zalw5b7xxzb6vb5bsbxphvjnn4
Knowledge Graphs
[article]
2021
arXiv
pre-print
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We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of 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. ...
Acknowledgements: We thank the attendees of the Dagstuhl Seminar on "Knowledge Graphs" for discussions that inspired and influenced this paper, and all those that make such seminars possible. ...
arXiv:2003.02320v5
fatcat:ab4hmm2f2bbpvobwkjw4xbrz4u
Integration of semantic-based bipartite graph representation and mutual refinement strategy for biomedical literature clustering
2006
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06
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We introduce a novel document clustering approach that overcomes those problems by combining a semantic-based bipartite graph representation and a mutual refinement strategy. ...
The primary contributions of this paper are the following. First, we introduce a new representation of documents using a bipartite graph between documents and co-occurrence concepts in the documents. ...
ACKNOWLEDGEMENTS This research work is supported in part from the NSF Career grant (NSF IIS 0448023), NSF CCF 0514679 and the research grant from PA Dept of Health ...
doi:10.1145/1150402.1150505
dblp:conf/kdd/YooHS06
fatcat:5ya77mvlxne3tobosuooxeavdq
K-Graph: Knowledgeable Graph for Text Documents
2021
Journal of KONBiN
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An approach called Knowledgeable graphs (K-Graph) is proposed to capture semantic knowledge. Documents are stored using graph nodes. ...
The authors propose an approach that will reduce the data redundancy to a larger extent. ...
But the approaches that work on graph similarity-based methods are computationally expensive [19] . ...
doi:10.2478/jok-2021-0006
fatcat:g57tga54jvfbpa7b4midhs7nzq
Profiling Linguistic Knowledge Graphs
2022
Zenodo
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Recently the number of approaches that model and interconnect linguistic data as knowledge graphs has experienced outstanding growth. ...
Such metrics are evaluated on linguistic data and our findings provide a basis for a more efficient understanding of linguistic data. ...
There are different recent surveys that discuss some of the approaches to profile knowledge graphs such as [4] , [17] and [13] . ...
doi:10.5281/zenodo.6827644
fatcat:5huv5456gfh2rlejna2gouyaaq
Knowledge-driven Site Selection via Urban Knowledge Graph
[article]
2021
arXiv
pre-print
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To get rid of the dilemma, in this work, we borrow ideas from knowledge graph (KG), and propose a knowledge-driven model for site selection, short for KnowSite. ...
However, existing data-driven methods heavily rely on feature engineering, facing the issues of business generalization and complex relationship modeling. ...
With domain entities as nodes and semantic relations as edges, KG could integrate multisource data into a graph structure, and then powerful knowledge representation learning (KRL) methods are developed ...
arXiv:2111.00787v1
fatcat:vkymdi6ytrbhzlr4ub2ypeel5y
Unveiling Scholarly Communities over Knowledge Graphs
[chapter]
2018
Lecture Notes in Computer Science
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Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. ...
As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply Korona to uncover co-authorship networks. ...
[13] present a comprehensive survey of link prediction in social networks, while Paulheim [9] presents a survey of methodologies used for knowledge graph refinement; both works show the importance ...
doi:10.1007/978-3-030-00066-0_9
fatcat:lomxthsvkvaqjkno2rjvp6jkwi
Unveiling Scholarly Communities over Knowledge Graphs
[article]
2018
arXiv
pre-print
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Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. ...
As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply Korona to uncover co-authorship networks. ...
[13] present a comprehensive survey of link prediction in social networks, while Paulheim [9] presents a survey of methodologies used for knowledge graph refinement; both works show the importance ...
arXiv:1807.06816v1
fatcat:3yo5it5i65hmjnuey3wijnhg2u
Object Detection Meets Knowledge Graphs
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. ...
The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve better consistency with background ...
Evaluation We empirically evaluate the proposed approach on two benchmark datasets. ...
doi:10.24963/ijcai.2017/230
dblp:conf/ijcai/FangKLTC17
fatcat:5gzkuc6ccvelhm3u7253gtbkou
CSKG: The CommonSense Knowledge Graph
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.11490v1 fatcat:dmrhhykbbbdhbg3fabkun4ru7e
Sources of commonsense knowledge support applications in natural language understanding, computer vision, and knowledge graphs. Given their complementarity, their integration is desired. ...
We analyze CSKG and its various text and graph embeddings, showing that CSKG is well-connected and that its embeddings provide a useful entry point to the graph. ...
We applied this representation approach to consolidate a commonsense knowledge graph (CSKG) from seven very diverse and disjoint sources: a textbased commonsense knowledge graph ConceptNet, a general-purpose ...
arXiv:2012.11490v2
fatcat:np2mzbtdlraytpei4kguf67k5a
Few-Shot Knowledge Graph Completion
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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FSRL can effectively capture knowledge from heterogeneous graph structure, aggregate representations of few-shot references, and match similar entity pairs of reference set for every relation. ...
Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. ...
and #1849816. ...
doi:10.1609/aaai.v34i03.5698
fatcat:fxs52bl4x5gr7j7kr4iw7wew3y
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