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Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
[article]
2020
arXiv
pre-print
Preserved Fulltext
A recent family of models called knowledge graph embeddings have shown promising results on general domain knowledge graphs, and we explore their capabilities in the biomedical domain. ...
We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph, provide a benchmark with comparison to existing methods and in-depth discussion on best practices, and ...
their advantages over previous methods, making a case for the importance of leveraging the multirelational nature of knowledge graphs for biomedical knowledge representation. • We establish a suite of ...
arXiv:2006.13774v1
fatcat:5ogiuwacnjfcpfctrurkaiiale
Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
2020
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
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A recent family of models called knowledge graph embeddings have shown promising results on general domain knowledge graphs, and we explore their capabilities in the biomedical domain. ...
We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph, provide a benchmark with comparison to existing methods and in-depth discussion on best practices, and ...
their advantages over previous methods, making a case for the importance of leveraging the multirelational nature of knowledge graphs for biomedical knowledge representation. • We establish a suite of ...
doi:10.18653/v1/2020.bionlp-1.18
pmid:33746351
pmcid:PMC7971091
fatcat:2qi6rl7rwjb7rlht34eqieau6e
Matching Entities Across Different Knowledge Graphs with Graph Embeddings
[article]
2019
arXiv
pre-print
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Using a classification-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2Vec graph embeddings of entities in each knowledge graph is sufficient to ...
We formalize this problem and present two large-scale datasets for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. ...
Acknowledgments This research was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada. ...
arXiv:1903.06607v1
fatcat:la4z3lqvwndy7i3h6mbuozpvqy
Convolutional 2D Knowledge Graph Embeddings
2017
Zenodo
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To scale to large knowledge graphs and prevent overfitting due to over-parametrization, previous work seeks to reduce parameters by performing simple transformations in embedding space. ...
We report state-of-the-art results for numerous previously introduced link prediction benchmarks, including the well-established FB15k and WN18 datasets. ...
In this work we propose a neural link predictor, ConvE, that uses 2D convolution over embeddings to predict new links in knowledge graphs. ...
doi:10.5281/zenodo.833288
fatcat:lctoheg4zra4pmfiixkuzrvxpa
DSSLP: A Distributed Framework for Semi-supervised Link Prediction
[article]
2020
arXiv
pre-print
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Experimental results demonstrate that the effectiveness and efficiency of DSSLP in serval public datasets as well as real-world datasets of industrial-scale graphs. ...
However, it's a great challenge to train and deploy a link prediction model on industrial-scale graphs with billions of nodes and edges. ...
As graph data exists ubiquitously, the link prediction problem has many applications, such as recommendation, fraud detection, knowledge graph completion, etc. ...
arXiv:2002.12056v2
fatcat:gw7ghj5ycjhn7ahscjuncb6bxq
Knowledge Embedding Based Graph Convolutional Network
[article]
2021
arXiv
pre-print
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Experimental results on benchmark datasets show the advantageous performance of KE-GCN over strong baseline methods in the tasks of knowledge graph alignment and entity classification. ...
propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. ...
This work is supported in part by the National Science Foundation (NSF) under grant IIS-1546329, and by the United States Department of Energy via the Brookhaven National Laboratory under Contract No. ...
arXiv:2006.07331v2
fatcat:lbbure7zsvhljdecmz6fssq6wi
Reasoning over RDF Knowledge Bases using Deep Learning
[article]
2018
arXiv
pre-print
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In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall ...
Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. ...
The Linked Data Cloud 1 website lists over 1,200 interlinked RDF(S) datasets, which constitute knowledge graphs suitable for our setting, some of which are of substantial size. ...
arXiv:1811.04132v1
fatcat:e42txhb2qvdsvj4bzuegxaxzvu
Inter-domain Multi-relational Link Prediction
[article]
2021
arXiv
pre-print
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Similar to other graph-structured data, link prediction is one of the most important tasks on multi-relational graphs and is often used for knowledge completion. ...
However, this poses a real challenge to existing methods that are exclusively designed for link prediction between entities of the same graph only (intra-domain link prediction). ...
of the art in various graph matching datasets. ...
arXiv:2106.06171v3
fatcat:mbpypirxwbhm5mokbluwquzpzm
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
[article]
2022
arXiv
pre-print
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In this paper, we propose kNN-KGE, a new knowledge graph embedding approach, by linearly interpolating its entity distribution with k-nearest neighbors. ...
Previous knowledge graph embedding approaches usually map entities to representations and utilize score functions to predict the target entities, yet they struggle to reason rare or emerging unseen entities ...
To the best of our knowledge, we are the first kNN-based approach for knowledge graph embedding. ...
arXiv:2201.05575v1
fatcat:umhtd3wp75h2doegoe7q3yd3ve
PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
[article]
2020
arXiv
pre-print
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We demonstrate this in our evaluation over three datasets on the Wikidata Knowledge Graph. ...
Question Answering systems are generally modelled as a pipeline consisting of a sequence of steps. In such a pipeline, Entity Linking (EL) is often the first step. ...
Chris Biemann of the Language Technology Group, University of Hamburg, for his valuable suggestions towards improving this work. ...
arXiv:2009.00106v1
fatcat:ixskwok4rrcdbi2m3lngurzahi
Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.09590v1 fatcat:k6p3riupefgxpjvj53j7rjomca
ECOLA jointly optimizes the knowledge-text prediction objective and the temporal knowledge embeddings, which can simultaneously take full advantage of textual and knowledge information. ...
Experimental results on the temporal knowledge graph completion task show that ECOLA outperforms state-of-the-art temporal KG models by a large margin. ...
Implementation We use the datasets augmented with reciprocal relations to train all baseline models. We tune hyperparameters of our models using the random search and report the best configuration. ...
arXiv:2203.09590v2
fatcat:hb7nt2l5s5ejbjhgo5alcpz364
A Toolkit for Generating Code Knowledge Graphs
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2002.09440v2 fatcat:ax423dbv4bfvpdxgpmjje6737uOther Versions
This results in an integrated code graph with over 2 billion triples. ...
Knowledge graphs have been proven extremely useful in powering diverse applications in semantic search and natural language understanding. ...
To our knowledge, Graph4Code is the first attempt to build a knowledge graph over a large repository of programs and systematically link it to documentation and forum posts related to code. ...
arXiv:2002.09440v3
fatcat:6m2xxc3xvrcvrhlykzqsr5rzqi
On event-driven knowledge graph completion in digital factories
2017
2017 IEEE International Conference on Big Data (Big Data)
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2021 pre-print arXiv:2109.03655v1 fatcat:cpnabuppi5h4hlhob4hzmeanim
., in the form of knowledge graphs. ...
Creation and maintenance of such knowledge is expensive and requires automation. ...
., at a mid size plant this knowledge may contain information about up to hundreds of machines, processes and materials, and hundreds of thousands of events. ...
doi:10.1109/bigdata.2017.8258105
dblp:conf/bigdataconf/RingsquandlKSLL17
fatcat:zsujquk2gve7fc4ut3463kqdbu
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
[article]
2017
arXiv
pre-print
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Reasoning over time in such dynamic knowledge graphs is not yet well understood. ...
The availability of large scale event data with time stamps has given rise to dynamically evolving knowledge graphs that contain temporal information for each edge. ...
to enhancement of knowledge graph. ...
arXiv:1705.05742v3
fatcat:ipr7szsdkfc5bpbttttogllkpy
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
[article]
2020
arXiv
pre-print
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We define relation-centric connectivity measures that allow us to connect the link prediction capacity to the structure of the knowledge graph. ...
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion. ...
As such, the overall performance of specialized or generalized embeddings on one knowledge graph is characterized by these three distributions over all relations in the given knowledge graph. ...
arXiv:2005.07654v2
fatcat:iy273mqrofcn7fp3gtpahba65e
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