scholarly journals Expanding a Database-derived Biomedical Knowledge Graph via Multi-relation Extraction from Biomedical Abstracts

2019 ◽  
Author(s):  
David N. Nicholson ◽  
Daniel S. Himmelstein ◽  
Casey S. Greene
Keyword(s):  
Contextual Information ◽  
Relation Extraction ◽  
Publication Rate ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Text Annotation ◽  
Label Function ◽  
Manual Curation ◽  
Function Combination ◽  
Knowledge Graphs

AbstractKnowledge graphs support multiple research efforts by providing contextual information for biomedical entities, constructing networks, and supporting the interpretation of high-throughput analyses. These databases are populated via some form of manual curation, which is difficult to scale in the context of an increasing publication rate. Data programming is a paradigm that circumvents this arduous manual process by combining databases with simple rules and heuristics written as label functions, which are programs designed to automatically annotate textual data. Unfortunately, writing a useful label function requires substantial error analysis and is a nontrivial task that takes multiple days per function. This makes populating a knowledge graph with multiple nodes and edge types practically infeasible. We sought to accelerate the label function creation process by evaluating the extent to which label functions could be re-used across multiple edge types. We used a subset of an existing knowledge graph centered on disease, compound, and gene entities to evaluate label function re-use. We determined the best label function combination by comparing a baseline database-only model with the same model but added edge-specific or edge-mismatch label functions. We confirmed that adding additional edge-specific rather than edge-mismatch label functions often improves text annotation and shows that this approach can incorporate novel edges into our source knowledge graph. We expect that continued development of this strategy has the potential to swiftly populate knowledge graphs with new discoveries, ensuring that these resources include cutting-edge results.

scholarly journals Mapping of ImageNet and Wikidata for Knowledge Graphs Enabled Computer Vision

2021 ◽  
pp. 151-161
Author(s):  
Dominik Filipiak ◽  
Anna Fensel ◽  
Agata Filipowska
Keyword(s):  
Computer Vision ◽  
Performance Metrics ◽  
Contextual Information ◽  
Ground Truth ◽  
Knowledge Graph ◽  
Graph Structure ◽  
Exact Match ◽  
Ground Truth Data ◽  
Final Performance ◽  
Knowledge Graphs

Knowledge graphs are used as a source of prior knowledge in numerous computer vision tasks. However, such an approach requires to have a mapping between ground truth data labels and the target knowledge graph. We linked the ILSVRC 2012 dataset (often simply referred to as ImageNet) labels to Wikidata entities. This enables using rich knowledge graph structure and contextual information for several computer vision tasks, traditionally benchmarked with ImageNet and its variations. For instance, in few-shot learning classification scenarios with neural networks, this mapping can be leveraged for weight initialisation, which can improve the final performance metrics value. We mapped all 1000 ImageNet labels – 461 were already directly linked with the exact match property (P2888), 467 have exact match candidates, and 72 cannot be matched directly. For these 72 labels, we discuss different problem categories stemming from the inability of finding an exact match. Semantically close non-exact match candidates are presented as well. The mapping is publicly available athttps://github.com/DominikFilipiak/imagenet-to-wikidata-mapping.

scholarly journals A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Space ◽  
Knowledge Graph ◽  
High Quality ◽  
Gaussian Distributions ◽  
Benchmark Datasets ◽  
Semantic Spaces ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.

scholarly journals ContextNet: representation and exploration for painting classification and retrieval in context

2019 ◽  
Vol 9 (1) ◽  
pp. 17-30
Author(s):  
Noa Garcia ◽  
Benjamin Renoust ◽  
Yuta Nakashima
Keyword(s):  
Contextual Information ◽  
Visual Representations ◽  
Fine Art ◽  
Multitask Learning ◽  
Knowledge Graph ◽  
Multiple Sources ◽  
Visual Elements ◽  
Knowledge Graphs ◽  
Art Analysis ◽  
Analysis Models

AbstractIn automatic art analysis, models that besides the visual elements of an artwork represent the relationships between the different artistic attributes could be very informative. Those kinds of relationships, however, usually appear in a very subtle way, being extremely difficult to detect with standard convolutional neural networks. In this work, we propose to capture contextual artistic information from fine-art paintings with a specific ContextNet network. As context can be obtained from multiple sources, we explore two modalities of ContextNets: one based on multitask learning and another one based on knowledge graphs. Once the contextual information is obtained, we use it to enhance visual representations computed with a neural network. In this way, we are able to (1) capture information about the content and the style with the visual representations and (2) encode relationships between different artistic attributes with the ContextNet. We evaluate our models on both painting classification and retrieval, and by visualising the resulting embeddings on a knowledge graph, we can confirm that our models represent specific stylistic aspects present in the data.

scholarly journals Disease ontologies for knowledge graphs

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Natalja Kurbatova ◽  
Rowan Swiers
Keyword(s):  
Data Integration ◽  
Data Sources ◽  
Knowledge Graph ◽  
Disease Area ◽  
Biomedical Knowledge ◽  
Disease Ontology ◽  
Disease Clusters ◽  
Common Task ◽  
Knowledge Graphs ◽  
Elegant Solution

Abstract Background Data integration to build a biomedical knowledge graph is a challenging task. There are multiple disease ontologies used in data sources and publications, each having its hierarchy. A common task is to map between ontologies, find disease clusters and finally build a representation of the chosen disease area. There is a shortage of published resources and tools to facilitate interactive, efficient and flexible cross-referencing and analysis of multiple disease ontologies commonly found in data sources and research. Results Our results are represented as a knowledge graph solution that uses disease ontology cross-references and facilitates switching between ontology hierarchies for data integration and other tasks. Conclusions Grakn core with pre-installed “Disease ontologies for knowledge graphs” facilitates the biomedical knowledge graph build and provides an elegant solution for the multiple disease ontologies problem.

scholarly journals Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language

2019 ◽  
Author(s):  
Charles Tapley Hoyt ◽  
Daniel Domingo-Fernández ◽  
Rana Aldisi ◽  
Lingling Xu ◽  
Kristian Kolpeja ◽  
...  
Keyword(s):  
Text Mining ◽  
Full Text ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Pubmed Central ◽  
Link Type ◽  
Information Density ◽  
Manual Curation ◽  
Rapid Accumulation ◽  
Knowledge Graphs

AbstractThe rapid accumulation of new biomedical literature not only causes curated knowledge graphs to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich knowledge graphs.We have developed two workflows: one for re-curating a given knowledge graph to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the knowledge graphs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.Database URLhttps://github.com/bel-enrichment/results

scholarly journals Knowledge Graphs Representation for Event-Related E-News Articles

2021 ◽  
Vol 3 (4) ◽  
pp. 802-818
Author(s):  
M.V.P.T. Lakshika ◽  
H.A. Caldera
Keyword(s):  
Knowledge Representation ◽  
Contextual Information ◽  
Background Information ◽  
Knowledge Graph ◽  
Learning Approaches ◽  
Text Documents ◽  
Precise Knowledge ◽  
Knowledge Graphs ◽  
News Corpus ◽  
Constructing Knowledge

E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. Thus, there is an urgent need for a technology that can automatically represent the gist of these e-news articles more quickly. Currently, popular machine learning approaches have greatly improved presentation accuracy compared to traditional methods, but they cannot be accommodated with the contextual information to acquire higher-level abstraction. Recent research efforts in knowledge representation using graph approaches are neither user-driven nor flexible to deviations in the data. Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as an efficient application in analyzing and generating knowledge representation from the extracted e-news corpus. This knowledge graph consists of a knowledge base built using triples that automatically produce knowledge representation from e-news articles. Inclusively, it has been observed that the proposed knowledge graph generates a comprehensive and precise knowledge representation for the corpus of e-news articles.

scholarly journals Task-Driven Knowledge Graph Filtering Improves Prioritizing Drugs for Repurposing

2021 ◽  
Author(s):  
Florin Ratajczak ◽  
Mitchell Joblin ◽  
Martin Ringsquandl ◽  
Marcel Hildebrandt
Keyword(s):  
Domain Knowledge ◽  
Drug Repurposing ◽  
New Drugs ◽  
Knowledge Graph ◽  
Biomedical Data ◽  
Biomedical Knowledge ◽  
Relation Type ◽  
Knowledge Graphs ◽  
Improved Performance ◽  
Efficient Learning

Abstract Background Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new connections between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. Results We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 12.4% on DRKG, with an average improvement of 17.5% on Hetionet and 5.1% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Conclusion Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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