COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature

07/24/2020 ∙ by Colby Wise, et al. ∙ 34

The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 6 million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID-19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations inherent to scientific literature. Moreover, because these engines mainly rely upon semantic information, their ability to capture complex global relationships across documents is limited, which reduces the quality of similarity-based article recommendations for users. In this work, we present the COVID-19 Knowledge Graph (CKG), a heterogeneous graph for extracting and visualizing complex relationships between COVID-19 scientific articles. The CKG combines semantic information with document topological information for the application of similar document retrieval. The CKG is constructed using the latent schema of the data, and then enriched with biomedical entity information extracted from the unstructured text of articles using scalable AWS technologies to form relations in the graph. Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval. Analysis demonstrates the quality of relationships in the CKG and shows that it can be used to uncover meaningful information in COVID-19 scientific articles. The CKG helps power and is publicly available.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1. Introduction

The onset of the novel SARS-CoV-2 virus has emphasized the need to accumulate insights from large volumes of information. Thousands of new scientific articles on the virus are being published weekly, leading to a rapid increase in the cumulative knowledge about the coronavirus disease (COVID-19). COVID-19 has heightened the need for tools that enable researchers to search vast scientific corpora to find specific information, visualize connections across the data, and discover related information in the data.

Several COVID-19 dedicated search engines have come online to address the need for information retrieval of scientific literature on the disease. Search engines like Sketch Engine COVID-19, Sinequa COVID-19 Intelligent Search, Microsoft’s CORD19 Search, and Amazon’s CORD19 Search use a variety of methodologies such as keyword search, natural language queries, semantic relevancy, and knowledge graphs. However, these engines return thousands of search results that overlook inherent relationships between scientific articles, such as subject topic and citations, and do not provide tools to visualize relationships, which is beneficial for knowledge discovery. In this paper, we construct the COVID-19 knowledge Graph (CKG) by extracting rich features and relationships of COVID-19 related scientific articles and develop a document similarity engine that combines both semantic and relationship information from CKG.

Knowledge graphs (KGs) are structural representations of relations between real-world entities where relations are defined as triplets containing a head entity, a tail entity, and the relation type connecting them. KG based information retrieval has shown great success in the past decades (Kim and Kim, 1990; Dalton et al., 2014). We construct the CKG using the CORD19 Open Research Dataset of scholarly articles (Wang et al., 2020). Scientific articles, publication authors, author institutional affiliations, and citations form key relationships in the graph. Further, we extract biomedical entity relationships and highly abstracted topics from the unstructured text of articles using Amazon Comprehend Medical service and train a topic model on the corpus. By applying data normalization technologies we eliminate duplicate entities and noisy linkages. The resulting KG contains 336,887 entities and 3,332,151 relations. The CKG has been made publicly available to researchers with rapid “one-click” cloud deployment templates.111 We introduce a document similarity engine that leverages both the semantic information of articles and the topological information from the CKG to accelerate COVID-19 related information retrieval and discovery. We employ SciBERT (Beltagy et al., 2019), a pretrained NLP model, to generate semantic embeddings for each article. Meanwhile, we utilize knowledge graph embedding (KGE) (Wang et al., 2017; Zheng et al., 2020) and graph neural network (Schlichtkrull et al., 2018) technologies to generate embeddings for entities and relations of the CKG. Finally, by combining judiciously the semantic embeddings and graph embeddings we use the similarity engine to propose top-k similar articles. The CKG and similarity engine are new additions to

, a website using machine learning to help researchers search thousands of COVID-19 related scientific articles using natural language question queries that has seen over 15 million queries across more than 70 countries. The CKG adds a graph-traversal ranking feature to search and the similarity engine powers the similarity-based recommended article system. To further demonstrate the quality of the CKG, we conduct a series of experiments analyzing the relations that form the core pillars of the graph. We first evaluate the ability of our methodology to capture the topic information in the text, and show that extracted topics align well with the subjects of scientific journals. We also perform link prediction analysis by extracting graph embeddings that validates the quality of the relations in the graph and demonstrates that we capture important topological information from the CKG. Our analysis shows that the semantic embeddings and graph embeddings learn useful information and improve our ability to quantify similarity between articles. Lastly, several motivating examples show that querying the CKG can extract actionable insights from scientific articles. To summarize, our contribution is fourfold:

  • We construct a scientific KG, named COVID-19 Knowledge Graph (CKG), by judiciously combining the inherent schema information from COVID-19 scientific articles as well as the extracted biomedical entity relationships and topic information.

  • We conduct several data normalization methodologies to curate the CKG and demonstrate its information retrieval, visualization and discovery capabilities. The CKG is publicly available through

  • We present a novel similarity-based document retrieval system that combines semantic article information with document topological information learned from the CKG and show that it reliably improves the quality of user-suggested documents.

  • The similarity engine and the CKG have been integrated into a public available search service for COVID-19 through to power the similarity-based article recommendation system and to provide a graph-traversal ranking feature.

2. CKG Construction & Curation

CKG is a directed property graph where entities and relations have associated attributes (properties) and direction (directed). Figure 1 illustrates the directed property graph structure for a small subgraph of CKG. In this section we describe the dataset used to construct the CKG, define the entity and relation types, detail CKG curation methods, provide summary statistics describing the graph, and detail the cloud infrastructure that drives CKG scalability.

Figure 1. Visualization of CKG. Paper entities (blue) connect to Concepts (red), topics (light blue), and authors (gold) through directed relations. Authors connect to institutions (green).

2.1. The CORD-19 Dataset

COVID-19 Open Research Dataset (CORD-19) is a dynamic, growing repository of scientific full text articles on COVID-19 and related coronaviruses created by the Allen Institute for AI (AI2) (Wang et al., 2020). The data is made available via Kaggle with weekly updates as part of the on-going CORD-19 Research Competition (7).

As of 06-01-2020, the CORD-19 dataset consists of over 60,000 full text. Rich metadata is provided as part of the dataset e.g. article authors. The data is sourced from several channels such as PubMed, bioArxiv, and medRxiv. The dataset is multidisciplinary with articles covering virology, translational medicine, epidemiology, computer science and more. CORD-19 grows constantly and AI2 is working with the wider research community to normalize and improve the quality of the data.

2.2. Entity Types

The CKG contains five types of entities corresponding to papers, authors, institutions, concepts, and topics as summarized in Table 1. Information on what these entities represent, their attributes, and how they are created follows.

Paper Entities.

Representation of scientific articles. Attributes include title, publication date, journal, and Digital Object Identifier (DOI) link as available in the CORD-19 Dataset from AI2.

Entity Type Count Relation Type Count
Papers 42,220 authored_by 240,624
Authors 162,928 affiliated_with 121,257
Institutions 21,979 associated_concept 2,739,665
Concepts 109,750 associated_topic. 95,659
Topics 10 cites 134,945
Total 336,887 3,332,151
Table 1. COVID-19 Knowledge Graph entity and relations.
Author Entities.

Representation of the paper authors. Attributes include the first, middle, and last names.

Institution Entities.

Institution affiliations for authors. Attributes include institution name, country, and city.

Concept Entities.

Comprehend Medical (CM) Detect Entities V2 is an Amazon Web Service that uses natural language processing (NLP) and machine learning for medical language entity recognition and relationship extraction 


. CM classifies extracted entities into entity types: Ibuprofen (entity) belongs to the Medications category (entity type). We leverage CM to extract biomedical entities from the scientific articles. Specifically, given the example text ”Abdominal ultrasound noted acute appendicitis, recommend appendectomy followed by several series of broad spectrum antibiotics,” CM extracts

Abdominal (Anatomy), ultrasound (Test Treatment Procedure), acute appendicitis (Medical Condition), appendectomy (Test Treatment Procedure), and antibiotics (Medication) as recognized entities along with model confidence scores. Entity names e.g. acute appendicitis, form concept entities in the CKG while entity category and model confidence score are the entities’ attributes.

Topic Entities.

We use an extension of Latent Dirichlet Allocation (LDA) (Blei et al., 2003) termed Z-LDA (Andrzejewski and Zhu, 2009), trained using the title, abstract and body text from each paper. Labels are generated with the help of medical professionals to eliminate, merge, and form 10 topics which serve as the basis for topic entities in the CKG: Vaccines/Immunology, Genomics, Public Health Policies, Epidemiology, Clinical Treatment, Virology, Influenza, Healthcare Industry, Lab Trials (human) and Pulmonary infections. Re-modeling and manually labeling a topic model is inefficient, therefore we train a multi-label classifer (Read et al., 2011) using the original topic model labels and a training split from 59k total documents. The resulting classifier achieves an average F score of 91.92 with on average 2.37 labels per document.

2.3. Relation Types

Relations in the CKG are directed and summarized in Table 1. Here we defined all relation types.


This relation connects paper entities with author entities and indicates that authorship relation.


This relation connects author entities with institution entities and indicates that affiliated relation.


This relation connects paper entities with concept entities and indicates that associated relation. These relation have the CM model confidence score as an attribute.


This relation connects paper entities with topic entities and indicates that associated relation. These relation have the Z-LDA prediction score as an attribute.


This relation connects paper entities with paper references indicating a citation relation.

2.4. CKG Curation

2.4.1. Concept Normalization

We use thresholding on the confidence scores as a de-noising step by requiring an entity’s confidence scores to exceed a threshold that is determined through empirical experimentation. We explored a parameter range of - in increments. Thresholding comes at the expense of entity coverage: higher confidence threshold increases the likelihood of papers with no or few extracted entities. Next, we lemmatize concept entity names as a form of normalization using SciSpacy (Neumann et al., 2019). SciSpacy is built upon the robust SpaCy NLP library (8), but is trained on biomedical texts similar to those in the CORD19 dataset. We experimentally found SciSpacy to provide target results for limited string lemmatization test cases. Moreover, we keep a running distribution of concept appearances across the dataset. A concept may appear in papers, where is the total number of papers in the dataset. We prune concepts that occur in less than 0.0001%. Concepts that appear in greater than 50% are flagged for manual qualitative assessment of information value. The main downside of this approach is scalability and in future work we plan to systematize and extend this process using domain-specific specialized ontology standardization tools like Comprehend Medical RxNorm (4).

2.4.2. Author Normalization

Author names in the CORD-19 dataset require judicious processing. Oftentimes, paper authors have incomplete information such as missing “first name” or high name variation between different academic journals. Additionally, author citations often follow an abbreviated format using “first initial, last name”. We utilize a hybrid approach similar to (Ammar et al., 2018) involving normalization and linking. When linking authors, we normalize author names via lower casing, removing punctuation, and merging “first, middle, last name”.

2.4.3. Citation Linking

We also normalize the author information in the cited papers and match the normalized author names. This allows us to link papers based on citations.

We require that both normalized author information and article title information match exactly. From here, we include citation links for papers referenced within the CORD-19 dataset and find   of papers cite another paper available in the CORD-19.

Figure 2. Degree distribution of CKG for various sub-graphs: shows degree change of CKG with concept relations removed; citation relations removed; topic relations removed.

2.5. Graph Statistics

Table 1 provides counts of all entity and relation types. The 42k paper entities have on average 2.3 outgoing topic relations, 64.9 outgoing relations to concepts, 5.7 outgoing relations to authors and 3.2 outgoing citation relations. Furthermore, 29k paper entities have at least one outgoing citation relation to another paper, 18k have at least one incoming citation relation from another paper, 14.6k have at least one incoming and outgoing citation relation, and 9.7k have neither an incoming nor outgoing citation relation. The 163k author entities have on average outgoing relations to institutions indicating not all authors have institution information in the data. When considering an undirected version of the graph, there are connected components with the diameter of the largest connected component (CC) equaling entities that indicates one large CC contains 99% of relations and entities, while the diameter () indicates the CKG is dense. Figure 2 shows the undirected degree distribution plot of several sub-graphs of the CKG. We observe that the greatest change in degree distribution comes from the sub-graph without concept relations, exemplifying that concepts form key links in the graph.

2.6. Infrastructure

We use Amazon Neptune, a fully-managed graph database optimized for storage and navigation scaling to billions of relationships. Neptune supports property graphs and the query languages like Apache TinkerPop Gremlin and SPARQL. Neptune’s Bulk Loading (1) feature helps reduce data ingestion time from several hours (sequential loading) to minutes for   entities and   relations using a db.r5.4xlarge (8 cores, 16 vcpu, 128 Gb Memory, 3500 Mbps storage bandwidth) Amazon Elastic Compute Cloud (EC2) instance. By utilizing (5) users can find the exported Neptune graph data, Amazon CloudFormation (2) templates for one-click recreation and deployment of the CKG, and the structured entities and relation files as comma-separated values (CSV) files. We use Tom Sawyer Graph Database browser for visualizations (9).

Figure 3. Query 1: author research leaders [blue box] ii) institutional leaders [gold box] iii) institution collaborations [green]

3. Using CKG for Information Retrieval

In this section we show the CKG uncovers intricate relationships in CORD-19 scientific articles that can aid the research and policy decision processes.

  • Query 1: What authors and institutions are publishing research pertaining to the drug remdesivir and human lab trial?

COVID-19 has highlighted the difficulty of health and public policy decision making during pandemics. The above question is motivated by the scenario where policy makers are interested in forming a task force of leading authors and institutions on a rapidly evolving area of research such as a drug treatments for COVID-19. Remdesivir is an investigational nucleotide analog drug currently in FDA clinical trials by Gilead Sciences (6). A CKG user can structure a query identifying articles with remdesivir concept and lab trials (human) topics form connections. Paper to concept and topic relations form ”one-hop” relations. From here we find paper to author relations via another ”one-hop” operation and subsequently, author to institution relations via a second ”one-hop” (two-hops total) operation. Figure 3 visually depicts this query process using a small subset of the graph. The author entity, surround by a blue box, is connected to two papers discussing both remdesivir and lab trials (blue arrows). This author can be viewed as research leader for this query. Similarly, the institutional research leader of this sub-graph is the vertex surrounded in gold box, connected to multiple authors who have published articles matching this query. Lastly, the CKG also helps to uncover multiple-organization collaborations depicted by the vertex surrounded by green box and arrows.

  • Query 2: What papers discussing COVID-19 risk factors are most often cited by researchers within the CORD-19 dataset?

Researchers can query the CKG to return scientific articles related to specific COVID-19 risk factors such as asthma, heart disease, and respiratory malfunction. The query returns articles with related risk factors. Next, the citation network is leveraged to rank articles by citation counts within the data set. Table 2 shows the top three results for this query and the respective citations.

CORD_UID Title Cited By
grw5s2pf The Molecular Biology of Coronaviruses 498
Bocavirus and Acute Wheezing
in Children
A Diverse Group of Previously Unrecognized
Human Rhinoviruses Are Common
Causes of Respiratory Illnesses in Infants
Table 2. Graph query results.

4. Using CKG for article recommendations

In this section we combine article semantic information with CKG topological information to quantify similarity between articles and construct a similarity-based recommendation system.

4.1. Leveraging Embeddings

4.1.1. Semantic Embeddings

In order to capture semantic information across the CORD-19 scientific articles we leverage SciBERT (Beltagy et al., 2019)

that has shown strong transfer learning performance on a wide variety of NLP tasks

(Cer et al., 2018). Specifically, our goal is to represent CORD-19 scientific articles as dense document embeddings.

Sentence Transformer library creates sentence level embeddings from the plain text articles (Reimers and Gurevych, 2019)

. We tokenize the title, abstract and body text into sentences and then using SciBert to create three embedding matrices representing sentences from component of the article. Next, we average each metric to compute three dense vectors. Finally, a single dense document embedding is obtained by averaging the vectors.

Table 3

shows the average pairwise cosine similarity of the semantic embeddings constructed from the title, abstract, and body. The cosine similarity matrix among paper pairs is averaged to obtain average similarity for each text portion. We observe the average similarity of scientific articles and availability in the dataset differ based on the article text portion used, noting titles on average have lower similarity and have the highest dataset coverage compared to abstracts or body text. The lower coverage of abstracts drove our decision to combine body and title text with abstracts.

Text Type Cosine Similarity Data Coverage
title .266 97.7%
abstract .139 84.9%
body .092 98.6%
.131 99.8%
Table 3. Average cosine distance and percent of dataset coverage using SciBERT embeddings.

4.1.2. Knowledge Graph Embeddings: TransE

We leverage knowledge graph embedding (termed KGE) methodology to encode entities and relations (relations) in the COVID-19 Knowledge Graph as -dimensional vector embeddings. The embeddings associated with the entities and relations of the graph are generated by a specific KGE algorithm TransE (Bordes et al., 2013) that satisfy a predetermined mathematical model. We can use these embeddings for downstream tasks such as paper recommendation (Zhang et al., 2019). In particular, papers with high similarity in embedding space will be highly correlated.

The knowledge graph is composed of entities and relations such that , where represents graph entities and represents the set of relations connecting entities. A specific instance of a relation is represented as a triplet , in which is the head entity, the type of the relation, and the tail entity. Given a set of triplets in the above format, TransE learns a low-dimensional vector for each entity and relationship where

by minimizing a margin-based objective function over the training set using stochastic gradient descent


where is the scoring function; , , are the embedding of the head entity , relation and the tail entity , and is a predefined constant. Here and represent the positive and negative sets of triplets respectively, and if the triplet corresponds to a positive example and otherwise. Negative triplets are corrupted versions of the extant (positive) triplets defined by the KG, in which either the head or the tail entity have been randomly swapped for another entity in .

We leverage the Deep Graph Library Knowledge Embedding library (DGL-KE) (Zheng et al., 2020), a high performance package for learning large-scale KGE, to train the aforementioned KGE model. By supplying the model with both the entities and relation triplets as described in table 1, we generate vector embeddings for each paper.

4.1.3. Relational Graph Convolutional Network

KGE models generate embeddings solely by taking into account the structure of the graph. Nevertheless, the learned semantic embeddings can be used as relation features for learning paper relation embeddings. In this section we present an experiment extending the KGE methodology by directly incorporating semantic information to learn paper embeddings that directly capture semantic and topological information. While KGE models do not directly exploit relation features graph convolutional networks can exploit such relation features and possibly obtain richer embeddings (Kipf and Welling, 2017). For this purpose, we apply a relational graph convolutional network (termed RGCN) model (Schlichtkrull et al., 2018) to learn the relation embeddings exploiting both paper semantic features as well as the graph structure.

An RGCN model is comprised by a sequence of RGCN layers. The output of the th RGCN layer for relation

is a nonlinear combination of the hidden representations of neighboring entities weighted based on the relation type. The relation features are the input of the first layer in the model, which are the semantic paper embeddings. For relation types without features we use an embedding layer that takes as input an one-hot encoding of the relation id.

The entity embeddings are obtained by the final layer of the RGCN. The major difference among RGCN and KGE is that RGCN embeddings are learned with graph convolutions and take into account relation features whereas the KGE embeddings are just supervised by equation (1(Schlichtkrull et al., 2018; Zheng et al., 2020). Recaping, the RGCN relation embeddings combine both the graph structure information as well as the relation features generated by the semantic embedding methods. We implement and train the RGCN model using the DGL framework (Wang et al., 2019). The RGCN model was parametrized with hidden units per layer, hidden layers.

4.2. Similarity Engine Construction

Our document similarity engine uses a combinations of the semantic and KGE embeddings as the RGCN model under-performed in certain ways as shown in Section 5. Thereby we capture semantic information contained within a publication with the paper’s topological information from the CKG e.g. papers, authors, concepts, topics, etc. relations. Given a paper, the engine retrieves a list of top-k most similar papers using cosine distance.

5. Analysis

This section is organized into two parts presenting metrics and results evaluating the work done in Sections 2 and 4 respectively. Part one validates the construction and curation of the CKG by showing article topics align with common subject focuses of scientific journals and CKG relations are high quality. Part two analyzes the results of the similarity engine demonstrating we can improve the quality of recommended articles using both semantic and topological information.

5.1. Graph Validation

5.1.1. Topic Model Validation

Most journals have well defined topics. For example, Journal of Virology explores the nature of viruses and mainly focuses on related domains; Journal of Vaccine focuses on the field of vaccinology. To evaluate our topic model, we summarise the generated topics from papers in the CKG belonging to these two journals in Figure 4. It can be seen that the generated topics of papers from Journal of Virology, e.g., virology, genomics and lab-trials-human, are highly related to virology. The generated topics of papers from Journal of Vaccine, e.g., vaccines-immunology, are highly related to vaccinology.

Figure 4. Distribution of topics by journal
Figure 5. link prediction score distribution by relation types

5.1.2. CKG Relation Validation

To assess the correctness of the triplets that make up the CKG, we used the KGE model described in Section 4.1.2 to score each of its triplets using


where and are the embeddings for the head and tail entities, is the embedding of the relation type, and

is an offset used to accelerate the training. We compute these scores for all of CKG’s triplets by following a 10-fold strategy to split the triplets into 10 sets. In this approach, for each fold we used the remaining 9 folds to estimate the KGE model and used it to computed the scores for the left-out fold. According to Equation 


, if the score computed for a triplet is around 0, then the triplet is consistent with the KGE model. On the other hand, if the score is further away from 0 (in either direction), then the triplet corresponds to potentially an outlier or an error. Figure 

5 shows the score distribution of the triplets for different relation types. These results show that the score of most triplets is close to 0 and that there is only a small fraction of inconsistent (according to the model) triplets.

5.2. Recommendation Analysis

5.2.1. Topic Similarity

We start by analyzing the topic similarity between each source paper and its top-5 most similar papers. In Table 4 a baseline is established by generating a top-5 list of papers random selected from the   scientific articles. Then, we collect top-5 similar article recommendations , for every source paper using four different embedding methods (Semantic, KGE, RGCN and Semantic&KGE). We make use of topic-based distances to compute measures of similarity by creating a one-hot encoded vector for every paper in our dataset representing its topics e.g. contains or not. Jaccard distance (Kosub, 2016) is used to compute distance between vectors


where is the number of occurrences of and , .

Intra-List Similarity (ILS) (Ziegler et al., 2005) is used to measure topic similarity of paper recommendations using the average Jaccard distance between a source paper and its list of similar papers. We then take the average of scores over all source papers and compare across methods as displayed in Table 4. For each source paper we define its topic similarity


According to Equation 4, the lower the score, the more common topics are between the source paper and its similar papers.

In Table 4 we observe lower average Jaccard scores between source papers and similar recommendations relative to the baseline in all embedding methods. Furthermore, we note KGE embedding achieves a comparatively lower score than RGCN. Finally, the combination of semantic and KGE embeddings achieves the lowest Jaccard score.

Method Topic Similarity
Random .821
Semantic .360
Graph .345
Graph .654
Sem. & KGE .311
Table 4. Topic similarity (Jaccard distance) of recommendations vs random baseline.

5.2.2. Citation Similarity

The CKG citation network shows the relationship between papers. If a paper is cited by another, they may share the same topic, use similar technology or have similar motivation. We train RGCN embeddings from the CKG with and without the citation network and follow the same methodology for KGE embeddings. We select only papers that cite at least one other paper. For each of these papers we generate the top-5 similar papers and calculate the average number of a paper’s citations that appear in the top-5 recommended most similar papers. For Table 5 we average this score across all papers for the four RGCN and KGE embeddings. We observe that KGE trained with citations has the highest overlap score at 29.11% as expected. Further, KGE embeddings learned without citations do a poor job of recommending cited papers in the top-5. This is expected since the relations authored_by, associated_topic, and associated_concept do not give much information to infer the exact citation: many papers share the same topic and concept.

Method Overlap
RGCN 5.22%
KGE 0.01%
RGCN 8.96%
KGE 29.11%
Table 5. RGCN vs KGE Top-5 Citation Overlap
Figure 6. Visual comparison of truncated SVD of four embedding methods using five scientific articles in the dataset. Paper CORD_UIDs: pw60qx7c, fjfc3rto, 790d7v7q, v2lp739t, kt5awf8i
Random Semantic KGE RGCN
Random 1.000 0.014 0.009 0.008
Semantic - 1.000 0.084 0.081
Graph - - 1.000 0.137
Graph - - - 1.000
Sem & KGE 0.10 0.164 0.463 0.005
Table 6. Overlapping (intersection over union) scores of Top-5 similar papers by methodology

5.2.3. Embedding Subspace

We use truncated singular value decomposition (SVD) to create 2D projections of paper embeddings of different embeddings methods. We select 5 papers with different topics in our dataset and their corresponding

top-5 recommendations. We plot the truncated SVD reduction of their embeddings and plot them based on the source paper. The results are represented in Figure 6. The top left shows the SciBERT embeddings for the five papers and their associated topics (color coded according to paper). We observe the topics genomics and epidemiology and human lab trials as described in Section 2, are close to each other. This is expected as many genomic studies are genome wide association studies, which are considered a subset of epidemiology. The top right shows the KGE embeddings’ SVD result. It can be seen papers from same topics are clustering to each other while separating across topics. On the other side, the combination of SciBERT embeddings with KGE embeddings which is currently used in the similarity engine (bottom left) shows that virology and vaccines immunology, and genomics and epidemiology and human lab trials narrow in proximity from KGE. This matches the observed research given virology is the study of viruses while similarly, vaccines immunology is the study of how viral immunizations stimulate the immune system hence closer embedding similarity match expectations of researchers.

Figure 7. Popularity (= occurrences of paper in the top-5 most similar paper list) analysis for semantic embedding and KGE embedding engine grouped by bins.

5.2.4. Recommendation Overlap

We generate top-5 most similar papers for each paper in the dataset using five different methodologies, Random (Randomly select 5 papers), Semantic, KGE, RGCN and Semantic&KGE. Table 6 captures the intersection over union of similar paper sets across methodologies. We observe a low overlapping between semantic and graph embeddings, which is as expected since Semantic capture the semantic information of certain paper while KGE/RGCN capture the topological information of the CKG. The combination of them, i.e. Semantic&KGE, shows the agreement with both side, which means it can recommend papers with a conjunction of both semantic and topological information..

5.2.5. Popularity

Figure 7 presents a popularity analysis of KGE and Semantic Embedding, where popularity captures the number of occurrences of an individual paper in the top-5 most similar items list for all papers in the dataset grouped by frequency. The left tail of the distribution shows papers that occur many time times in top-5 recommended lists with the overall distribution resembling a power law distribution common to recommendation systems (Jannach et al., 2013). For KGE embeddings 707 papers occur more than 20 times and for semantic 912 occur more than 20 times.

6. Conclusion

In this paper we construct a COVID-19 Knowledge Graph from the CORD-19 dataset and demonstrate how researchers and policy makers can extract timely information to answer key scientific questions on COVID-19 from a corpus of scientific articles. To further facilitate efforts we employ machine learning entity detection models to extract medical entities and relationships. With the help of medical professionals we add global topic information that forms additional medical relationships in the CKG. We train KGE models using CKG relations to obtain paper embeddings capturing topological isomorphic and semantic information for the application of similar paper retrieval on Future work may include further enhancements to CKG information retrieval capabilities such as: expanding biomedical entity extraction using biomedical concept annotators like PubTator222, re-training RGCN models with additional entity and relation attributes, and incorporating additional KGs into the CKG e.g. COVID-19 drug repurposing graphs (Gramatica et al., 2014).


  • [1] Note: Cited by: §2.6.
  • [2] Note: Cited by: §2.6.
  • [3] Note: Cited by: §2.2.
  • [4] Note: Cited by: §2.4.1.
  • [5] Note: Cited by: §2.6.
  • [6] Note: Cited by: §3.
  • [7] Note: Cited by: §2.1.
  • [8] Note: Cited by: §2.4.1.
  • [9] Note: Cited by: §2.6.
  • W. Ammar, D. Groeneveld, C. Bhagavatula, I. Beltagy, M. Crawford, D. Downey, J. Dunkelberger, A. Elgohary, S. Feldman, V. Ha, et al. (2018) Construction of the literature graph in semantic scholar. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers), pp. 84–91. Cited by: §2.4.2.
  • D. Andrzejewski and X. Zhu (2009) Latent dirichlet allocation with topic-in-set knowledge. In

    Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing

    pp. 43–48. Cited by: §2.2.
  • I. Beltagy, K. Lo, and A. Cohan (2019) SciBERT: a pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3606–3611. Cited by: §1, §4.1.1.
  • D. M. Blei, A. Y. Ng, and M. I. Jordan (2003) Latent dirichlet allocation. Journal of machine Learning research 3 (Jan), pp. 993–1022. Cited by: §2.2.
  • A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko (2013) Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems, pp. 2787–2795. Cited by: §4.1.2.
  • D. Cer, Y. Yang, S. Kong, N. Hua, N. Limtiaco, R. S. John, N. Constant, M. Guajardo-Cespedes, S. Yuan, C. Tar, et al. (2018) Universal sentence encoder. arXiv preprint arXiv:1803.11175. Cited by: §4.1.1.
  • J. Dalton, L. Dietz, and J. Allan (2014) Entity query feature expansion using knowledge base links. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 365–374. Cited by: §1.
  • R. Gramatica, T. Di Matteo, S. Giorgetti, M. Barbiani, D. Bevec, and T. Aste (2014) Graph theory enables drug repurposing–how a mathematical model can drive the discovery of hidden mechanisms of action. PloS one 9 (1). Cited by: §6.
  • D. Jannach, L. Lerche, F. Gedikli, and G. Bonnin (2013) What recommenders recommend–an analysis of accuracy, popularity, and sales diversity effects. In International conference on user modeling, adaptation, and personalization, pp. 25–37. Cited by: §5.2.5.
  • Y. W. Kim and J. H. Kim (1990) A model of knowledge based information retrieval with hierarchical concept graph. Journal of Documentation. Cited by: §1.
  • T. N. Kipf and M. Welling (2017) Semi-supervised classification with graph convolutional networks. In iclr, Toulon, France. Cited by: §4.1.3.
  • S. Kosub (2016) A note on the triangle inequality for the jaccard distance. CoRR abs/1612.02696. External Links: Link, 1612.02696 Cited by: §5.2.1.
  • M. Neumann, D. King, I. Beltagy, and W. Ammar (2019) ScispaCy: fast and robust models for biomedical natural language processing. In Proceedings of the 18th BioNLP Workshop and Shared Task, pp. 319–327. Cited by: §2.4.1.
  • J. Read, B. Pfahringer, G. Holmes, and E. Frank (2011) Classifier chains for multi-label classification. Machine learning 85 (3), pp. 333. Cited by: §2.2.
  • N. Reimers and I. Gurevych (2019) Sentence-bert: sentence embeddings using siamese bert-networks. arXiv preprint arXiv:1908.10084. Cited by: §4.1.1.
  • M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling (2018) Modeling relational data with graph convolutional networks. In European Semantic Web Conference, pp. 593–607. Cited by: §1, §4.1.3, §4.1.3.
  • L. L. Wang, K. Lo, Y. Chandrasekhar, R. Reas, J. Yang, D. Eide, K. Funk, R. Kinney, Z. Liu, W. Merrill, et al. (2020) CORD-19: the covid-19 open research dataset. arXiv preprint arXiv:2004.10706. Cited by: §1, §2.1.
  • M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, et al. (2019)

    Deep graph library: towards efficient and scalable deep learning on graphs

    arXiv preprint arXiv:1909.01315. Cited by: §4.1.3.
  • Q. Wang, Z. Mao, B. Wang, and L. Guo (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29 (12), pp. 2724–2743. Cited by: §1.
  • Y. Zhang, X. Chen, Y. Yang, A. Ramamurthy, B. Li, Y. Qi, and L. Song (2019) Can graph neural networks help logic reasoning?. arXiv preprint arXiv:1906.02111. Cited by: §4.1.2.
  • D. Zheng, X. Song, C. Ma, Z. Tan, Z. Ye, J. Dong, H. Xiong, Z. Zhang, and G. Karypis (2020) DGL-ke: training knowledge graph embeddings at scale. arXiv preprint arXiv:2004.08532. Cited by: §1, §4.1.2, §4.1.3.
  • C. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen (2005) Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, pp. 22–32. Cited by: §5.2.1.