Exploring Use Cases for CovidGraph.

HealthECCO is the driving force behind the COVID-19 knowledge graph spanning multiple biomedical data domains. One way to access CovidGraph is SemSpect, an interface designed for data exploration in graphs. To showcase the possibilities that arise from integrating a variety of COVID-19 related data sources over the last three years, we present three use cases from the (bio-)medical domain. Availability: The project is open source and freely available from: https://healthecco.org/covidgraph/. The source code and documentation are available on GitHub: https://github.com/covidgraph.

Molecular cartooning with knowledge graphs.

Molecular "cartoons," such as pathway diagrams, provide a visual summary of biomedical research results and hypotheses. Their ubiquitous appearance within the literature indicates their universal application in mechanistic communication. A recent survey of pathway diagrams identified 64,643 pathway figures published between 1995 and 2019 with 1,112,551 mentions of 13,464 unique human genes participating in a wide variety of biological processes. Researchers generally create these diagrams using generic diagram editing software that does not itself embody any biomedical knowledge. Biomedical knowledge graphs (KGs) integrate and represent knowledge in a semantically consistent way, systematically capturing biomedical knowledge similar to that in molecular cartoons. KGs have the potential to provide context and precise details useful in drawing such figures. However, KGs cannot generally be translated directly into figures. They include substantial material irrelevant to the scientific point of a given figure and are often more detailed than is appropriate. How could KGs be used to facilitate the creation of molecular diagrams? Here we present a new approach towards cartoon image creation that utilizes the semantic structure of knowledge graphs to aid the production of molecular diagrams. We introduce a set of "semantic graphical actions" that select and transform the relational information between heterogeneous entities (e.g., genes, proteins, pathways, diseases) in a KG to produce diagram schematics that meet the scientific communication needs of the user. These semantic actions search, select, filter, transform, group, arrange, connect and extract relevant subgraphs from KGs based on meaning in biological terms, e.g., a protein upstream of a target in a pathway. To demonstrate the utility of this approach, we show how semantic graphical actions on KGs could have been used to produce three existing pathway diagrams in diverse biomedical domains: Down Syndrome, COVID-19, and neuroinflammation. Our focus is on recapitulating the semantic content of the figures, not the layout, glyphs, or other aesthetic aspects. Our results suggest that the use of KGs and semantic graphical actions to produce biomedical diagrams will reduce the effort required and improve the quality of this visual form of scientific communication.

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision -making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision -making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.

Lessons learned to enable question answering on knowledge graphs extracted from scientific publications: A case study on the coronavirus literature.

The article presents a workflow to create a question-answering system whose knowledge base combines knowledge graphs and scientific publications on coronaviruses. It is based on the experience gained in modeling evidence from research articles to provide answers to questions in natural language. The work contains best practices for acquiring scientific publications, tuning language models to identify and normalize relevant entities, creating representational models based on probabilistic topics, and formalizing an ontology that describes the associations between domain concepts supported by the scientific literature. All the resources generated in the domain of coronavirus are available openly as part of the Drugs4COVID initiative, and can be (re)-used independently or as a whole. They can be exploited by scientific communities conducting research related to SARS-CoV-2/COVID-19 and also by therapeutic communities, laboratories, etc., wishing to find and understand relationships between symptoms, drugs, active ingredients and their documentary evidence.

Conco-ERNIE: Complex User Intent Detect Model for Smart Healthcare Cognitive Bot

The outbreak of Covid-19 has exposed the lack of medical resources, especially the lack of medical personnel. This results in time and space restrictions for medical services, and patients cannot obtain health information all the time and everywhere. Based on the medical knowledge graph, healthcare bots alleviate this burden effectively by providing patients with diagnosis guidance, pre-diagnosis, and post-diagnosis consultation services in the way of human-machine dialogue. However, the medical utterance is more complicated in language structure, and there are complex intention phenomena in semantics. It is a challenge to detect the single intent, multi-intent, and implicit intent of a patient's utterance. To this end, we create a high-quality annotated Chinese Medical query (utterance) dataset, CMedQ (about 16.8k queries in medical domain which includes single, multiple, and implicit intents). It is hard to detect intent on such a complex dataset through traditional text classification models. Thus, we propose a novel detect model Conco-ERNIE, using concept co-occurrence patterns to enhance the representation of pre-trained model ERNIE. These patterns are mined using Apriori algorithm and will be embedded via Node2Vec. Their features will be aggregated with semantic features into Conco-ERNIE by using an attention module, which can catch user explicit intents and also predict user implicit intents. Experiments on CMedQ demonstrates that Conco-ERNIE achieves outstanding performance over baseline. Based on Conco-ERNIE, we develop an intelligent healthcare bot, MedicalBot. To provide knowledge support for MedicalBot, we also build a Chinese medical graph, CMedKG (about 45k entities and 283k relationships). © 2023 Association for Computing Machinery.

Short Analysis of the Impact of COVID-19 Ontologies

During the COVID-19 pandemic, researchers started to develop technical approaches to solve the numerous challenges imposed by the new pandemic. One fundamental precondition for research is to make relevant data about the COVID-19 pandemic available in a machine-processable way. For this purpose, COVID-19 ontologies and knowledge graphs have been developed and proposed for many different subareas of COVID-19 applications and research. In this paper, we provide a short analysis of the impact of COVID-19 ontologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19

Question Answering based on Knowledge Graph (KG) has emerged as a popular research area in general domain. However, few works focus on the COVID-19 kg-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at https://github.com/CHNcreater/COVID-19-KGQA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning. © 2023 Owner/Author.

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature;(2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction;(3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects. © 2022 IEEE.

Beyond Natural Language Processing: Building Knowledge Graphs to Assist Scientists Understand COVID-19 Concepts

To combat COVID-19, scientists must digest the vast amount of relevant biomedical knowledge in the literature to understand disease mechanisms and related biological functions. Nearly 3,000 scientific papers are published on PubMed every day. This knowledge bottleneck has resulted in severe delays in developing COVID-19 vaccines and drugs. Our research produces a hierarchy of knowledge concepts related to COVID-19, designed to assist scientists in answering questions and generating summaries. It aims to discover scientific and comprehensive knowledge to extract fine-grained multimedia elements (i.e., physical and visual structures, relational events and events, and chemical knowledge). Our project is toward one step in natural language understanding: detailed contextual sentences, subgraphs, and knowledge subgraphs are the first time to be automatically generated, and relations and coreferences of COVID-19 mentions will be sketched. Extensive results show that our method outperforms other state-of-the-art methods. In addition, we have published the generated knowledge graph on Google Drive1 and released the source in the Github2. © 2022 ACM.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

The medical conversational system can relieve doctors' burden and improve healthcare effi-ciency, especially during the COVID-19 pan-demic. However, the existing medical dialogue systems have die problems of weak scalability, insufficient knowledge, and poor controlla-bility. Thus, we propose a medical conversa-tional question-answering (CQA) system based on the knowledge graph, namely MedConQA, which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures, including medi-cal triage, consultation, image-text drug recom-mendation, and record. Each module has been open-sourced as a tool, which can be used alone or in combination, with robust scalability. Besides, to conduct knowledge-grounded dia-logues with users, we first construct a Chinese Medical Knowledge Graph (CMKG) and col-lect a large-scale Chinese Medical CQA (CM-CQA) dataset, and we design a series of meth-ods for reasoning more intellectually. Finally, we use several state-of-the-art (SOTA) tech-niques to keep the final generated response more controllable, which is further assured by hospital and professional evaluations. We have open-sourced related code, datasets, web pages, and tools, hoping to advance future research. © 2022 Association for Computational Linguistics.

Empowering COVID-19 Fact-Checking with Extended Knowledge Graphs

During the COVID-19 outbreak, fake news regarding the disease have spread at an increasing rate. Let's think, for instance, to face masks wearing related news or various home-made treatments to cure the disease. To contrast this phenomenon, the fact-checking community has intensified its efforts by producing a large number of factchecking reports. In this work, we focus on empowering knowledge-based approaches for misinformation identification with previous knowledge gathered from existing fact-checking reports. Very few works in literature have exploited the information regarding claims that have been already fact-checked. The main idea that we explore in this work is to exploit the detailed information in the COVID-19 fact check reports in order to create an extended Knowledge Graph. By analysing the graph information about the already checked claims, we can verify newly coming content more effectively. Another gap that we aim to fill is the temporal representation of the facts stored in the knowledge graph. At the best of our knowledge, this is the first attempt to associate the temporal validity to the KG relations. This additional information can be used to further enhance the validation of claims.

LaSER: Language-specific event recommendation

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the Cesar Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Graph Network based Approaches for Multi-modal Movie Recommendation System

The three times increase of SonyLiv viewers during the Tokyo Olympic, the 10% hike of YouTube users during the isolation era of covid-pandemic, and the 19% growth in Netflix user count due to the fastest growth of OTT, etc. have made the digital platform's mode all-time active and specific. The hourly increase of users' interactions and the e-commerce platform's desire of letting users engage on their sites are pushing researchers to shape the virtual digital web as user specific and revenue-oriented. This paper develops a deep learning-based approach for building a movie recommendation system with three main aspects: (a) using a knowledge graph to embed text and meta information of movies, (b) using multi-modal information of movies like audio, visual frames, text summary, meta data information to generate movie/user representations without directly using rating information;this multi-modal representation can help in coping up with cold-start problem of recommendation system (c) a graph attention network based approach for developing regression system. For meta encoding, we have built knowledge graph from the meta information of the movies directly. For movie-summary embedding, we extracted nouns, verbs, and object to build a knowledge graph with head-relation-tail relationships. A deep neural network, as well as Graph attention networks, are utilized for measuring performance in terms of RMSE score. The proposed system is tested on an extended MovieLens-100K data-set having multi-modal information. Experimental results establish that only rating-based embeddings in the current setup outperform the state-of-the-art techniques but usage of multi-modal information in embedding generation performs better than its single-modal counterparts. 1. © 2022 IEEE.

Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments' toxicities.

In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug-drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.

Detecting fake news using machine learning and reasoning in Description Logics

Reasoning in Description Logics (DLs) can detect inconsistencies between trusted knowledge and not trusted sources. The proposed method is exemplified on fake news for Covid19. Machine learning is used to generate DL axioms from positive and negative examples using tools such as DL-Learner. The resulted knowledge graph formalised in DL is merged with the trusted ontologies on Covid-19. Reasoning in DL is then performed with the Racer engine, which is responsible to detect inconsistencies within the ontology. When detecting inconsistencies, a "red flag"is raised to signal possible fake news and the corresponding counterspeech is generated. © 2022 IEEE.

e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.

Target discovery and identification processes are driven by the increasing amount of biomedical data. The vast numbers of unstructured texts of biomedical publications provide a rich source of knowledge for drug target discovery research and demand the development of specific algorithms or tools to facilitate finding disease genes and proteins. Text mining is a method that can automatically mine helpful information related to drug target discovery from massive biomedical literature. However, there is a substantial lag between biomedical publications and the subsequent abstraction of information extracted by text mining to databases. The knowledge graph is introduced to integrate heterogeneous biomedical data. Here, we describe e-TSN (Target significance and novelty explorer, http://www.lilab-ecust.cn/etsn/), a knowledge visualization web server integrating the largest database of associations between targets and diseases from the full scientific literature by constructing significance and novelty scoring methods based on bibliometric statistics. The platform aims to visualize target-disease knowledge graphs to assist in prioritizing candidate disease-related proteins. Approved drugs and associated bioactivities for each interested target are also provided to facilitate the visualization of drug-target relationships. In summary, e-TSN is a fast and customizable visualization resource for investigating and analyzing the intricate target-disease networks, which could help researchers understand the mechanisms underlying complex disease phenotypes and improve the drug discovery and development efficiency, especially for the unexpected outbreak of infectious disease pandemics like COVID-19.

LaSER: Language-specific event recommendation

While societal events often impact people worldwide, a significant fraction of events has a local focus that primarily affects specific language communities. Examples include national elections, the development of the Coronavirus pandemic in different countries, and local film festivals such as the César Awards in France and the Moscow International Film Festival in Russia. However, existing entity recommendation approaches do not sufficiently address the language context of recommendation. This article introduces the novel task of language-specific event recommendation, which aims to recommend events relevant to the user query in the language-specific context. This task can support essential information retrieval activities, including web navigation and exploratory search, considering the language context of user information needs. We propose LaSER, a novel approach toward language-specific event recommendation. LaSER blends the language-specific latent representations (embeddings) of entities and events and spatio-temporal event features in a learning to rank model. This model is trained on publicly available Wikipedia Clickstream data. The results of our user study demonstrate that LaSER outperforms state-of-the-art recommendation baselines by up to 33 percentage points in MAP@5 concerning the language-specific relevance of recommended events.

CheetahKG: A Demonstration for Core-based Top-k Frequent Pattern Discovery on Knowledge Graphs

Knowledge graphs capture the complex relationships among various entities, which can be found in various real world applications, e.g., Amazon product graph, Freebase, and COVID-19. To facilitate the knowledge graph analytical tasks, a system that supports interactive and efficient query processing is always in demand. In this demonstration, we develop a prototype system, CheetahKG, that embeds with our state-of-the-art query processing engine for the top-k frequent pattern discovery. Such discovered patterns can be used for two purposes, (i) identifying related patterns and (ii) guiding knowledge exploration. In the demonstration sessions, the attendees will be invited to test the efficiency and effectiveness of the query engine and use the discovered patterns to analyze knowledge graphs on CheetahKG. © 2022 IEEE.

Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on Knowledge Graph

The aim is to construct a country-dimension knowledge graph of COVID-19 vaccines from the information of COVID-19 vaccines and to analyze the leading countries of vaccine R&D by combining the advantages of easy operation and intuitive feeling of knowledge graph visualization, to provide a reference for Chinese vaccine R&D departments and international cooperation. In this paper, through data collection, based on entity extraction and relationship construction, a knowledge graph of country dimensions was established by specifying the central vaccine R&D countries and vaccine distribution, and multidimensional microdata such as word frequency and betweenness centrality were combined to analyze the national characteristics of the COVID-19 vaccine. The analysis of the knowledge graph of the country dimension of the COVID-19 vaccine shows that countries with robust technology and economies, such as the US and China, choose to develop vaccine distribution independently, countries with advanced economies, such as Saudi Arabia, decide to purchase vaccine distribution, and less developed countries, such as South Africa and Latin America, need international aid for vaccines or purchase low-cost vaccines. This paper constructs the correlation between nodes and nodes of the COVID-19 vaccine with the help of a knowledge graph, systematically and comprehensively reveals the research mainstay and distribution model of the COVID-19 vaccine from the national level, and provides rationalized suggestions for international cooperation in vaccine R&D in China. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.