Question Answering Systems for Covid-19

In the present scenario COVID-19 pandemic has ruined the entire world. This situation motivates the researchers to resolve the query raised by the people around the world in an efficient manner. However, less number of resources available in order to gain the information and knowledge about COVID-19 arises a need to evaluate the existing Question Answering (QA) systems on COVID-19. In this paper, we compare the various QA systems available in order to answer the questions raised by the people like doctors, medical researchers etc. related to corona virus. QA systems process the queries submitted in natural language to find the best relevant answer among all the candidate answers for the COVID-19 related questions. These systems utilize the text mining and information retrieval on COVID-19 literature. This paper describes the survey of QA systems-CovidQA, CAiRE (Center for Artificial Intelligence Research)-COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) available for COVID-19. All these QA systems are also compared in terms of their significant parameters-like Precision at rank 1 (P@1), Recall at rank 3(R@3), Mean Reciprocal Rank(MRR), F1-Score, Exact Match(EM), Mean Average Precision, Score metric etc.; on which efficiency of these systems relies.

Dug: A Semantic Search Engine Leveraging Peer-Reviewed Literature to Span Biomedical Data Repositories

As the number of public data resources continues to proliferate, identifying relevant datasets across heterogenous repositories is becoming critical to answering scientific questions. To help researchers navigate this data landscape, we developed Dug: a semantic search tool for biomedical datasets that utilizes evidence-based relationships from curated knowledge graphs to find relevant datasets and explain why those results are returned. Developed through the National Heart, Lung, and Blood Institute's (NHLBI) BioData Catalyst ecosystem, Dug can index more than 15,911 study variables from public datasets in just over 39 minutes. On a manually curated search dataset, Dug's mean recall (total relevant results/total results) of 0.79 outperformed default Elasticsearch's mean recall of 0.76. When using synonyms or related concepts as search queries, Dug's (0.28) far outperforms Elasticsearch (0.1) in terms of mean recall. Dug is freely available at https://github.com/helxplatform/dug, and an example Dug deployment is also available for use at https://helx.renci.org/ui.

Fenix: A Semantic Search Engine Based on an Ontology and a Model Trained with Machine Learning to Support Research

This paper presents the final results of the research project that aimed to build a Semantic Search Engine that uses an Ontology and a model trained with Machine Learning to support the semantic search of research projects of the System of Research from the University of Nariño. For the construction of FENIX, as this Engine is called, it was used a methodology that includes the stages: appropriation of knowledge, installation and configuration of tools, libraries and technologies, collection, extraction and preparation of research projects, design and development of the Semantic Search Engine. The main results of the work were three: a) the complete construction of the Ontology with classes, object properties (predicates), data properties (attributes) and individuals (instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the successful training of the model for which Machine Learning algorithms and specifically Natural Language Processing algorithms were used such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also done in Jupyter Notebook with Python within the virtual environment of anaconda and with Elasticsearch; and c) the creation of FENIX managing and unifying the queries for the Ontology and for the Machine Learning model. The tests showed that FENIX was successful in all the searches that were carried out because its results were satisfactory.

COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

The Technique of Different Semantic Search Engines

Semantic Search is a search technique that improves looking precision through perception the reason of the search and the contextual magnitude of phrases as they show up in the searchable statistics space, whether or not on the net to generate greater applicable result. We spotlight right here about Semantic Search, Semantic Web and talk about about exceptional kind of Semantic search engine and variations between key-word base search and Semantic Search and the benefit of Semantic Search. We additionally provide a short overview of the records of semantic search and its function scope in the world.