Python代写 | CS551J Research Project/Scientific Report

本次Python代写是抓取与COVID-19疫苗相关的资源并分析数据

CS551J Research Project/Scientific Report

Assessment Tasks & Report Guidance
Your report must conform to the below structure and include the required content as outlined in each
section. Each subtask has its own marks allocated. You must supply a written report, along with the
corresponding code, containing all distinct sections/subtasks that provide a full critical and reflective
account of the processes undertaken.
Overview
Knowledge Graph (KG) is a powerful knowledge representation and reasoning tool for linking
structured and unstructured data together. For example, semantic knowledge graph combines
structured and unstructured information on the Web so that computer systems can understand text.
Consequently, knowledge graph enables search techniques and question answering. The aims of this
assessment are to understanding knowledge representation and reasoning using KG, creating and
encoding a text knowledge graph, and knowledge graph analysis. In this assessment, you will
particularly focus on the Coronavirus (COVID-19) vaccines; for COVID-19 vaccine information,
please refer to the NHS site “https://www.nhs.uk/conditions/coronavirus-covid-19/coronavirusvaccination/coronavirus-vaccine/”. Following these tasks, you will create a COVID-19 vaccine
knowledge graph (COVID-VKG) from scratch and perform a basic analysis. Note that in your report,
for each of the following tasks you must demonstrate the process how it was performed by
providing graphs and code snippets and provide analysis if necessary.
Tasks:
1. Collect resources from Web articles/texts related to COVID-19 vaccines. You can use
existing software such as Python tools to crawl Web pages related to this topic. (10%)
2. Extract texts from the crawled web pages (5%) and extract sentences relevant to COVID-19
vaccines from these texts; you may need to remove the noise sentences. (5%)
3. Given the extracted sentences, write a programme to extract the triples required for creating
COVID-VKG. These triples can be represented as structured data which include not only the
triples, but also the resources and the sentences. (20%)
4. As the data have been represented as structured data, use the principles of Resource
Description Framework (RDF) to create the COVID-19 vaccine knowledge graph. Note that
the entities extracted from text should be mapped to a vacabulary in a known ontology such
as Dbpedia. If an extracted entity can be mapped to an identical entity in any known ontology,
the uniform resource identifier (URI) should be used as a representative for the extracted
entity; otherwise, a new URL is given to the entity. Represent the COVID-VKG by using NTriples and Turtle serialisation methods. You should explicitly show some examples of your
COVID-VKG in your report and describe how you have created the KG. (40%)
5. After the COVID-VKG has been created, use querying tools to extract knowledge from the
graph. For example, this can be used for question answering to answer the following
questions: What are the approved vaccines and thus are being used given a country? What
are the side effects for a vaccine? You should explicitly explain how your programme
answers these questions and the outcome of your programmes. (20%)


程序代写代做C/C++/JAVA/安卓/PYTHON/留学生/PHP/APP开发/MATLAB


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