I am seeking an expert in Computational Modelling within the domain of Social Sciences. Using SPARQL specifically, the focus of this project will involve querying and manipulating semantic data. While the type of databases or data sources is not yet defined, proficiency in working with RDF formats, Relational and NoSQL databases would be valuable. Ideal candidate should possess the following skills:
- Proficient knowledge in Sparql.
- Exceptional problem-solving ability.
- Strong background in Social Sciences.
- Experience working with different data sources.
Knowledge in Image Analysis and Natural Language Processing is also a huge plus.
The House of Commons SPARQL endpoint ([login to view URL]) provides access to
structured data about Members of Parliament (MPs) and questions that have been asked in the House
of Commons. Use this data together with data from Wikidata (where necessary) to answer the following
questions, presenting your findings in the form of a report of up to 3000 words, based on data about
questions asked by MPs between 1 January 2023 and 30 September 2023 inclusive:
[login to view URL] what extent do Members of Parliament (MPs) tend to ask questions that directly reference
their own constituency or a location in it? You should answer this question by identifying named
entities that refer to places or identifiable geographical features (e.g. “Dartford Crossing”,
“Reading Gaol”, etc.) in asked questions, and determining whether or not these are located in
the MP’s constituency using data from Wikidata. You are free to choose any reasonable method
in doing so – even if doing so will result in some false negatives – (e.g. relying on Wikidata
property P131 “located in the administrative territorial entity”), however you should estimate
how reliable you believe your chosen approach is. (N.B. it is not expected or required that your
approach will result in 100% accuracy.)
[login to view URL] applying LDA topic modeling and analyzing the results, what (if any) identifiable regional
differences are there in the types of questions asked – e.g. do MPs representing, say,
constituencies located in the North of England tend to ask more questions about certain topics
than those in Southeast England? In answering this question, you should start by aggregating
data into regions larger than an electoral district, such as those denoted by the property “region
of England” [login to view URL] (For simplicity, you may treat Scotland and
Northern Ireland as two separate regions without further subdivisions, or alternatively use any
reasonable administrative subdivisions for these regions as you see fit). Discuss the assumptions
and limitations of your approach and analysis.
The following SPARQL query can be used as a starting point:
SELECT *
WHERE {
?question <[login to view URL]> ?qnum .
?person <[login to view URL]> ?question .
?question <[login to view URL]> ?text .
?question <[login to view URL]> ?date .
FILTER (?date >= "2023-01-01+00:00"^^xsd:dateTime && ?date < "2023-10-01+00:00"^^xsd:dateTime)
}
This query returns the following data:
?question Entity representing a question
?person Entity representing the person who asked the question
?qnum Numerical identifier for this question
?text The text of the question
More details will be provided on request!
Hi,
I hope you are doing fine.
I have almost 10 years of experience in machine learning algorithms. I can implement various types of artificial intelligence algorithms including yours with Matlab, Python and etc. I have PhD from Tohoku University and have several journal publications on the subjects. You can see portfolio for my previous projects.
I read about your project and am interested in working with you. Please send me a message so that we can discuss more.
Best regards.
Hi,
I would like to grab this opportunity and will work till you get 100% satisfied with our work.
As an expert who have many years of experience on Java, Machine Learning (ML), Hadoop, Elasticsearch, Spark
Lets connect in chat so that We discuss further.
Regards
Hello, How are you?
I am a senior with extensive experience in Sparql and Computational Modeling within the domain of Social Sciences. I have a strong background working with different data sources and have successfully completed projects involving querying and manipulating semantic data. My solutions have been praised for their exceptional problem-solving ability and have brought significant insights to the table.
- Proficient in Sparql
- Strong background in Social Sciences
- Experience with different data sources
- Knowledge in Image Analysis and Natural Language Processing
- Proven track record of delivering successful projects
Best Regards, [Your Name]
With a solid background in Statistics and Data Science, particularly in Machine Learning (ML), the task you have described aligns perfectly with my area of expertise. I have a strong grasp of data manipulation techniques using SPARQL, which is fundamental for querying and handling semantic data as required for this project. As a problem-solving driven individual, I find it invigorating to overcome hurdles like identifying named entities that refer to places or identifiable geographical features, and I assure you that creativity and precision will be at the heart of my approach to this task.
Given my previous experience in working with a variety of data sources including RDF format, Relational, and NoSQL databases, I am confident in my ability to effectively utilize the House of Commons SPARQL endpoint together with Wikidata to obtain accurate results. My proficiency in Python and R will further enable me to conduct TDA topic modeling as recommended and analyze any identifiable regional differences in the types of questions asked, as well as discussing the assumptions and limitations of my approach and analysis in detail.