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COMPSCI 752 Big Data Management Assignment 3
1 Querying data through RDFS [1.5 marks]
Suppose that our Tbox T and Abox A are defined as follows:
Tbox T:
|
RegisteredIn RegisteredIn HasPrograms Design LedBy LedBy Abox A: |
rdfs : domain rdfs : range rdfs : range rdfs : subPropertyOf rdfs : domain rdfs : range |
Design(Stats, DataScience)
Design(CS, InfoSys)
RegisteredIn(Alice, DataScience)
RegisteredIn(Peter, InfoSys)
RegisteredIn(Mary, DataScience)
LedBy(CS, Giovanni)
We consider the following conjunctive query:
HasProgram
Dept
Professor
q(x) : − Student(x), RegisteredIn(x, y), HasPrograms(z, y), Dept(z)
Questions:
1. What is the answer of q(x) when evaluated on only Abox A? Explain the answer. [0.5 marks]
2. What is the answer of q(x) when evaluated on both Tbox and Abox < T, A >?
Explain the answer.
[1 mark]
2 Knowledge graph [3.5 marks]
We will build a knowledge graph based on the profile text from Ninh’s homepage.
"Prior to joining University of Auckland in December 2018, Ninh worked in Copenhagen for 7 years at the University of Copenhagen and IT University of Copenhagen. He received his PhD at IT University of Copenhagen under thesupervision of Professor Rasmus Pagh in 2014. After that, he spent 4 years in postdoctoral positions in Copenhagen. He was the recipient of the best paper awards in WWW Conference 2014 and PKDD 2020. AMiner has recognized him as the 2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining (Rising Star) for his outstanding and vibrant contributions to this field between 2012 and 2021."
1. Unsupervised method: Assume that nouns will be entities, and verbs form relations. Using NLP techniques (e.g. nltk packages), write a small Python script to parse the above text into entities and relationships. [1 mark]
Construct a knowledge graph based on the parsing result. [0.5 marks]
2. Supervised method: Using a pre-trained model (e.g. https://spacy.io/models/ en) to parse the above text for entities and verbs. [1 mark]
Assume that verbs form relations, construct a knowledge graph based on the parsing result. [0.5 marks]
3. If we use some specific nouns as verbs, e.g. supervision, award, contribution, how do the constructed knowledge graphs above change? [0.5 marks]