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    [CS 298 Proposal]

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CS297 Proposal

Translating Natural Languages to SPARQL Query Language for RDF based Question Answering System

Shreya Satish Bhajikhaye (shreyasatish.bhajikhaye@sjsu.edu)

Advisor: Dr. Chris Pollett

Description:

RDF is a data framework used for managing linked resources and knowledge graphs. Sparql is a powerful language developed to query the continuously growing RDf data but using it requires an understanding of its syntax and semantics. This project aims to overcome this limitation by helping an average human user search for information using natural languages. The system will use word embeddings to map the sentence structures in the question to the terms in the underlying database. It will then build a query in sparqle according to the user's requirements.

Schedule:

Week 1: Sep 25 - Aug 31Discuss topics and draft proposal
Week 2: Sep 01 - Sep 07Finalize schedule and read about RDF and Sparql query language
Week 3: Sep 08 - Sep 14Work on deliverable 1 - Read relevant topics in [1]
Week 4: Sep 15 - Sep 21Deliverable 1 due - SPARQL queries
Week 5: Sep 22 - Sep 28Read [2] to understand concepts in Question Answering System. Select three types to to read in depth.
Week 6: Sep 29 - Oct 05Work on deliverable 2.
Week 7: Oct 06 - Oct 12Deliverable 2 due
Week 8: Oct 13 - Oct 19Read papers [3] and [4]
Week 9: Oct 20 - Oct 26Implement a simple neural network
Week 10: Oct 27 - Nov 02Work on deliverable 3
Week 11: Nov 03 - Nov 09Work on deliverable 3
Week 12: Nov 10 - Nov 16Deiverable 3 due.
Week 13: Nov 17 - Nov 23Work on Deliverable 4
Week 14: Nov 24 - Nov 30Deliverable 4 due. Start work on report
Week 15: Dec 01 - Dec 07First draft of report due. Revise draft of report
Week 16: Dec 08Deliverable 5 due - CS297 report

Deliverables:

The full project will be done when CS298 is completed. The following will be done by the end of CS297:

1. Implement five complex queries to Wikidata using SPARQL.

2. Read about three different Question Answering systems and implement some non-trivial aspects of one.

3. Build a neural network that maps sentence terms to subject, object and predicate.

4. Write a program that finds the entities in a sentence and locates the terms in the Wikidata items.

5. CS 297 report due.

References:

[1] DuCharme, B.(2013). Learning Sparql: Querying and Updating for SPARQL 1.1. O'Reilly

[2] Dimitrakis, E., Sgontzos, K. & Tzitzikas, Y. A survey on question answering systems over linked data and documents., J Intell Ins Syst 55, p233-259. (2020).

[3] Rusu, D., Dali, L., Fortuna, B., Grobelnik, M., & Mladeni, D. (2007). Triplet Extraction from Sentences.

[4] Liu, Y., Zhang, T., Liang, Z., Ji, H., & McGuinness, D. (2018). Seq2RDF: An End-to-end Application for Deriving Triples from Natural Language Text. ArXiv, abs/1807.01763.