Querying semantic data with natural language, 2015

Item — Call Number: MU Thesis Kir

Identifier: b7636895

Staff Only

Scope and Contents

From the Collection:

The collection consists of theses written by students enrolled in the Monmouth University graduate Software Engineering program. The holdings are bound print documents that were submitted in partial fulfillment of requirements for the Master of Science degree.

Dates

Creation: 2015

Creator

Kirstein, Ernest (1990- ) (Author, Person)
Rosca, Daniela (Thesis advisor, Person)
Wang, Jiacun, 1963- (Thesis advisor, Person)

Conditions Governing Access

The collection is open for research use. Access is by appointment only.

Access to the collection is confined to the Monmouth University Library and is subject to patron policies approved by the Monmouth University Library.

Collection holdings may not be borrowed through interlibrary loan.

Research appointments are scheduled by the Monmouth University Library Archives Collections Manager (723-923-4526). A minimum of three days advance notice is required to arrange a research appointment for access to the collection.

Patrons must complete a Researcher Registration Form and provide appropriate identification to gain access to the collection holdings. Copies of these documents will be kept on file at the Monmouth University Library.

Extent

1 Items (print book) : 46 pages ; 8.5 x 11.0 inches (28 cm).

Language of Materials

English

Additional Description

Abstract

Searching through semantic data using SPARQL requires expert knowledge and thus remains out of reach for casual users. In this work, we explore the development of natural language interfaces for semantic data. This work looks at a number of approaches towards this goal as well as providing a new approach. Our system works like a natural language compiler and handles a limited set of natural language questions which, though narrow in scope, have complex semantics that other systems would be unable to handle.

Partial Contents

1. Introduction -- 2. Previous work -- 3. Converting natural language questions into SPARQL queries -- 4. Software architecture -- 5. Resolving user input names to RDF entities -- 6. Top-down parsing -- 7. Future work -- 8. Conclusion.