GitHub - dwaynee174/Search-Engine-Using-SPARQL-for-LAPD-Crime-Ontology: A search system which aides a Los Angeles Police Department (LAPD) Sergeant/ Officer to get to case specifics in less than 4 secs per case by analyzing RDF dataset from data.gov to semantically search and optimize crime and collision raw data ontology.

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A search system which aides a Los Angeles Police Department (LAPD) Sergeant/ Officer to get to case specifics in less than 4 secs per case by analyzing RDF dataset from data.gov to semantically search and optimize crime and collision raw data ontology.

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LAPD_Crime_and_Collision_Raw_Data_for_2015.csv		LAPD_Crime_and_Collision_Raw_Data_for_2015.csv
ReadMe.txt		ReadMe.txt

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Abstract
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Often extensive amount of raw data exists in the real world. It is quite cumbersome to analyze such 
huge amount of data by just a mere glance and even if sufficient time is spent understanding the same, 
it may not always produce semantically correct results. We have selected LAPD Crime and Collision raw data
for 2015, which gives us raw data is RDF. Hence, in our study we implement a search system using SPARQL
Protocol and RDF Query Language which is both a query language and a data access protocol used to
draw out semantic information from RDF[1]. Our system provides a user Interface wherein the 
LAPD Official can type in search string and the backbone JENA API helps to fetch and display 
information to the Official. Deriving semantically justified information plays a major role in 
this context as it helps the LAPD officials to understand and work with their ontologies effectively 
and particularly use the information to the optimum scale. In this paper, we try to understand our RDF
data and its complexity, the goals of our system, how they are achieved and the challenges met.

Keywords:  SPARQL Protocol and RDF Query Language (SPARQL), Jena API, RDF, LAPD, Protege, Ontology