NALMO: A Natural Language Interface for Moving Objects Databases

Xieyang Wang, jianqiu Xu, Hua Lu

Abstract:

Moving objects databases (MODs) have been extensively studied
due to their wide variety of applications including traffic management,
tourist service and mobile commerce. However, queries in
natural languages are still not supported in MODs. Since most users
are not familiar with structured query languages, it is essentially
important to bridge the gap between natural languages and the
underlying MODs system commands. Motivated by this, we design
a natural language interface for moving objects, named NALMO.
In general, we use semantic parsing in combination with a location
knowledge base and domain-specific rules to interpret natural language
queries. We design a corpus of moving objects queries for
model training, which is later used to determine the query type.
Extracted entities from parsing are mapped through deterministic
rules to perform query composition. NALMO is able to well translate
moving objects queries into structured (executable) languages.
We support four kinds of queries including time interval queries,
range queries, nearest neighbor queries and trajectory similarity
queries. We develop the system in a prototype system SECONDO
and evaluate our approach using 240 natural language queries extracted
from popular conference and journal papers in the domain of
moving objects. Experimental results show that (i) NALMO achieves
accuracy and precision 98.1% and 88.1%, respectively, and (ii) the
average time cost of translating a query is 1.47s.