Translating Place-Related Questions to GeoSPARQL Queries

Abstract

Many place-related questions can only be answered by complex spatial reasoning, a task poorly supported by factoid question retrieval. Such reasoning using combinations of spatial and non-spatial criteria pertinent to place-related questions is increasingly possible on linked data knowledge bases. Yet, to enable question answering based on linked knowledge bases, natural language questions must first be re-formulated as formal queries. Here, we first present an enhanced version of YAGO2geo, the geospatially-enabled variant of the YAGO2 knowledge base, by linking and adding more than one million places from OpenStreetMap data to YAGO2. We then propose a novel approach to translate the place-related questions into logical representations, theoretically grounded in the core concepts of spatial information. Next, we use a dynamic template-based approach to generate fully executable GeoSPARQL queries from the logical representations. We test our approach using the Geospatial Gold Standard dataset and report substantial improvements over existing methods.

Geographic Knowledge Base Question Answering over OpenStreetMap

“…GeoKBQA has recently attracted increased interest from researchers due to its ability to handle place-related questions that necessitate sophisticated spatial reasoning, an aspect not thoroughly supported by conventional factoid question retrieval methods [12]. Recent advancements in GeoKBQA have predominantly utilized the GeoQuestions201 dataset for their research.…”

“…Ref. [12] employed a rule-based query generator, utilizing neural-based constituency and dependency parsing methods to discern the structure of input questions and the relations among their tokens. They incorporated neural networks to determine each encoding in the parsed tree.…”

“…Structured data stored in KBs are more suitable than unstructured text retrieval to answer such questions. Using structured data, geographic question answering (GeoQA) systems can perform spatial (i.e., 200 m radius) and nonspatial operations (i.e., count how many), and retrieve information based on specific criteria (e.g., High Street in Oxford) [12].…”

“…Research in geographic knowledge base question answering (GeoKBQA) has been limited, with only a handful of studies addressing this area [12][13][14]. Ref.…”

“…Ref. [12] employed deep neural networks (DNNs), such as BERT, to transform natural language questions into sequences of encodings, using a template-based approach for converting these sequences into GeoSPARQL queries. Further contributing to the field, ref.…”