The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing
In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe that the query vocabulary is distinct from human vocabulary. Language Models (LMs) are pre-dominantly trained for human language tasks, and hence, if the query vocabulary is replaced with a vocabulary more attuned to the LM tokenizer, the performance of models may improve. We carry out carefully selected vocabulary substitutions on the queries and find absolute gains in the range of 17% on the GrailQA dataset.
Code (1)
Tasks
Graph Question AnsweringQuestion AnsweringSemantic ParsingSimilar Papers 제목 키워드 기반
Modern Baselines for SPARQL Semantic Parsing
In this work, we focus on the task of generating SPARQL queries from natural language questions, which can then be executed on Knowledge Graphs (KGs). We assume that gold entity and relations have been provided, and the …
Knowledge GraphsSemantic ParsingA Neural Approach to KGQA via SPARQL Silhouette Generation
Semantic parsing is a predominant approach to solve the Knowledge Graph Question Answering (KGQA) task where, natural language question is translated into a logic form such as SPARQL. Semantic parsing based …
Graph Question AnsweringMachine TranslationNMTQuestion Answering+3Enhancing SPARQL Generation by Triplet-order-sensitive Pre-training
Semantic parsing that translates natural language queries to SPARQL is of great importance for Knowledge Graph Question Answering (KGQA) systems. Although pre-trained language models like T5 have achieved significant suc…
Graph Question AnsweringLanguage ModelingLanguage ModellingMasked Language Modeling+5Semantic Parsing Natural Language into SPARQL: Improving Target Language Representation with Neural Attention
Semantic parsing is the process of mapping a natural language sentence into a formal representation of its meaning. In this work we use the neural network approach to transform natural language sentence into a query to a…
Semantic ParsingSentenceGeoSPARQL+: Syntax, Semantics and System for Integrated Querying of Graph, Raster and Vector Data -- Technical Report
We introduce an approach to semantically represent and query raster data in a Semantic Web graph. We extend the GeoSPARQL vocabulary and query language to support raster data as a new type of geospatial data. We define n…