Improving SPARQL Query Performance Through Vocabulary Changes

In this study, we examine how the words used in output affect the performance of models that turn natural language questions into SPARQL queries. The goal is to answer questions using information from a knowledge graph. This means converting everyday language into specific queries that computers can use to find the answers.

#What is SPARQL?

SPARQL is a query language that lets people ask questions about data stored in a knowledge graph. A knowledge graph is a collection of information made up of things and the relationships between them. For example, if someone asks, "What is the capital of France?" the system needs to understand the question and turn it into a SPARQL query that can fetch the answer from the knowledge graph.

#How Does Semantic Parsing Work?

The process of converting a natural language question into a SPARQL query involves several steps:

1. Entity Linking: The system identifies key objects in the question and connects them to the knowledge graph.

2. Relation Linking: Next, it determines the relationships between these objects and links them to the knowledge graph.

3. Query Formation: Finally, the system creates a SPARQL query using the identified entities and relationships. This query is then used to get the answer from the knowledge graph.

#Focus of the Study

In this study, we concentrate on the part where the SPARQL query is built. Previous work has shown that making small swaps in the vocabulary can lead to better results. Here, we take this idea further by changing the entire vocabulary used in SPARQL queries.

Some special characters in SPARQL can cause issues for models, so we replace these with more standard text identifiers. This altered version of the query is what we call a "masked query."

#Experiment Setup

We conducted experiments using two versions of a model called T5, which is commonly used for language tasks. The models were trained on a dataset called GrailQA that includes questions and their corresponding SPARQL queries.

#Different Vocabulary Types

We looked at several types of vocabulary replacements:

• Original: This keeps the standard SPARQL vocabulary unchanged.

• Dictionary: Here, we swap SPARQL keywords with common English words. For instance, the word "SELECT" might be replaced with "DOG."

• Character Substitution: In various character substitution methods, SPARQL keywords are replaced with single letters, numbers, or combinations of letters and numbers. For example, "SELECT" could be turned into "A" or "ATYZGFSD".

#Findings on Vocabulary Impact

Our results show that models perform better when we use substituted vocabularies compared to the original SPARQL vocabulary. As we increased the complexity of the character-based vocabularies, performance decreased, especially in the more complex settings.

#Analyzing Performance

We tracked how well the models matched their generated queries against the correct answers. The accuracy was highest with substituted vocabularies compared to the original vocabulary.

Interestingly, performance varied between different models. The smaller T5 model was more affected by vocabulary changes than the larger one.

#Importance of Token Familiarity

It appears that the model's familiarity with different types of tokens plays a role. Simple characters may be recognized more readily than specific SPARQL terms. This is likely because the model has encountered simpler tokens more often during its initial training stages.

#Error Analysis

We analyzed mistakes made in the output. Many errors arose from non-standard characters remaining in the queries, which the model struggled to handle.

We noticed that replacing problematic characters can significantly increase the model's ability to produce correct outputs. When examining errors from different substituted vocabularies, we found that simpler substitutions led to fewer syntax errors.

#Conclusion and Future Directions

Our findings suggest that using a modified vocabulary can lead to better performance in semantic parsing tasks, even with smaller models. This could also help save energy and resources in the long run.

For future research, it would be beneficial to look deeper into how different vocabulary styles affect model performance. We also want to explore how attention maps-the ways models focus on different parts of the input-can shed light on this topic.

Furthermore, examining models with different training settings and data sizes could yield additional insights. There's a clear opportunity to refine methods for improving semantic parsing through vocabulary adjustments, and we aim to continue this exploration in future work.

By replacing the original SPARQL vocabulary with words that the model is more familiar with, we see that the model can more effectively translate natural language questions into machine-readable queries.