Bridging the Gap Between Database and Semantic Web Communities

Over the last ten years, Knowledge Graphs (KGs) have become very popular in both industries and schools. This interest is mainly driven by two groups: the Database community and the Semantic Web community. Both sides have different languages and methods for working with KGs. The Database community often uses SQL and Datalog, while the Semantic Web community uses SPARQL and OWL. This difference has created a gap between both groups, making it hard to connect the technologies they use.

The aim of this work is to create a system that addresses the needs of both communities. The system will combine features from both sides to improve the use of KGs in various applications.

#Background

Knowledge Graphs organize information in a way that allows machines to understand and process it better. They use entities (like people, places, and things) and relationships between them to create a web of knowledge. Researchers have been looking into how to manage and query these graphs effectively.

Typically, the Database community focuses on efficiently processing large amounts of data. They want powerful languages that can handle complex queries. On the other hand, the Semantic Web community is interested in standards set by the World Wide Web Consortium (W3C) and how to represent knowledge in a machine-readable format. This group focuses on ensuring that data can interoperate across different systems.

#Challenges Faced

One major challenge is that both communities have different requirements for their systems. The Semantic Web community needs to comply with existing W3C standards and use SPARQL features effectively. For them, the ability to handle existential quantification is critical. The Database community, however, puts more weight on the expressive power of query languages to deal with complex business applications.

Another issue arises because there hasn't been a fully implemented system that meets the requirements of both communities. While some attempts have been made to bridge this gap, many existing approaches only work for one community or the other. This means that important needs and functionalities are often missed.

#Proposed Solution

To address these challenges, this work proposes a system that combines the strengths of both communities. The proposed system includes several features:

• Theoretical Translation: The system offers a complete framework for integrating SPARQL support into a Knowledge Graph language. This integration takes into account important requirements from both communities.
• Translation Engine: The system includes a translation engine which processes SPARQL 1.1 functionalities. It fills gaps between theory and practical system development, making the engine robust and user-friendly.
• Experimental Evaluation: The system has undergone extensive testing against other systems to measure compliance with SPARQL standards and compare performance.

#System Overview

The system is built on the Vadalog platform, which allows for the implementation of Datalog rules. It is designed to manage queries that are both expressive and efficient. This section will outline the major components and how they operate seamlessly within the framework.

#1. Theoretical Framework

The theoretical foundation of the system provides a uniform way to transform SPARQL queries into Datalog rules. The framework is built upon established semantics for both languages, ensuring that the transformation is accurate and reliable.

#2. Translation Engine

The translation engine is the core of the system. It allows for the conversion of SPARQL queries into Datalog. The engine includes:

• Data Translation Method: This generates Datalog rules from an RDF Dataset.
• Query Translation Method: This produces Datalog rules from a SPARQL query.
• Solution Translation Method: This translates Datalog solutions back into SPARQL results.

These components work together to ensure that users can input SPARQL queries and receive correct results in Datalog format.

#3. Effectiveness and Performance

The system has been evaluated using various benchmarks to measure its performance against other well-known systems. The evaluation covers:

• Compliance with SPARQL Standards: The system has been tested to confirm that it meets all relevant standards outlined by the W3C.
• Performance Comparisons: The results show that the proposed system performs comparably to other leading SPARQL engines, excelling particularly in complex query scenarios.

#Technical Details

In order to understand how the system operates, we need to dive deeper into the technical aspects that allow for seamless integration of SPARQL and Datalog.

#Data Representation

Knowledge Graphs are represented using RDF triples, which consist of a subject, predicate, and object. The system translates these triples into Datalog facts, allowing for efficient querying and reasoning.

#Query Processing

When a user submits a SPARQL query, the translation engine processes the query step-by-step. This detailed processing ensures that all aspects of the SPARQL syntax are handled correctly, including:

• Triple Patterns
• FILTER conditions
• OPTIONAL patterns
• UNION operations
• Property paths

Each of these elements is carefully converted into Datalog rules, maintaining the intended meaning and functionality of the original query.

#Handling Complex Queries

The ability to efficiently handle complex queries, such as those involving property paths, is one of the standout features of the proposed system. The system has specialized mechanisms in place to facilitate the evaluation of these complex queries, which often require recursive reasoning.

#Experimental Evaluation

The experimental section outlines how the system has been tested to ensure its robustness and compliance with the SPARQL standard. Various benchmarks and performance metrics have been employed to achieve this goal.

#Benchmark Selection

The benchmarks used in the evaluation include:

• FEASIBLE Benchmark: This benchmark provides a diverse set of queries that cover a wide range of SPARQL features.
• SP2Bench: A widely recognized benchmark designed to test scalability and execution performance.
• BeSEPPI: Focused on property path queries, this benchmark evaluates how well a system can handle complex relationships.

#Results of the Evaluation

The results from the benchmarks indicate that the proposed system not only complies with the SPARQL standard but also shows competitive performance compared to other leading systems.

• The system correctly processes all accepted queries from the FEASIBLE benchmark, achieving a high degree of correctness and completeness.
• In terms of performance, the system demonstrates faster query execution times, particularly for complex property path queries.

#Conclusion and Future Work

The proposed system represents a significant step forward in bridging the gap between the Database and Semantic Web communities. By providing a unified framework for processing SPARQL queries and converting them into Datalog, the system enables more efficient and effective management of Knowledge Graphs.

Future work will focus on:

• Expanding Coverage: Increasing the range of supported SPARQL features to cover all aspects of the standard.
• Performance Optimization: Investigating ways to optimize query execution further, particularly for complex queries.
• Unified Benchmark Development: Creating comprehensive benchmarks that incorporate all SPARQL features to facilitate future evaluations.

This work opens up new possibilities for integrating KGs into various applications, making it easier for users to leverage the power of both the Semantic Web and traditional database technologies.