Property Graph Schema Optimization for Domain-Specific Knowledge Graphs (2003.11580v3)
Abstract: Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. The data in these knowledge graphs can be described using ontologies, which provide a semantic abstraction to define the content in terms of the entities and the relationships of the domain. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Although there has been a lot of effort to build systems that enable efficient querying over knowledge graphs, the problem of schema optimization for query performance has been largely ignored in the graph setting. In this work, we show that graph schema design has significant impact on query performance, and then propose optimization algorithms that exploit the opportunities from the domain ontology to generate efficient property graph schemas. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. We conduct empirical evaluations with two real-world knowledge graphs from medical and financial domains. The results show that the schemas produced by the optimization algorithms achieve up to 2 orders of magnitude speed-up compared to the baseline approach.
Summary
- The paper proposes a methodology that optimizes property graph schemas using ontological rules such as union and inheritance to reduce query traversals.
- It leverages empirical evaluations from medical and financial datasets to demonstrate significant query speed-ups and performance improvements.
- The work highlights practical schema optimization techniques—such as merging nodes and copying properties—to enhance real-world graph database performance.
Property Graph Schema Optimization for Domain-Specific Knowledge Graphs
In the paper "Property Graph Schema Optimization for Domain-Specific Knowledge Graphs," the authors introduce a methodology for optimizing property graph schemas to enhance query performance in domain-specific knowledge graphs. Utilizing ontologies, the paper proposes several optimization strategies that leverage the semantic richness of the relationships described in an ontology. This essay covers the implementation and practical application details of the paper without sensational language and provides visual references to support the concepts.
Introduction to Domain-Specific Knowledge Graphs
Domain-specific knowledge graphs integrate and manage data within specific enterprise applications, such as medical or financial domains, by utilizing ontologies. Ontologies serve as a semantic framework that classifies domain entities and relationships, allowing for improved query efficiency by reducing edge traversals. However, existing systems have largely overlooked schema optimization for property graphs, which can significantly impact query performance.
Figure 1: Motivating Example.
Approach for Property Graph Schema Optimization
The paper proposes several rules to derive optimized property graph schemas from ontologies:
Union Rule
Within a union relationship, member concepts are directly connected to all concepts connected to the union concept. This rule aims to avoid unnecessary edge traversals through intermediate nodes.
Figure 2: Union Relationship.
Inheritance Rule
The inheritance rule optimizes queries by copying properties either from a parent concept to child concepts or vice versa, according to the Jaccard similarity between their properties. This reduces traversals between parent and child nodes.
Figure 3: Inheritance Relationship.
One-to-One, One-to-Many, and Many-to-Many Rules
- One-to-One Rule: Merges two related concepts into one node to reduce join-like traversals.
- One-to-Many Rule: Copies properties from the "many" side to the "one" side as a list for better aggregation performance.
- Many-to-Many Rule: Symmetrically applies the one-to-many strategy in both directions.
Experimentation and Findings
The empirical evaluations use knowledge graphs from the medical and financial sectors. The optimized schemas presented profound reductions in query times, often achieving orders of magnitude speed-ups compared to baseline schemas created via direct ontology mapping.
Figure 4: Total Query Latency (MED and FIN).
Microbenchmark Analysis
The microbenchmark focuses on several graph primitives such as pattern matching, property lookups, and aggregation functions. Across all tested primitives, property graphs instantiated from the optimized schemas show significant reductions in edge traversals, resulting in faster query execution.
Workload Performance
Using workloads mirroring real-world access patterns in terms of uniform and Zipf distributions, the authors show that the schema optimization remains effective and robust under variable conditions.
Conclusion
The algorithms and rules offered in this work enable the translation of domain ontologies into efficient property graph schemas. These optimized schemas significantly enhance query performance, providing a valuable framework for practitioners building knowledge-intensive applications across various domains.
The work aligns with the growing demand for optimized data structures in graph databases and paves the way for future research into automated and adaptive schema optimization methods. The proposed solution proves applicable across existing property graph systems, making it a versatile tool to significantly boost performance in graph-based data management solutions.
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What this paper is about (in simple terms)
This paper is about making “knowledge graphs” much faster to search. A knowledge graph is like a big map of facts: circles (called nodes) are things like drugs or diseases, and arrows (called edges) show how they are connected, like “treats” or “causes.” The authors show that how you design the “blueprint” for this graph (the schema) can make searches way faster, and they present smart ways to redesign that blueprint using the rules of the domain (an ontology) as a guide.
The main questions the paper asks
- Does the way we design the graph’s blueprint (schema) change how fast we can answer questions?
- How can we use the domain’s ontology (the official list of types of things and how they relate) to design a better schema?
- Can we reduce the number of “hops” through the graph (like taking shortcuts) to speed up queries?
- How do we balance speed gains with extra memory cost when we copy or combine data?
- Can we build practical algorithms that pick the best schema automatically, and do they work on real medical and financial data?
How they approached the problem
Think of a city map:
- Places are nodes (e.g., Drug, Disease).
- Roads connecting places are edges (e.g., “Drug treats Disease”).
- A “schema” is the plan that says which kinds of places exist and how they can connect.
- A “query” is a trip plan, like “find all foods that interact with this drug.”
The time it takes to answer a query often depends on how many roads you must travel (edge traversals). Fewer roads = faster answers. The authors design rules that reorganize the map to add shortcuts or combine places—without changing the meaning of the information.
They propose five simple, powerful rules:
- Union rule: If there’s a “group” node that only represents “members” (like a hub that just gathers two types), connect the members directly to neighbors to skip the hub. It’s like closing a central roundabout and adding direct streets.
- Inheritance rule: For parent/child types (like Vehicle → Car), sometimes copy shared info down or up so you don’t need to take an extra hop to the parent or child. Like labeling both “Vehicle” and “Car” parking spots with the same rules when it helps.
- One-to-one rule: If two types always match one-to-one, merge them into a single node type. Like combining two rooms that always go together into one room.
- One-to-many rule: If one thing connects to many other things (like a Drug → many Indications), copy key lists (like a list of indications) into the “one” side so counting or checking them is instant, with no travel. Like keeping a checklist on the fridge instead of walking to every room.
- Many-to-many rule: Do the same “list-copying” in both directions when two types can connect to many of each other.
These tricks reduce hops and speed up queries. The trade-off: copying info takes more space. So the authors add planners that decide when the speedup is worth the space.
Two smart planners help pick the best schema under a memory budget:
- Concept-centric planner: Finds the most “important” types (concepts) first and optimizes around them. “Important” is measured with a popularity score like PageRank (similar to how Google ranks pages), adjusted for how often those types are used and how big they are. It then applies the rules until the memory budget is used up.
- Relation-centric planner: Scores each connection (relationship) by its benefit (how much it speeds common queries) versus its cost (extra memory). It then picks the best set of changes that fit the memory budget.
Both planners can use:
- Data stats: how many items of each type, how big properties are, how many links exist.
- Workload hints: which types and links people query most often.
What they found and why it matters
- Fewer hops make a huge difference. In one medical example, a pattern-matching query dropped from about 3245 ms to 23 ms—around 100 times faster—by removing an unnecessary middle node.
- Aggregation got faster too. Counting related items (like all indications for a drug) ran about 8 times faster by storing a ready-made list instead of walking to every connected node.
- These ideas worked on real medical and financial knowledge graphs, not just toy examples.
- Their approach is, to their knowledge, the first to use ontologies to automatically optimize property graph schemas.
- Importantly, they keep the meaning intact while changing the “shape” of the graph to match how it’s used.
What this could change in the real world
- Faster answers for apps like medical decision support, fraud detection, and customer service, where speed means better outcomes and experiences.
- Lower compute costs because queries do less work.
- A new mindset: schema design for graphs really matters, just like indexing does for databases.
- A practical toolkit: simple, explainable rules plus planners that respect a memory budget.
- Future directions: handle more complex ontology features, automatically adapt as data and workloads change, and plug into different graph systems.
In short: The paper shows that using domain knowledge to redesign the graph’s blueprint—by adding smart shortcuts and combining the right pieces—can make queries dramatically faster, often with small and controlled increases in storage.
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