RDF Knowledge Graphs Similarity Search

RDF Knowledge Graphs and Semantic SPARQL Similarity Search

RDF knowledge graphs have become increasingly popular, but users face challenges in querying them due to the schema-free nature of RDF data. This schema-free nature allows for diverse graph representations, making it difficult to formulate complex SPARQL expressions.

traditional methods require users to have full knowledge of the underlying schema, which can be challenging, especially for open-domain knowledge graphs like DBpedia.

Motivating Example

A motivating example is given to find cars produced in Germany, which requires considering multiple UNION operators and different structural patterns. The example illustrates the difficulties of formulating complex SPARQL queries that conform to the syntax and consider the flexible underlying schemas.

Challenges and Contributions

Challenge 1: Mining diverse structural patterns with equivalent semantic meanings, which is different from schema mapping and ontology alignment. This challenge requires a deep understanding of the lexical, semantic, and structural relationships between concepts in the graph.

Challenge 2: Measuring semantic similarity in a uniform manner, as traditional graph similarity metrics only consider graph structures without considering semantic meanings. This challenge requires a thorough understanding of the semantics of the concepts and the relationships between them.

Limitations of Existing Approaches

Existing approaches to graph similarity search focus on structure similarity, but do not support vertex/edge label substitution or consider semantic similarity. This lack of support for semantic similarity makes it difficult to accurately measure the similarity between graphs with different representations of the same information.

Proposed Solution

An instance-driven approach is proposed to mine semantically equivalent patterns, including concept generation, edge redirection, and inductive inference. This approach is designed to address the limitations of existing approaches and provide a more accurate measure of semantic similarity.

A novel similarity measure, called semantic graph edit distance, is proposed to integrate traditional graph structure similarity, concept-level similarity, and diverse semantically equivalent structure patterns. This measure is designed to provide a more accurate and comprehensive measure of semantic similarity.

RDF Knowledge Graphs and Semantic Graph Patterns

A knowledge graph is a directed graph G = (V, E, L), where V denotes a set of vertices (entities, concepts, and literals), E denotes the set of edges, and L denotes the set of labels. Each entity in a knowledge graph is associated with a type. The type ontology graph is a directed acyclic graph describing the relations among types/predicates.

Instance-Driven Mining of Semantic Graph Patterns

The goal is to identify semantic graph patterns based on meaning-equivalent instances. The task is to mine the corresponding semantic graph patterns based on a family of sets of meaning-equivalent instances.

Two algorithms are devised to deal with single entities and entity pairs. The algorithm finds the type of each entity, and then adds the pattern (∗, type, tk) to the semantic graph pattern Pi.

Single Entity-Based Semantic Graph Patterns

Given a set of meaning-equivalent instances, the task is to mine the corresponding semantic graph patterns. The algorithm finds the type of each entity, and then adds the pattern (∗, type, tk) to the semantic graph pattern Pi.

Entity Pairs-Based Semantic Graph Patterns

Given a set of meaning-equivalent entity pairs, the task is to mine the corresponding semantic graph patterns. The algorithm enumerates all simple paths between the entities in each pair, and then replaces each entity with its corresponding type to obtain the type path.

Representative Semantic Graph Patterns

There are three types of representative semantic graph patterns: Concept Generalization, Edge Redirection, and Inductive Inference.

Problem Formalization

The problem is to formalize the semantic graph edit distance and the problem studied in this paper. The semantic graph edit distance is a novel definition that considers the graph structure similarity, concept-level similarity, and diverse semantic-equivalent structure patterns.

Semantic Graph Edit Operations

There are nine primitive semantic graph edit operations: Semantic Vertex Insertion, Semantic Vertex Deletion, Semantic Edge Insertion, Semantic Edge Deletion, Semantic Vertex Substitution, Semantic Edge Substitution, Semantic Path Insertion, Semantic Path Deletion, and Semantic Path Substitution.

Semantic Graph Edit Distance

Semantic graph edit distance is a measure of the minimum cost required to transform one graph into another. It is defined as the minimum cost required to transform graph G1 to graph G2 by applying semantic