Cross-Domain Knowledge Graph

• Cross-domain knowledge graphs are structured representations that integrate diverse data, entities, and relationships from multiple domains.
• They employ advanced semantic partitioning and edge-centric similarity metrics with hierarchical fuzzy clustering to capture nuanced, overlapping memberships.
• This approach supports practical applications such as biomedical informatics, clinical decision support, and interdisciplinary media analytics.

A cross-domain knowledge graph is a structured representation that integrates data, entities, and relationships originating from multiple, often heterogeneous, domains. Unlike traditional domain-specific knowledge bases, cross-domain knowledge graphs explicitly model, partition, and analyze semantic overlaps and interconnections among concepts spanning disparate knowledge sources such as life sciences, social web, media, and scientific publications.

1. Semantic Partitioning and Edge-Centric Similarity

Central to cross-domain knowledge graph construction is the move from physical topology-based partitioning to semantic partitioning. Instead of clustering nodes based on direct connectivity or syntactic relationships, semantic partitioning leverages measures of conceptual similarity—often computed over predicates in RDF graphs—so that clusters (partitions) aggregate semantically proximal nodes even if they are physically distant in the graph.

A key technical advancement is the adoption of edge-centric similarity metrics. For instance, given two predicate nodes $V_{p_i}$ and $V_{p_j}$, their semantic association is determined by their "neighboring level" $n(V_{p_i}, V_{p_j}) = L$, defined as the shortest path length with semantic edges. This level is then passed to a similarity function $f(\cdot)$:

$ps(V_{p_i}, V_{p_j}) = f(n(V_{p_i}, V_{p_j}))$

where closer pairs (smaller $L$) receive higher weights. This formulation enables the grouping of predicates—and thus corresponding triples—that are semantically related across domains, forming soft partitions that more accurately reflect cross-domain overlaps.

2. Hierarchical and Fuzzy Clustering for Partitioning

Integration across domains introduces significant overlap and uncertainty: entities or predicates may logically belong to multiple domains. Traditional rigid clustering leads to suboptimal partitions in these cases. The hierarchical fuzzy c-means (HFCM) algorithm addresses this by enabling nodes to simultaneously belong to multiple clusters with varying degrees of membership. The HFCM operates recursively, splitting clusters into finer groups until further partitioning does not increase cluster quality as evaluated by silhouette width, or until hardware resource constraints (e.g., per-machine graph size) are reached.

Formally, the fuzzy clustering is driven by a similarity (or distance) matrix $CM$:

$$CM[i, j] = \begin{cases} ps_{i,j}, & i \neq j \ 0, & i = j \end{cases}$$

This matrix is optimized so that semantically linked nodes (as per Definitions 1–4 in the source) are co-clustered, with fuzzy assignment capturing uncertainty and real-world cross-domain affiliations.

3. Domain Integration Strategies

Cross-domain knowledge graphs require methods for ingesting and aligning data from arbitrary source domains. The discussed framework normalizes heterogeneous RDF datasets, including those from Gene Ontology, DrugBank, BioPortal, and social media sources. Semantic clustering is then applied not on raw source labels but on predicate similarities, so data from unrelated domains (e.g., gene-disease and drug interaction networks) can be fused when their underlying semantics overlap.

This integration is powered by:

• Distance measurement (computing the shortest semantic path between predicates from different domains)
• Probability-based similarity (overlap in entities connected to predicates)
• Dynamic weighting (using intermediary predicates for multi-step relationships)

Clusters derived from these techniques often span traditional domain boundaries, directly supporting composite queries across biomedical, clinical, and media knowledge.

4. Challenges and Solutions: Scale, Semantics, and Overlapping Membership

Key challenges in constructing cross-domain knowledge graphs include:

• Data volume and heterogeneity: The proliferation of big linked data renders manual cross-domain annotation and knowledge extraction unscalable.
• Semantic versus physical relationships: Physical partitioning does not capture latent semantic relationships. Nodes separated in network topology may be closely related conceptually.
• Overlap and uncertainty: Real-world entities (e.g., a gene involved in multiple diseases) require fuzzy or multi-membership partitioning.

Solutions provided by the edge-centric neighboring and hierarchical fuzzy clustering include:

• Explicit measurement of semantic distance, facilitating more accurate partitioning.
• Fuzzy partitioning to reflect multi-domain membership.
• Automated evaluation (silhouette width) to set the optimal number of clusters, balancing computational manageability with semantic integrity.

5. Applications: From Biomedical Integration to Multi-Modal Knowledge Discovery

The cross-domain knowledge graph framework underpins applications such as:

• Biomedical research: Automated discovery of gene-disease-drug relationships aids tasks like drug repositioning and integrative genomics.
• Clinical decision support: Consolidation of narratives spanning medical literature and patient data is enabled by cross-domain semantic linkage.
• Media and social web curation: Merging datasets from different sources helps with trend analysis, misinformation identification, and comprehensive media monitoring.
• Interdisciplinary knowledge mining: Queries that span research literature, clinical trials, and genomics can discover patterns hidden within siloed domain-specific KGs.

The capacity to bridge disparate datasets through semantic rather than syntactic/surface integration enables more powerful, accurate, and expressive downstream analytics.

6. Mathematical Formulation and Evaluation Metrics

The framework provides formal definitions for path-based semantic similarity, probability overlap between predicate domains, and the process for building a distance matrix $CM$ used in clustering:

• Neighboring level: $n(V_{p_i}, V_{p_j}) = L$ when distance $d(V_{p_i}, V_{p_j}) = L$.
• Probability-based similarity: For predicates $V_{p_i}$ and $V_{p_j}$ with entity sets $A$ and $B$,

$$ps(V_{p_i}, V_{p_j}) = |A \cap B| + |A \setminus B|$$

• Dynamic weighting: For multi-step paths, similarity is assigned via maximization or multiplication of intermediate step similarities.

Cluster quality and the effectiveness of partitioning are automatically validated using silhouette width—a metric evaluating intra-cluster cohesion versus inter-cluster separation—at every recursive partitioning step.

7. Implications and Future Directions

The cross-domain knowledge graph approach described offers a paradigm shift for large-scale knowledge discovery, shifting focus to semantic-driven, fuzzy, and scalable integration. By formalizing semantic similarity metrics and enabling fuzzy partitioning, it addresses both scalability and the intrinsic uncertainty of cross-domain knowledge representation.

Applications in biomedical informatics, media analytics, and integrated decision support stand to benefit substantially. The evolution toward automated, semantic-based integration—rather than human-curated or physically partitioned KGs—provides the foundation for next-generation cross-domain knowledge analytics platforms, facilitating more efficient, accurate, and comprehensive discovery across heterogeneous big data ecosystems (Shen, 2019).