Semantic Integration is the process of integrating information from diverse sources into a single structure. For example, pulling video conferencing invitations into a calendar app. Or consolidating news mentions about a set of organizations into an organization view within a dashboard.
The primary challenges of semantic integration include understanding the meaning, context, or form of data and properly pairing it up with useful values from another source. Additionally, if semistructured or unstructured data sources are included, formatting, cleaning, and coherently structuring data is key.
Semantic integration is central for incorporating multiple data type communications between machines which is often needed to create intelligent systems. Context around data allows machines to make decisions and serve up the right data into the right workflow.
Metadata often plays a critical role in semantic integration, particularly when ontologies vary. Mapping distinct ontologies to one another can vastly increase the range of data that can be pulled into a single cache as well as can be provided to machine learning algorithms.
Benefits of Utilizing Knowledge Graphs For Semantic Integration
Diffbot provides the world’s largest commercially-available Knowledge Graph, which utilizes many levels of semantic integration. Data pulled from sources across the web must be extracted and structured such that it can be linked regardless of it’s original source or format.
One benefit knowledge graphs provide for semantic integration is that each entity within knowledge graphs has a unique identifier. This allows for disambiguation (e.g. two “John Does” may have different work history fields) even if different data types incorporate the same entity.
Knowledge graphs are also fantastic ways to integrate data into other data stores. The added semantics stored in entity ontologies can expand the available fields (and thus source types) that can be integrated into your existing entries.
The third way knowledge graphs support semantic integration is through their flexibility. Knowledge graphs provide flexible schemas that can incorporate new fact types “on the fly.” The range of fact types that can be modelled in a graph structure including concepts, relationships between things, hierarchies, and types of things themselves.
Semantic Integration Research
- “Semantic Integration With Ontology Based Approach”
- “An Ontology-Guided Semantic Data Integration Framework to Support Integrative Data Analysis Of Cancer Survival”
- “An Approach For Semantic Integration of Heterogeneous Data Sources”
See also, knowledge fusion, entity resolution, and ontology.