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This is accomplished through tags, annotations, and metadata (TAM). Smart content includes labeled (tagged, annotated) metadata (TAM). The key to success is to start enhancing and augmenting content management systems (CMS) with additional features: semantic content and context. Collect, curate, and catalog (i.e.,
We store this in GraphDB by leveraging standard tooling for knowledge graph management. Through Ontotext Metadata Studio (OMDS), we then apply semantic content enrichment using text analysis based on our marketing vocabularies. In this way, we benefit from better SEO and semantic-driven content discovery.
Knowledge graphs (KG) came later, but quickly became a powerful driver for adoption of Semantic Web standards and all species of semantic technology implementing them. This way KGs help organizations smarten up proprietary information by using global knowledge as context for interpretation and source for enrichment.
Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of data integration, data and service-level management. Knowledge Graphs are the Warp and Weft of a Data Fabric.
The enrichment process begins when a document is ingested into the raw zone, invoking an Amazon S3 event that initiates a Step Functions workflow. Amazon S3 emits an object created event and matches an EventBridge rule. The event invokes a Step Functions state machine.
Seen through the three days of Ontotext’s Knowledge Graph Forum (KGF) this year, complexity was not only empowering but key to the growth of knowledge and innovation. Knowledge graphs are often criticized for being too complex”, said Atanas, “but such initiatives actually are bound to be complex.
The aim of this project is to make cultural events findable for voice-powered and AI-powered search assistants. This becomes possible thanks to metadata enrichment, integrating and linking data from various data sources and, ultimately, dumping, structuring and querying that data with the help of a semantic graph database.
The aim of this project is to make cultural events findable for voice-powered and AI-powered search assistants. This becomes possible thanks to metadata enrichment, integrating and linking data from various data sources and, ultimately, dumping, structuring and querying that data with the help of a semantic graph database.
Knowledge graphs, while not as well-known as other data management offerings, are a proven dynamic and scalable solution for addressing enterprise data management requirements across several verticals. As a hub for data, metadata, and content, they provide a unified, consistent, and unambiguous view of data scattered across different systems.
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