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The keys to modern knowledge engineering

The keys to modern knowledge engineering

April 20, 2018

The goal of knowledge engineering is to take integrated and evaluated data and turn the insights derived from it into knowledge. However, even after emerging as an artificial intelligence-driven solution to collect, understand, and infer relationships from big data, questions persist on how to begin the process of knowledge engineering and what tools to use.

Robin Bramley, Chief Scientific Officer at Ixxus, believes that there are four keys to modern knowledge engineering. According to him, one of the keys to leveraging knowledge engineering is the semantic web because it enhances web technologies with formally represented, open ontologies that can unambiguously describe real-world domains in a machine- and human-readable, interoperable way.

Another key is open data sets. Open data sets when published using semantic web principles are known as Linked Open Data (LOD). When interlinked, LOD sets help establish broader connections between silos of human knowledge. This enables data from different sources to be connected and queried.

The third key to knowledge engineering is machine learning, which is an approach to synthesize raw or semantically enriched content to yield insights. The fourth key is cloud-computing infrastructure. It enables rapid prototyping and experimentation, thanks to its ability to acquire large amounts of computational resources for specific tasks and to scale infrastructure up and down rapidly.

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Brought to you by Scope e-Knowledge Center, an SPi Global Company, a trusted global partner for Digital Content Transformation Solutions, Knowledge Modeling (Taxonomies, Thesauri and Ontologies), Abstracting & Indexing (A&I), Metadata Enrichment and Entity Extraction.

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