This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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.,
Metadata used to be a secret shared between system programmers and the data. Metadata described the data in terms of cardinality, data types such as strings vs integers, and primary or foreign key relationships. Inevitably, the information that could and needed to be expressed by metadata increased in complexity.
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.
These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledgediscovery. ExaMode, an acronym for Extreme-scale Analytics via Multimodal Ontology Discovery & Enhancement, is a project funded by the European Union, H2020 programme. Certainly not!
This means the creation of reusable data services, machine-readable semantic metadata and APIs that ensure the integration and orchestration of data across the organization and with third-party external data. Knowledge Graphs are the Warp and Weft of a Data Fabric. Augment your data via reasoning, analytics and text analysis.
Central to today’s efficient business operations are the activities of data capturing and storage, search, sharing, and data analytics. Without metadata management and other data-related operations with semantic technologies, organizations often struggle to connect data sets and achieve a unified view of their enterprise data.
Atanas Kiryakov presenting at KGF 2023 about Where Shall and Enterprise Start their Knowledge Graph Journey Only data integration through semantic metadata can drive business efficiency as “it’s the glue that turns knowledge graphs into hubs of metadata and content”.
Automatic document summarization, natural language processing (NLP), and data analytics powered by generative AI present innovative solutions to this challenge. Solution overview The AWS Serverless Data Analytics Pipeline reference architecture provides a comprehensive, serverless solution for ingesting, processing, and analyzing data.
Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Several factors are driving the adoption of knowledge graphs. Graph solutions have gained momentum due to their wide-ranging applications across multiple industries. million users.
This dramatically simplifies the interaction with complex databases and analytics systems. Let’s see how we’ve approached this with our Ontotext Knowledge Graph project. How Ontotext uses RAG We build on top of our products GraphDB and Ontotext Metadata Studio to develop a content enrichment system.
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.
As a result, contextualized information and graph technologies are gaining in popularity among analysts and businesses due to their ability to positively affect knowledgediscovery and decision-making processes. The Graph CoE is an important step in transforming a lighthouse project or silo deployment into a true enterprise platform.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content