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Category: Semantic technologies (Page 1 of 72)

Our coverage of semantic technologies goes back to the early 90s when search engines focused on searching structured data in databases were looking to provide support for searching unstructured or semi-structured data. This early Gilbane Report, Document Query Languages – Why is it so Hard to Ask a Simple Question?, analyses the challenge back then.

Semantic technology is a broad topic that includes all natural language processing, as well as the semantic web, linked data processing, and knowledge graphs.


Ontotext releases Ontotext Metadata Studio 3.7

Ontotext, a provider of enterprise knowledge graph (EKG) technology and semantic database engines, announced the availability of Ontotext Metadata Studio (OMDS) 3.7, an all-in-one environment that facilitates the creation, evaluation, and quality improvement of text analytics services. This latest release provides out-of-the-box, rapid natural language processing (NLP) prototyping and development so organizations can iteratively create a text analytics service that best serves their domain knowledge. 

As part of Ontotext’s AI-in-Action initiative, which helps data scientists and engineers benefit from the AI capabilities of its products, the latest version enables users to tag content with Common English Entity Linking (CEEL), text analytics service. CEEL is trained to tag mentions of people, organizations, and locations to their representation in Wikidata – the public knowledge graph that includes close to 100 million entity instances. With OMDS, organizations can recognize approximately 40 million Wikidata concepts, and  streamline information extraction from text and enrichment of databases and knowledge graphs. Organizations can:

  • Automate tagging and categorization of content to facilitate more efficient discovery, reviews, and knowledge synthesis. 
  • Enrich content, achieve precise search, improve SEO, and enhance the performance of LLMs and downstream analytics.
  • Streamline information extraction from large volumes of unstructured content and analyze market trends.

https://www.ontotext.com/products/ontotext-metadata-studio/

Ontotext and TopQuadrant partner to accelerate adoption of graph and semantic technologies

Ontotext, a semantic data and knowledge graph technology provider, and TopQuadrant, a provider of software tools for data governance and semantics, announced a partnership to bring advantages to their shared customer base. With TopQuadrant, Ontotext clients gain a knowledge graph creation and curation tool that enables new data governance use cases, while TopQuadrant clients benefit from improved scalability, usability, and performance. The combination of front and back-end systems enables:

  1. Scalability and Performance for Large Data Sets: With Ontotext’s RDF database GraphDB, semantic data products such as taxonomies, tag lists, metadata stores, and code lists, can now scale to handle master data management and enterprise data quality and validation efforts.
  2. Policy Enforcement and Automation: TopQuadrant’s expertise in data governance and metadata management will help clients enforce policies across organizations’ full data landscape, mitigating risk of regulatory fines such as for GDPR.
  3. Pharma R&D Semantic Solution: This solution will enable data models to capture data and improve data quality, enhancing collaboration and efficiency, automating regulatory reporting and ultimately enabling new insights into drug discovery.
  4. Semantic Data Catalog: The semantic approach for active metadata management harmonizes data and metadata across an entire organization. The semantic data catalog actively populates metadata and makes data more interoperable and reusable.

https://www.ontotext.com/company/news/ontotext-and-topquadrant-a-powerful-partnership/

Ontotext GraphDB now available in AWS Marketplace

Ontotext announced that its core product, GraphDB, is now available in the AWS Marketplace. Delivered through the AWS Marketplace, enterprises can simplify, globally implement and roll out graph databases to support cloud data migration from on-premises environments to AWS and other public cloud providers and remain compliant with stringent industry and privacy regulations.

Ontotext GraphDB makes building knowledge graphs faster, which provides users with an end-to-end platform for enterprise-wide data integration and discovery. GraphDB was designed for companies with decentralized data challenges who need data-driven analytics to drive insights for mission-critical business needs. GraphDB on AWS enables their joint customers to:

  • Develop a linking engine for enterprise data management, dismantling data silos and minimizing time to insights and time to market.
  • Harmonize data sources to allow effective data sharing, collaboration, and semantic data discovery to deliver ROI on information architecture spend.
  • Enable standardized data exchange, discovery, integration, and reuse to deliver 360 views of their business.

With a simplified billing process, customers can quickly get started with GraphDB to integrate disparate and disconnected structured and unstructured data.

https://www.ontotext.com/products/graphdb/

WordLift introduces Content Generation Tool

WordLift announced a WordLift Content Generation Tool, technology for content generation that seamlessly integrates the capabilities of Large Language Models (LLMs) and incorporates a compound network of Knowledge Graphs (KG).

At the heart of WordLift is the Knowledge Graph. It’s a dynamic, interconnected web of information. It’s mapping every piece of information, fact, and relationship. It creates a rich tapestry that breathes life into your content. It isn’t only data; it’s a living, evolving entity that understands context, relationships, and nuances. By leveraging these graphs built using WordLift, we guide the LLMs, ensuring they remain on the right path, enriching content with depth and relevance, enabling a more reliable and accurate way of exploiting LLMs.

Key features and technologies: 

  • Structured Data Integration, making content more readable and recognizable for search engines like Google.
  • Knowledge Graph Creation, with the help of AI, allows search engines to comprehend the structure of your content.
  • Content Recommendation System can suggest products to users and integrate clickable cards, widgets, and shopping cart banners.
  • Generative AI-powered content creation to scale content production through AI within the company’s guidelines and TOV.
  • Integration with Data Studio facilitates the creation of shareable, comprehensive reports.

https://wordlift.io

Expert.ai launches AI platform for Life Sciences

Expert.ai announced availability of the expert.ai Platform for Life Sciences. With the expert.ai Platform for Life Sciences, teams can access advanced natural language understanding capabilities, learning methodologies, 3rd-party large language models like BioBert and Bio-GPT as well as customizable pre-built knowledge models to build custom solutions.

Through a hybrid AI approach combining natural language tools, enterprise language models and machine learning, the expert.ai Platform for Life Sciences shifts the way unstructured medical and scientific data is monitored, understood, analyzed and collated. Teams can access knowledge and insights trapped in medical articles, reports, press releases, clinical research, customer/patient interactions, consent forms, etc. as well as up-to-date knowledge available based on standards like MeSH, UMLS Conditions & Interventions and IUPAR. Pharmaceutical and Life Sciences teams can:

  • Confirm scientific claims against trusted public and private knowledge sources;
  • Extract connections between biomedical entities in literature for in-depth causality analysis to support researchers; 
  • Monitor clinical trials and social media sources filtered by any combination of indication, drug, mechanism of action, sponsor, or geography to gain insight for clinical trials; 
  • Accelerate the quality control process of clinical and preclinical reports analysis using sensitive and proprietary data sources prior to their submission to regulatory bodies.

https://www.expert.ai/

Stardog introduces Stardog 9

Stardog, a provider of an enterprise knowledge graph platform, announced Stardog 9, with a range of new features and enhancements that enable organizations to easily connect data, people, and processes, and improve performance, scalability, and security. With this release, Stardog’s knowledge graph powered semantic layer has new integrations for Azure Synapse, Collibra Data Governance and Databricks. Benefits include:

  • Expanded Data Access: Stardog 9 supports federated access to Azure Synapse which enhances connectivity to data in Azure Data Lake Storage Gen-2 (ADLS2), reducing the friction in accessing and connecting data through meaning for self-serve analytics.
  • Activated Metadata: Stardog 9 extends Stardog’s Knowledge Catalog to harvest enterprise metadata with integrations for Collibra and Microsoft Purview Data catalogs (in-preview mode only), and any JDBC-accessible data source. These integrations make it easy to semantically-enrich technical metadata with business concepts and enable Data Governance teams and end users to search, query, and explore data assets with an Enterprise Metadata Knowledge Graph.
  • Smart, Automated Entity Linking Across Data Silos: Stardog 9 can identify and link data associated with business objects across data landscapes for better decisions in support of use-cases from Customer 360 to Digital Twin to Fraud Detection, leveraging Databricks Spark to process data.

https://www.stardog.com/blog/introducing-stardog-9/

Expert.ai and Reveal Group partner to combine NLP and RPA

Expert[.]ai and Reveal Group announced a partnership to help organizations extend the value in intelligent automation programs with natural language processing and understanding (NLP/NLU). Robotic process automation (RPA) makes organizations more profitable and responsive, streamlining enterprise workflows and enhancing employee engagement and productivity by removing mundane tasks from their workdays. By adding NLP/NLU to RPA, enterprises now have the ability to increase the flexibility and scalability of automation, expanding deployment to more complex use cases and business processes by making sense of unstructured language data. Unstructured data is critical for organizations to be able to understand, analyze and use it to enable a real intelligent automation across the entirety of an enterprise data assets.
 
The expert.ai hybrid AI platform complements the Reveal Group’s expertise in intelligent automation services. With expert.ai, NLP outputs, including intent, automatic categorization, emotional and behavioral traits identification, entity extraction and sentiment analysis, can be deployed and delivered  by Reveal Group to automate multiple use cases, from common cross-industry use cases (email triage in customer services, data analysis, comparison and extraction in legal departments) to more industry-oriented processes (claims management in insurance companies, loan origination and customer onboarding in banking and financial services.).
  
https://www.expert.ai/https://revealgroup.com/
 

Kobai launches Saturn Knowledge Graph

Kobai, a codeless knowledge graph platform, announced the availability of Kobai Saturn, a knowledge graph to harness the scale, performance, and cost efficiency of the lakehouse architecture. Kobai Saturn extends the capabilities of the Kobai Platform, integrating every use case and function into a single semantic layer.

Business users need quick insights to make day-to-day decisions, which require connected data from data from across the enterprise. With Kobai Saturn, organizations can leverage the ease of knowledge graphs with the scalability of a data warehouse. New capabilities include:

  • Direct integration: embedded in the data layer, organizations can query data without moving it from the lake or warehouse, following W3C and Lakehouse open standards for complete interoperability
  • Improved performance: on-demand and burstable compute leveraging the underlying data layer for faster graph queries and ML training without virtualization
  • Seamless collaboration: publish business question as SQL views to integrate with existing data science and business intelligence tools

Kobai’s codeless platform provides a business-first approach and a collaborative environment to rapidly share insights across the entire organization. The new Kobai Saturn knowledge graph works directly with Kobai’s Studio framework and Tower visualization products.

https://www.kobai.io/products/kobai-saturn

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