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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 announces Metadata Studio 3.8

Ontotext, a provider of enterprise knowledge graph and semantic database engines, announced the latest version of Ontotext Metadata Studio (OMDS), a tool designed for knowledge graph enrichment through text analytics of unstructured documents. Version 3.8 aids in the creation, evaluation, and quality improvement of text analytics services. With more intuitive and effective search solution capabilities, enhancement to OMDS removes the difficulties users face when exposing semantic search over their documents, especially when they are working with their own, custom reference domain models. Updates include:

  • Enhanced Domain Model Search Interface transforms the reference annotation schema into a user-friendly search interface, allowing exploration and retrieval of content based on the preferred domain data model.
  • Knowledge Graph Enrichment and Extension enables users to reuse their domain models so they can be leveraged for advanced analytics and quality management.
  • Advanced Search Capabilities supports all types of searches. The solution allows users to conduct simple searches such as identifying documents containing specific text as well as complex queries that filter documents based on the presence or absence of certain text and combinations of metadata objects and property values.
  • Improved Usability and Workflow Efficiency enables users to organize content effortlessly by moving documents between corpora or deleting them from the database.

AtScale introduces Developer Community Edition for semantic modeling

AtScale, a provider of semantic layer solutions for analytics and Generative AI, today announced the public preview of the AtScale Developer Community Edition. This free downloadable version of AtScale’s semantic layer platform allows users to build and share semantic models to democratize analytics. The Developer Community Edition serves as a Universal Semantic Hub, allowing semantic models to be distributed to various AI/BI tools, promoting a connected data environment. Features include:

  • Semantic Modeling Language – An object-oriented modeling language called the semantic modeling language (SML) was introduced to express complex business concepts. SML facilitates the sharing and reusing of composable and versionable semantic objects, supporting an analytics mesh, or hub-and-spoke governance style for users.
  • Business-Friendly Interface – Users can comprehend and analyze data without needing to know complex query languages or database knowledge.
  • Public Semantic Model GitHub Repository – AtScale Developer Community Edition adds a public GitHub repository for pre-built, reusable semantic models, helping users and the community to share, learn, and innovate together, making the benefits of analytics widely accessible. Industry-specific semantic models are readily available for use with any BI tool, offering value to companies seeking to leverage data modeling in their operations.

Ontotext updates LinkedLifeData Inventory solution

Ontotext, a provider of enterprise knowledge graph (EKG) technology and semantic database engines, announced the latest version of its LinkedLifeData (LLD) Inventory solution, an accelerator for building knowledge graphs. Providing 200+ semantic-ready biomedical datasets, and available immediately, LinkedLifeData Inventory 1.4 serves as a resource for the scientific community, fostering multidisciplinary exploration and analysis across various facets of Life Sciences and Healthcare research.

Covering data in multiple modalities, such as genomics, proteomics, metabolomics, molecular interactions, and biological processes, LinkedLifeData Inventory allows healthcare and life sciences professionals to access public datasets and ontologies in Resource Description Framework (RDF) format to ensure semantic richness and interoperability, facilitate advanced data integration, semantic querying, and insight generation in alignment with FAIR data principles.

LinkedLifeData Inventory helps pharma companies discover and repurpose existing drugs to treat rare and newly identified diseases. Biotech companies leverage LLD Inventory to identify new drug targets and build model datasets, while research teams use it to efficiently navigate the huge volume and wide range of data about genes, proteins, compounds, diseases, etc. Enhancements include:

  • Automated data ingestion and updating
  • Entity linking and improved metadata governance
  • AI-generated Gene-disease link prediction datasets
  • Newly added datasets
  • Transformation of complex derivative datasets

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.

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.

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.

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. launches AI platform for Life Sciences announced availability of the Platform for Life Sciences. With the 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 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.

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