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Category: Semantic technologies (Page 6 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.


Access Innovations launches Data Harmony Hub for automated content classification and semantic metadata enrichment

Access Innovations, Inc. announced the launch of Data Harmony Hub, a cloud-based platform that provides fully automated content tagging using expertly curated taxonomies. Users can easily and quickly select from a variety of existing taxonomies that are continuously maintained and updated. Data Harmony Hub is a managed service allowing organizations that need their content to be tagged to improve search for their users, to provide better insight into their digital assets, to identify new revenue streams, and to greatly reduce risk and compliance issues.

The Data Harmony Hub is the result of working with Access Innovations’ clients over many years to understand their goal to integrate a tagging and semantic enrichment process into their workflow. The low-code integration allows organizations to easily connect their content management system or document store to Data Harmony Hub. Once integrated, the managed service works behind the scenes 24/7 to improve the findability and discoverability of content.

https://www.accessinn.com/data-harmony-hub/

Franz’s Gruff 8.1 brings visual knowledge graphs to web applications

Franz Inc., supplier of graph database technology for entity-event knowledge graph solutions, announced Gruff 8.1, a knowledge graph visualization software tool that can be embedded in any web page or web application. Users can now visually build queries and visualize connections between enterprise data directly within a web page or web application, enabling a simple and seamless knowledge discovery experience.

Gruff, available as a browser-based application or pre-integrated into AllegroGraph, is a no-code visual query application that enables users to create visual Knowledge Graphs that display data relationships in views driven by the user. Gruff’s visual query builder empowers both novice and expert users to create simple to complex queries without writing code. The ‘Time Machine’ function within Gruff gives users the capability to explore temporal context and connections within data. Visualizations can be customized to fit a specific user experience, data relationships, or business requirements.

Special dialogs appear in Gruff 8.1 when saving or loading a SPARQL query, graphical query, or layout. The dialog shows descriptive information about each file and allows the user to filter the list of choices in various ways to make it easier to locate the desired file.

https://allegrograph.com/products/gruff/

Cambridge Semantics updates Anzo

Cambridge Semantics announced a release of their knowledge graph platform, Anzo 5.3 to make it faster to create knowledge graphs with new codeless capabilities centered around discovering, analyzing, and connecting your enterprise data. In addition, the 5.3 release gives users an array of capabilities centered around increasing the speed and ease of data onboarding, as well as enhancements for workflow management and migration.

Direct Data Loading (DDL) enables users to create knowledge graphs directly from relational and semi-structured data sources. Customers can build knowledge graphs directly from data sources like HTTP API, RDBMS, Parquet files, and JSON files, and was designed to be flexible with use cases via no-code, low-code, and developer interfaces. Direct Data Loading is currently in Preview mode.

The new Migration Packages automate migration via a command-line interface. Packages are organized by artifact types (ontologies, datasets, graphmart, etc.) to make it easy to manage with Git. File source incremental ingestion is a no-code capability that enables loading only new files that were created/modified since the last ingestion. The Data Profiling capability enables users to perform no-code data quality control and data discover.

https://blog.cambridgesemantics.com/introducing-anzo-5.3-further-enhancing-the-leading-knowledge-graph-platform

eccenca updates multi-graph platform

eccenca, provider of automated decision processing technology, released a new version of its eccenca Corporate Memory. The multi-graph platform is a mature solution for data integration and linking, knowledge capture and reasoning. It has been helping companies like Siemens, Bosch and Total to manage their complex data landscapes and to increase their digital maturity. The latest advancements continue eccenca’s roadmap to make knowledge graph technology accessible and manageable to business users.

eccenca has also increased flexibility of data integration and exploration. As a multi-graph platform with many interface options, eccenca Corporate Memory can ingest a myriad of data sources. This includes other knowledge graph solutions that companies might already have in use for limited (and often siloed) use cases. The latest release of eccenca Corporate Memory further simplifies the integration of new sources. I addition to the optimized import automation options for vocabularies, metadata and master data sets, Google Spreadsheets and Excel sheets files can now be integrated via the commonly used share links. The reusability of project artifacts like workflow definitions help further decrease the workload of data scientists and data engineers. While the added regex extraction allows multi-match requests in complex, long strings of data.

https://eccenca.com/products/enterprise-knowledge-graph-platform-corporate-memory

RWS embeds semantic AI capabilities in Tridion

RWS, provider of language, content management and intellectual property services, has embedded semantic AI within its Tridion content platform. The new semantic AI capabilities go beyond traditional approaches to personalization, delivering smart recommendations and intuitive search results that guide customers towards finding accurate answers to their queries. Tridion is an intelligent content platform that enables organizations to create, manage and deliver multilingual digital content and digital experiences to customers, employees and partners across any channel.

Companies can use these new capabilities across their digital experience and self-service platforms, and across internal systems and intranets. Semantic AI will automatically tag content and generate metadata across an organization’s pool of information, following a well-defined knowledge model. This helps ensure content is classified accurately and consistently for any touchpoint, and eases the time-consuming burden of content authors to manually tag content.

Semantic AI is available for the various components of Tridion: Tridion Sites for Web content Management, Tridion Docs for Structured Content Management and Tridion Dynamic Experience Delivery for search and headless content publishing. The new semantic AI capabilities are available through an OEM partnership with the Semantic Web Company.

https://www.rws.com/content-management/tridion/semantic-ai/

MarkLogic acquires metadata management provider Smartlogic

MarkLogic Corporation, a complex data integration and portfolio company of Vector Capital, announced it has acquired Smartlogic, a metadata management semantic AI technology solutions provider. As part of the transaction, Smartlogic’s founder and Chief Executive Officer, Jeremy Bentley, as well as other members of the senior management team, will join the MarkLogic executive team. Financial terms of the transaction were not disclosed.

Founded in 2006, Smartlogic has deciphered, filtered, and connected data for many of the world’s largest organizations to help solve their complex data problems. Global organizations in the energy, healthcare, life sciences, financial services, government and intelligence, media and publishing, and high-tech manufacturing industries rely on Smartlogic’s metadata and AI platform to enrich enterprise information with context and meaning, as well as extract critical facts, entities, and relationships to power their businesses.

https://www.marklogic.com/https://www.smartlogic.com

Meltwater acquires DeepReason.ai

Meltwater B.V., a global SaaS provider of media intelligence and social analytics, has entered into a definitive agreement to acquire artificial intelligence start-up DeepReason.ai, a spin-off from Oxford University’s computer science department, for $7.3m in a combination of cash and Meltwater equity including earn-outs contingent on reaching technical milestones and retention requirements.

DeepReason.ai was established in 2018, to focus on the field of AI known as “reasoning”. Their technology is based on the Value Added Data Systems (VADA) research project, which was funded by UK research council EPSRC. This work represents 75 years of aggregate R&D experience, and is overseen by Georg Gottlob, Oxford professor and Fellow of the Royal Society. DeepReason.ai has developed a reasoning engine with an ability to maintain incremental views of knowledge graphs to solve the costly challenge of updating and maintaining complex knowledge graphs at scale.

Meltwater ingests and processes over 800 million documents a day, extracting new information on over 14 million companies, 50 million public personas (such as key decision makers within those companies and social media influencers) and 75 million topics. Every day, this knowledge graph expands by incorporating 2 billion connections to conversations around these companies, public personas and topics.

https://www.meltwater.com/en/about/press-releases/meltwater-acquires-deepreason-ai

Enterprise Knowledge Graph Foundation (EKGF) releases draft maturity model

Enterprise Knowledge Graph Foundation released Version 1.0 of the Enterprise Knowledge Graph Maturity Model (EKG/MM), designed to promote best practices across the knowledge graph community. Version 1.0 was created in an ongoing collaboration between experts and practitioners, and the model can be accessed or downloaded from the EKGF Website. Founding organizational members include agnos.ai, eccenca, data.world, Global IDs, Cambridge Semantics, Ontotext, Stardog, and Wizdom.

EKG/MM is designed to be the industry-standard definition and guide for the capabilities required for an enterprise knowledge graph. Intended to harmonize data from disparate sources across organizations, it can be used by business leaders, project managers, trainers, HR, legal, compliance, and finance departments, data managers, technologists, and more. It establishes standard criteria for measuring progress and sets out the practical questions that all involved stakeholders ask to ensure trust, confidence, and flexibility of data.

EKG/MM covers four essential capability areas, called “pillars,” which are grouped by the main constituencies in an enterprise: Business, Organization, Data, and Technology, each of which includes standard evaluation criteria for measuring the maturity of the design, implementation, and maintenance of an EKG.

https://www.ekgf.org

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