Curated for content, computing, and digital experience professionals

Category: Semantic technologies (Page 1 of 69)

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.


AtScale adds enterprise AI capabilities to semantic layer platform 

AtScale, a provider of semantic layer solutions for modern business intelligence and data science teams, announced new product capabilities for organizations working to accelerate the deployment of enterprise artificial intelligence (AI). These new capabilities leverage AtScale’s position within the data stack with cloud data warehouse and lakehouse platforms including Google BigQuery, Microsoft Azure Synapse, Amazon Redshift, Snowflake, and Databricks. The AtScale Enterprise semantic layer platform now incorporates:

  • Semantic Predictions – Predictions generated by deployed AI/ML models can be written back to cloud data platforms through AtScale. These model-generated predictive statistics inherit semantic model intelligence, including dimensional consistency and discoverability. Predictions are immediately available for exploration by business users using BI tools (AtScale supports connectivity to Looker, PowerBI, Tableau, and Excel) and can be incorporated into augmented analytics resources.
  • Managed Features – AtScale creates a hub of centrally governed metrics and dimensional hierarchies that can be used to create a set of managed features for AI/ML models. AtScale managed features inherit semantic context, making them more discoverable and easier to work with. Managed features can now be served directly from AtScale, or through a feature store like FEAST, to train models in AutoML or other AI platforms.

https://www.atscale.com/product/ai-link/

Apptek and expert.ai announce strategic partnership

AppTek and expert.ai announced they have entered into a strategic technology partnership to bring AI-based text analytics to dynamic audio content in multiple languages. The partnership leverages AppTek’s Automatic Speech Recognition (ASR) and Neural Machine Translation (NMT) technologies with expert.ai’s natural language understanding capabilities to enable organizations to leverage audio content in the unstructured data sets that they manage for improving decision making and augmenting intelligent automation.

As organizations increasingly utilize language data—emails, documents, reports and other free form text— for an ever-growing range of enterprise use cases (knowledge discovery, contract analysis, policy review, email management, text summarization, classification, entity extraction etc.), natural language capabilities will play a critical role in powering any process or application that relies on unstructured language data. The combined capabilities of AppTek and expert.ai supercharge enterprise and government NLU and NLP applications, expanding the data types and sources available for analysis to provide even more informational output.

Using AppTek’s speech-to-text technology within the expert.ai Platform, organizations can automatically transcribe audio types from different sources, including high-quality media broadcast content, podcasts, meetings, one-to-one interviews or even low bandwidth telephone conversations. In addition, they can leverage advanced multilingual functionalities to generate accurate, customizable and scalable translations across hundreds of language pairs.

https://www.apptek.com/https://www.expert.ai/

Stardog joins Databricks Partner Connect

Stardog, an Enterprise Knowledge Graph platform provider, today announced it had joined Databricks Partner Connect, which lets Databricks customers integrate with select partners directly from within their Databricks workspace. Stardog is the first Databricks partner to deliver a knowledge-graph-powered semantic layer. Now with just a few clicks, data analysts, data engineers, and data scientists can model, explore, access, and infer new insights for analytics, AI, and data fabric needs — an end-to-end user experience without the burden of moving or copying data. Together, Stardog’s availability on Databricks Partner Connect enables joint customers to:

  • Easily define and reuse relevant business concepts and relationships as a semantic data model meaningful to multiple use cases.
  • Link and query data in and outside of the Databricks Lakehouse Platform to provide just-in-time cross-domain analytics for richer insights.
  • Ask and answer questions across a diverse set of connected data domains to fuel new business insights without the need for specialized skills.

https://www.stardog.comhttps://www.databricks.com/partnerconnect

Algolia acquires Search.io

Algolia, an API-First Search & Discovery Platform, announced the acquisition of Search.io, whose flagship product is Neuralsearch – a vector search engine that uses hashing technology on top of vectors to provide price performance at scale. Algolia will combine its keyword search and Search.io’s Neuralsearch into a single API-First Search and Discovery platform with a hybrid search engine, which comprises both keyword and semantic search in a single API.

The combination of Algolia (with its keyword search) and Search.io (with its vector-based semantic search), enables Algolia to more effectively surface the most accurate and relevant results for users, whether they use specific keywords or natural human expressions. Many companies claim to offer some form of semantic search, however, these companies may not offer the capabilities of keyword search and vector-based semantic search in a single API cost-effectively, or the ability to scale. In essence, Algolia provides users with the ability to search as they think. With Search.io, Algolia aims to empower business users with a better way to manage the automation of unique and engaging end user experiences.

https://www.algolia.com/about/news/algolia-disrupts-market-with-search-io-acquisition-ushering-in-a-new-era-of-search-and-discovery/

Ontotext announces Metadata Studio

Ontotext introduced Ontotext Metadata Studio, built on top of the GraphDB and Ontotext Platform. Metadata Studio enables organizations to get more out of their content by unlocking new business models or achieving cost optimizations by putting their own Subject Matter Experts (SME) at the heart of text analysis.

With Ontotext Metadata Studio, organizations can use business analysts to define Semantic Objects as specific views, abstracting developers away from the complexity and peculiarities of the knowledge graph. This allows them to reference the pre-existing domain knowledge modeled in their ontologies and annotate relevant documents following the established Annotation Guidelines for the specific use case.

Ontotext Metadata Studio can be integrated with many text analysis services via GraphDB’s Text Mining Plugin, e.g., spaCy, IBM Watson, Amazon Comprehend, Google NLP, Ontotext Tag (powering the Ontotext NOW demonstrator), etc. This enables the evaluation of a service or the suitability of a combination of services for the currently explored use case against the ground truth data produced by the annotators. This can shorten the Time to market (TTM) for new product development.

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

Data Harmony suite Recommender released

Access Innovations, Inc., provider of Data Harmony software solutions, announced the release of their new Recommender as part of the Data Harmony Suite. Recommender is now available to all Data Harmony clients using versions 3.16 or higher.

Recommender uses the semantic fingerprint of an article, its subject metadata tagging, matching to other articles and content within the database. When the searcher finds an article they like, the Recommender automatically displays other items with the same semantic fingerprint nearby on the search interface. This allows immediate display of highly relevant content to the search without scrolling and frustration in trying to find similar items. It also allows for display of other relevant content such as conference papers, ads, books, meetings, expert profiles, and so forth.

This is not based on personalization profiles or purchasing history. By using the metadata weighting and other algorithms it provides only items relevant to the current query faster search and the surfacing of more related information to the user.

For those interested in using Recommender there are two prerequisites: 1) the content needs to be indexed or tagged using a controlled vocabulary like a thesaurus or taxonomy, and 2) the search interface needs to be able to accommodate the API call to the tagged data and subsequent display of the results.

https://www.accessinn.com

Ontotext releases GraphDB 10

GraphDB 10.0 is the first major release since GraphDB 9.0 was released in September 2019. It implements next generation, simpler and more reliable cluster architecture to deliver better resilience with reduced infrastructure costs. GraphDB 10 lowers the complexity of operations with better automation interfaces and a self-organized cluster for automated recovery. Deployment and packaging optimizations allow for effortless upgrades across the different editions of the engine, all the way from GraphDB Free to the Enterprise Edition. The improved full-text search (FTS) connectors of GraphDB 10 enable more comprehensive filtering as well as easier downstream data replication. Finally, parallelization of the path search algorithms brings massive improvement in graph analytics workloads through better exploitation of multi-core hardware.

Unlike previous versions, GraphDB 10 is packaged as a single distribution that can run in Free, Standard or Enterprise Edition modes depending on the currently set license. It requires zero development effort to pass from one edition to another. It is also possible to export a repository with an expired license so users are never locked out of their own data. Two major areas of improvement coming in 10.1 will be query performance optimization and availability on some of the major cloud platforms.

https://www.ontotext.com

Stardog updates enterprise knowledge graph platform

Stardog, an Enterprise Knowledge Graph platform provider, unveiled Stardog 8.0, with new innovations to streamline data exploration and discovery for all citizen data users.

The new Advanced Query tool in Stardog Explorer empowers citizen data users to ask complex business questions via the semantic layer more easily. By removing the need to learn a graph query language, users can self-serve from across their enterprise data landscape. Also new in Explorer is the ability to see what’s in your Stardog database by browsing data source and virtual graph metadata in the new Stardog Data Catalog graph. Additional updates include:

  • Project resources (imported CSV files and virtual graphs) can be previewed and refreshed to see updates in the data.
  • Enhanced support for project collaboration through exporting, importing, and duplicating projects.
  • Our new query profiler is available and shows you the query plan, allows you to interrupt slow queries, and can show you partial results.
  • Error notifications now stay displayed and the error text can be copied for troubleshooting.
  • A new Stardog Data Catalog graph is built from the metadata about data sources and virtual graphs within a Stardog database.
  • Improved performance for querying multiple virtual graphs and SPARQL update queries.

https://www.stardog.com/blog/introducing-stardog-8.0/

« Older posts

© 2022 The Gilbane Advisor

Theme by Anders NorenUp ↑