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.
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.
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.
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.
Access Innovations, Inc., provider of Data Harmony software solutions, announced a partnership with SiteFusion ProConsult LLC, the North American joint venture of SiteFusion GmbH and EBCONT proconsult GmbH. SiteFusion’s content management system for publishers can leverage the taxonomy management and semantic metadata enrichment capabilities of Data Harmony. Workflows can be configured to instantiate an automated metadata enrichment step to make the publications or content more findable and discoverable. In addition, publishers can analyze text content and complete entity identification (people, places, things, etc.), which is important to meet governance requirements.
https://www.accessinn.com ■ https://www.sitefusion.pro
Ontotext, an enterprise knowledge graph technology and semantic database engine provider, announced that Integral Venture Partners (Integral), a capital investment firm, announced this week that an Integral–led investment consortium has entered into a definitive agreement with our mother company Sirma Group Holding, to acquire Ontotext as a global supplier of a deep-tech enterprise software, operating in the graph databases space and the Artificial Intelligence market. The Integral-led international investment consortium also includes PortfoLion Capital Partners, the venture capital and private equity arm of OTP Bank, and Carpathian Partners, a specialized technology-focused investment platform based in London. The Consortium’s investment in excess of €30 million will be structured as a combination of a capital increase and a secondary share purchase. The transaction is not subject to any regulatory approvals and is expected to close by August 2022.
Supported by new capital, Ontotext will accelerate its international expansion and go-to-market operations, focusing on the US market. We will invest in further development of our vertical product stack — end-to-end solutions for specific industries starting with Life Sciences and Financial Services. Last but not least, we will further strengthen our position as global provider of knowledge graph technology.
Finch Computing, developers of real-time natural language processing solution Finch for Text, announced that it has added relationship extraction and co-references to the product. Relationship extraction gives users an ability to decipher relationships between entities, and co-reference enables words like “her” or “him” or “the leader” appearing in text to be resolved to a specific, named entity.
Finch for Text can now find important relationships between entities such as: Acquired-by, Co-Investor-with, Competitor-with, Customer-of, Director-of, Educated-at, Employer-of, Founder-of, Invested-in, Organization-Location, Owner-of, Partner-of, Person-Location, Relative-of, and Subsidiary-of. For companies and people in particular, understanding these connections helps users perform faster, richer and deeper analysis.
Entity co-reference refers to the ability to resolve otherwise obscure references to an entity – like her, him, the company, the product – to a disambiguated entity. The value of this capability is that it helps users understand all mentions of an entity even if that mention isn’t by name. It improves salience scores because the product can better gauge how much an article is about a given entity. It also improves sentiment scores with more mentions to analyze, and the same is true for relationship extraction – there are more relationships discovered because there are more mentions linked to an entity.
From the Google Products Blog…
… today we’ve added 24 languages to Translate, now supporting a total of 133 used around the globe.
Over 300 million people speak these newly added languages — like Mizo, used by around 800,000 people in the far northeast of India, and Lingala, used by over 45 million people across Central Africa. As part of this update, Indigenous languages of the Americas (Quechua, Guarani and Aymara) and an English dialect (Sierra Leonean Krio) have also been added to Translate for the first time.
This is also a technical milestone for Google Translate. These are the first languages we’ve added using Zero-Shot Machine Translation, where a machine learning model only sees monolingual text — meaning, it learns to translate into another language without ever seeing an example. While this technology is impressive, it isn’t perfect. And we’ll keep improving these models to deliver the same experience you’re used to with a Spanish or German translation, for example. If you want to dig into the technical details, check out our Google AI blog post and research paper.