Expert.ai, experts in artificial intelligence (AI) for language understanding and language operations, released new features for its Natural Language (NL) platform enhancing natural language processing (NLP) workflow support. Employing a hybrid approach that combines NL techniques – including machine learning and knowledge-based, symbolic AI – the platform leverages unstructured data, like text in documents, applications and tools, to enable organizations across vertical domains to create new business models and optimize processes.
- The new release enables the use of Kubernetes (K8s) to store core data on-premise, implement specific security measures or comply with specific regulatory requirements.
- The release allows integration of 3rd-party external knowledge sources including Unified Medical Language System (UMLS) like MeSH, ICD9 and ICD10 and specific resources like the ones provided by WAND Inc., a source for domain specific taxonomies.
- Developers can now interact with expert.ai APIs using visual documentation, making it easy for back-end implementation and client-side consumption. Development teams can now visualize and interact with the API resources using a familiar Swagger interface.
- Navigation of Knowledge Graphs (KGs): Resulting in customized navigation of knowledge models to identify the strength of related concepts and connections.
Zeta Alpha, a neural search and discovery platform, announced they have integrated with OpenAI’s GPT with its semantic neural search engine, to provide more reliable and explainable AI generated answers to enterprise search queries. This capability gives workers the ability to leverage GPT to access knowledge hidden in troves of internal company data.
Generative AI models like GPT tend to ‘hallucinate,’ or give answers that seem plausible, but are not factually correct. This prevents organizations from adopting AI tools for enterprise search and knowledge management. The combination of Zeta Alpha’s intelligent neural search engine and advances in GPT-3 reduce this problem by applying natural language understanding. Other enhancements include:
- InPars v2, a GPT-powered neural search model that enables fast tuning on synthetic in-domain data without the cost of creating terminology lists and taxonomies.
- Zeta Alpha enables users to ask a question and get contextually relevant results, automatically saving text to a spreadsheet or note for further analysis, and mapping back to the location where the document is saved for future access.
- Visualizing the information landscape in a semantic map and interpreting it with summaries by GPT can guide knowledge workers in the right direction to answer important strategic questions.
Digital Science has completed the acquisition of metaphacts, which has become the newest member of the Digital Science family. Based in Germany, metaphacts is a knowledge graph and decision intelligence software company. Its main product metaphactory is a platform that supports customers in accelerating their adoption of knowledge graphs and driving knowledge democratization. metaphacts operates in the pharmaceutical, engineering, manufacturing, finance, insurance, retail and energy markets, and will be working most closely with Digital Science portfolio product Dimensions.
This acquisition will see metaphacts and Digital Science build new, joint knowledge democratization solutions, facilitating the interface between humans and machines, and helping transform raw data into human and machine-interpretable, actionable insights to power business decisions. metaphactory’s semantic knowledge modelling approach will be applied to the Dimensions linked information dataset to expose new, meaningful knowledge through metaphactory’s semantic search and graph exploration capabilities.
Customers can leverage this curated, packaged data solution and enrich and gain additional context for their proprietary knowledge. Additional integrations with complementary products from the Digital Science portfolio, such as OntoChem’s text analysis and data mining products, are also available.
https://metaphacts.com ■ https://www.digital-science.com
The EBU (European Broadcasting Union) announced EBUCorePlus, a new media metadata standard ontology for media enterprises. It is defined by EBU Members for the media community. It follows up on two long-standing EBU ontologies: EBUCore and CCDM (Class Conceptual Data Model). The two were merged and revised. The result is EBUCorePlus, the new standard that can fully replace its predecessors. It inherits both the reliability of EBUCore and the end-to-end coverage of the media value chain of CCDM. EBUCorePlus is specified using the ontology web language and therefore strictly semantic.
EBUCorePlus serves as a plug and play framework. It can be used out of the box, either in its entirety or just a subset of its elements. But it may also be adapted and extended to enterprise-specific needs. Especially for system integration tasks and defining requirements, projects benefit from EBUCorePlus as a business – not technology – oriented language.
The EBU’s free CorePlus Demonstrator Kit (CDK) can help with extending development skills from entity-relationship models to ontologies, from tables to triples, and from SQL to SPARQL, and is available in cloud, hybrid and on- prem versions. It contains a graph database, populated with the EBUCorePlus ontology and sample data.
Squirro, an Augmented Intelligence solutions provider, announced a global partnership with knowledge graph provider the Semantic Web Company, creating a “Composite AI” proposition. The new Composite AI solution delivers Machine Learning (ML), Natural Language Processing (NLP), and knowledge graph technology, and marries content with a user’s intent and context to intelligently augment decision-making.
ML creates and identifies signals but doesn’t take into account the representation of knowledge and reasoning behind it. The full picture – expanded queries and results – gives a greater understanding. The Semantic Web Company and Squirro partnership expands the scope and quality of AI applications by delivering that deeper understanding.
Squirro’s Insight Engine provides NLP and ML to classify the content on a sentence level according to the user’s intent. It provides a user-friendly interface and learns from the interaction what the user is looking for and the context of their search. This is aligned with the Semantic Web Company’s knowledge graph technology – known as PoolParty – which connects people and intent with data. This enriches content with domain knowledge and serves as a context engine. It contextualizes concepts from the content and links them to other meaningful concepts and contents to extend the search results.
https://squirro.com ■ http://www.poolparty.biz
Expert.ai updated its natural language (NL) platform. Combining machine learning (ML) and symbolic knowledge representation (Hybrid AI), the updated platform facilitates the design, development and deployment of language models, and accelerates production of enterprise applications, through accurate language understanding. Upgrades include:
- Knowledge models fortified: Built in pre-trained rules-based models contain expanded industry, role and use-case concepts, and relationships to improve the accuracy of natural language (NL) projects. Other model enhancements include updated environmental, social, governance (ESG) classification and sentiment, and Personally Identifiable Information (PII) extraction.
- New solutions for pharma & life science: Additional knowledge models support solutions for drug discovery, clinical trial insights, opinion leader identification, and scientific publication insight analysis. A new preclinical report analysis solution speeds up the quality control check process of reports prior to their submission to regulatory bodies.
- AI-driven Robotic Process Automation (RPA): The NL, hybrid platform integrates with UiPath, Blue Prism and Automation Anywhere, expert.ai supercharges bots with NL capabilities by merging different AI techniques. This expands the scope of intelligent process automation across tasks.
- Expanded deployment options: The expert.ai Platform now supports on-premise deployments of NL workflows for.
- New operational monitoring dashboard: Delivers improved visibility to operational metrics associated with language operations (LangOps).
Access Innovations, Inc., provider of Data Harmony software solutions, announced a partnership with Aptara to provide intelligent publishing solutions to book and journal publishers. Serving as a digital publishing provider since 1988, Aptara is proficient in designing and developing custom content for Fortune 500 companies and other organizations from all industries. They create, enrich, and optimize content to ensure it can be reused and repurposed — future-proofing it for all known and as yet undiscovered outputs.
Both Aptara and Access Innovations have extensive content experience, from structuring to conversion and metadata enrichment. Having the depth of experience will provide the combined client base with a full range of services to help reduce expenses while creating the digital-first experience most publishers need to compete in today’s evolving publishing industry. With this partnership in place Aptara clients can expect to see smoothly integrated options for taxonomies, and other metadata enhancement offered as a regular production flow service. Access Innovations clients can utilize significant additional options in production and distribution options.
https://www.accessinn.com ■ https://www.aptaracorp.com
Pinecone Systems Inc., a machine learning (ML) search infrastructure company, announced the release of a keyword-aware semantic search solution that enables accessible and advanced combination of semantic and keyword search results. “Vector search” allows companies to provide relevant results based on semantic, or similar meanings, as opposed to simple keyword-based searches. At the same time, keywords still matter in searches involving uncommon words like names or industry-specific terms. With few exceptions, companies have to choose between semantic search and keyword search, or running both systems in parallel.
Neither of these options is ideal. When companies choose one or the other, the results are not as complete as they could be, and when they run both systems in parallel and try to combine the results, cost and complexity goes up significantly. This technology can search across two data types — “dense vectors” generated by ML models to represent meaning, and “sparse vectors” generated by traditional keyword-ranking models such as BM25 — before automatically fusing everything into one ranked list of the most relevant results. The Pinecone hybrid search feature is available in beta.