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.
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.
Elastic, the company behind Elasticsearch, announced enhancements to its cross-cluster search and cross-cluster replication capabilities with interoperability between self-managed deployments and Elastic Cloud, now generally available. Customers can seamlessly search data across multiple Elasticsearch clusters deployed on-premises, on Kubernetes, and in the cloud.
Cross-cluster search enables users to search across multiple clusters and visualize data in one coherent view for deeper insights. Cross-cluster replication allows customers to replicate data between clusters regardless of their physical location (cloud or on-premises) and deployment model. Features include:
- Streamlining workflows with a single user interface to search and replicate data between Elasticsearch clusters regardless of environment—on-premises, public cloud, hybrid, and multi-cloud.
- Enabling customers to minimize risk and increase operational efficiency while retaining complete visibility of their data throughout the gradual migration of on-premises workloads to the cloud.
- Optimizing customers’ ability to troubleshoot production applications, analyze security events, and manage where their sensitive data resides.
- Improving disaster recovery scenarios where data redundancy and business continuity are critical, while increasing service resilience and lowering latency.
IBM released an open-sourced part of the IBM Deep Search Experience in a new toolkit, Deep Search for Scientific Discovery (DS4SD), for scientific research and businesses with the goal of spurring on the rate of scientific discovery.
To help achieve this goal, we’re now publicly releasing a key component of the Deep Search Experience, our automatic document conversion service. It allows users to upload documents in an interactive fashion to inspect a document’s conversion quality. DS4SD has a simple drag-and-drop interface, making it very easy for non-experts to use. We’re also releasing deepsearch-toolkit, a Python package, where users can programmatically upload and convert documents in bulk.
Deep Search uses AI to collect, convert, curate, and ultimately search huge document collections for information that is too specific for common search tools to handle. It collects data from public, private, structured, and unstructured sources and leverages state-of-the-art AI methods to convert PDF documents into easily decipherable JSON format with a uniform schema that is ideal for today’s data scientists. It then applies dedicated natural language processing and computer vision machine-learning algorithms on these documents and ultimately creates searchable knowledge graphs.
Tellius announced a partnership with Databricks to give joint customers the ability to run Tellius natural language search queries and automated insights directly on the Databricks Lakehouse Platform, powered by Delta Lake, without the need to move any data.
With Tellius, organizations can search and analyze their data to identify what is happening with natural language queries, understand why metrics are changing via AI-powered Insights, and determine next best actions with deep insights and AutoML. Connecting to Delta Lake on Databricks only takes a few clicks, and then users can perform a natural language search of their unaggregated structured and unstructured data to answer their own questions. They can drill down to get granular insights, leverage single-click AI analysis to uncover trends, key drivers, and anomalies in their data, and create predictive models via AutoML in Tellius. Answers and insights can be utilized to write back to source applications to operationalize insights. Faster data collaboration helps democratize data access across analytics teams with less worrying about performance or IT maintenance.
Enterprise search provider Sinequa announced the addition of advanced neural search capabilities to its Search Cloud Platform, for better relevance and accuracy to enterprises. As an optional capability of Sinequa’s Search Cloud platform, Neural Search uses four deep learning language models. These models are pre-trained and ready to use in combination with Sinequa’s Natural Language Processing (NLP) and semantic search.
Sinequa optimized the models and collaborated with the Microsoft Azure and NVIDIA AI/ML teams to deliver a high performance, cost-efficient infrastructure to support intensive Neural Search workloads without a huge carbon footprint. Neural Search is optimized for Microsoft Azure and the latest NVIDIA A10 or A100 Tensor Core GPUs to efficiently process large amounts of unstructured data as well as user queries.
Sinequa’s Neural Search improves relevance and is often able to directly answer natural language questions. It does this with deep neural nets that go beyond word-based search to better leverage meaning and context. Sinequa’s Search Cloud platform combines neural search with its extensive NLP and statistical search. This unified approach provides more accurate and comprehensive search results across a broader range of content and use cases.
Algolia, an API-First Search & Discovery platform, unveiled additional AI (artificial intelligence) models and capabilities in its Recommend Spring Release 2022. Algolia Recommend introduces AI models powered by behavioral insights. When coupled with Algolia’s fast indexing capabilities, customers are able to immediately put their most relevant and up to date content into motion for end-users. From a single dashboard, merchandisers, digital content managers, or digital business leaders can choose the model that is right for them, deploy it, and track the results. The release includes:
- Popular Trends – AI models that detects emerging trends based on users’ behavioral data as they interact with various brands, categories of products and content, and topics of interest.
- Business Rules – Low-code/no-code functionality for controlling AI and activating unique business strategies. This provides greater flexibility for category merchandisers, online retail strategists, and content specialists to generate new recommendations.
- Hybrid Recommend Engine – This is a combination of collaborative filtering algorithms and content-based filtering algorithms to increase the relevancy and accuracy of recommendations. Recommendations can be presented to users as the content-based data is indexed. Availability of behavioral information either at this initial stage or later can further help fine-tune and enrich the quality of recommendations.
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