TASKS
CHALLENGE(S)
ENTITYZE
DIFFERENCE
CLEAN
To clean text data, you have to solve the twin challenges of high volume and high cardinality (dimensions).
Entityze separates signal from noise in the data with no manual input and unlike most tools, it offers several customizable levels of data aggregation.
NORMALIZE
The meaning of text can often be ambiguous. Does "Washington" refer to the Northwest Pacific state, the federal US capital or the first President of the US?
Entityze analyzes and understands context to remove text data ambiguity with no manual input. It is both more accurate and more extensive than other tools.
ENRICH
With most tools, entity linking stops at the normalization step. You have to develop your own code or ML models to associate data and features to an entity.
Entityze's built-in knowledge graph automatically connects the dots with both generic real-world and industry-specific information. You can easily add multiple custom industry- or company-specific ontologies.
TASKS
CHALLENGE(S)
ENTITYZE DIFFERENCE
ORGANIZE DATA
As every data scientist knows all too well, a disproportionate amount of time has to be devoted to data preparation. This is particularly true when dealing with unstructured data like text.
No need for custom code and tedious data wrangling, you can start on your model faster. And if you integrate Entityze in your standard data pipeline, your text is already pre-labeled and categorized before any project starts.
EXPLORE DATASETS
The complexity of the exploration step in any data science project is highly dependent of the diversity of the data. It's extremely difficult to generate meaningful clusters from raw text.
Entityze gives you a meaning-based, structured view of the text data that makes it a breeze to generate groupings and buckets. You can automatically visualize the data as a graph,, with built-in meaning aggregation levels that let you zoom in and out with a click.
LABEL DATA
Manual labeling of text data is a tedious process with ample room for input error or discrepancy. It's also difficult to leverage existing labels from other projects, as the data organization and labeling is often specific to a corpus of documents.
Entityze automates the data labeling process and provides a universal, generic meaning framework. You can integrate your own specific vocabulary on top of the Entityze knowledge graph. You can also add business rules that leverage the Entityze labels to automatically generate your own custom labels.
TASKS
CHALLENGE(S)
ENTITYZE DIFFERENCE
UNIFY
While data catalogs provide a one-stop shop for disparate sources of structured data, there are many limitations around text data. Data catalogs mainly address the 20% of corporate data that is already structured.
Entityze Meta generates light, structured metadata for text documents that directly integrates with the operating standards of mainstream data catalog solutions. No need to change your existing tools and processes.
DISTRIBUTE
In the age of data lakes, data that is not cataloged remains invisible and cannot be analyzed. In fact, less than 2% of all data created ever gets analyzed.
With Entityze Meta, data engineers can unlock a vast trove of untapped unstructured data on behalf of the data analysis and data science teams.
SECURE
Widening access to text data across an organization brings an additional layer of information security and confidentiality challenges.
With the information generated by Entityze Meta, you can define simple access rules that go beyond authorship. You can restrict access based on content and meaning, and you can also leverage the built-in multiple granularity levels to aggregate data differently for different people.
TASKS
CHALLENGE(S)
ENTITYZE DIFFERENCE
SIMPLIFY
QUERIES
Users are often required to combine multiple keywords to narrow down search relevant results to a manageable volume. This often results in a game of trial and error for the user.
Entityze Search produces meaning and context information that can be added to your enterprise search index. A search query with a combination of keywords can be easily converted into a search query with a combination of meanings, reducing the back and forth.
IMPROVE
RESULTS
Keyword-based search scores and ranks results based on keyword frequency and proximity. It’s not uncommon to see highly ranked results that contain the right combination of keywords, yet are otherwise irrelevant to the user.
Entityze search produces meaning information (semantic layer) that can be used to rank and filter search results. It’s akin to running two different searches (one for keywords and one for meaning), and then combining the two set of results.
Information Technology teams can spend a lot of time and effort creating and fine-tuning the synonym lists and custom vocabulary that will be added to an enterprise search engine.
Entityze Search accelerates the deployment of semantic search and enables customization at team or user level, rather than at enterprise level.
