Combining and merging multiple data sources & analytics of multiple content analysis and document analysis tools
Data enrichment is done by modular document analysis, content analysis or data enrichment plugins managed with our lightweight, flexible, extendable and interoperable open source ETL (extract, transform, load) and data enrichment framework and toolset for document processing, automated content analysis and media analysis, merge and data enrichment pipelines.
Document processing, data integration, content analysis, content enrichment and data enrichment pipeline (Enhancement chain)
The document processing pipeline or chain is a list of data enrichment plugins (enhancer), which will be runned for each document to enrich, analyse or link them with additional data or analysis.
A part of the default document processing pipeline configuration is for example:
- Extract text (enhance-text)
- OCR images (enhance-ocr)
- Adding annotations and tags (enhance-rdf)
- Indexing to database and/or search index (f.e. Solr or Elastic Search)
Semantic data enrichment plugins
Such modular data enrichment plugins (enhancer) will enhance or enrich the content with additional meta data or analytics.
You can analyze your data with internal webservices (or if you don't need privacy with external webservices or "the cloud") or with your favorite programming language in a custom data enrichment plugin.
Configuration of a custom document processing, content analysis and data enrichment pipeline
The pipeline or the enabled plugins are configurated for all data sources in /etc/opensemanticsearch/connector or can be overwritten or extended for each data source or connector in their specialized configs like connector-files
The analysis chain is running in order, since some plugins depend on data analysis of other plugins.
You can add additional or new data enrichment plugins to
Or you overwrite this config option to define a custom data enrichment pipeline with only a few needed plugins.
Enrich parts, enrich later, add additional enrichments, update data enrichments or distributed data enrichment
opensemanticsearch-enrich can run data enrichment parts or plugins which are not enabled in your default document processing pipe later or from time to time.
For example sometimes its better to index all documents without OCR in short time and after that to do the OCR of the documents with images which will need long time. So the users are able to search in most documents and text, not having to wait until only few parts and only for a few documents like some text in images are recognized in a long time process first before other documents after them were indexed, which takes only very few time, because there are no images.
Or you can do expensive data enrichment like OCR at night or on low server load or distribute this work on different processors (parallel processing) or servers (cluster) or web services (cloud).
Another possibility is to enrich with tools or webservices that imporoved or updated their results because of better analytics quality or more available data from time to time to integrate newer data or analytcs results.
Or to enrich later with a additional webservice, without to have to run the full document processing chain again.
Or if a webservice was not available while indexing to enrich data with its analytics later.
You can run the tool from REST-API or on the command line:
opensemanticsearch-enrich --plugins pluginname
Optional you can add a search query or filter query, so only the interesting or important data or document(s) will get enriched:
opensemanticsearch-enrich --plugins pluginname query
Integration of other frameworks for data integration for data warehouse or for extraction, transformation and load (ETL)
There are powerfull open source ETL frameworks (extraction, transformation and load) for data integration, mapping, filtering and transformation for data warehousing with powerful features and graphical user interfaces (GUI).
Linked Data with open Semantic Web Standards
Modern Semantic Web and Linked Data standard formats help you to integrate linked open data to improve data analysis, navigation and filtering.
Enrich your data with free databases and Open Data
So you can enrich your data with many free knowledge bases like WikiData (the structured database of Wikipedia) to use its lists of names or structures for analytics or import a Wiktionary as a thesaurus to find more verb forms especially of irregular verbs or synonyms.