Text & Data Mining
Overview of text and data mining
• Text and data mining concerns the automatic processing of large collections of various forms of data and information to identify, organise and perform analysis in order to determine possible links within the content that may not be obvious on initial inspection.
• There are various methods to perform this processing, but there are elements common to all methods, including an automated way to process all sizes and types of content in which to identify relevant information, facilitate its extraction and its analysis.
• Content mining has links to semantic technology as it focuses on the interlinks and contextual commonalities to enhance the understanding of the content.
• The development of these mining approaches are of particular importance within the scientific community to drive the interdisciplinary nature of research and support new areas of discovery.
Elsevier’s principles on text and data mining
• Elsevier wants to support our customers to advance science and health.
• We want to help them realise the maximum benefit from our content and enhance insight and understanding through content mining.
• Our journals and books have added value - we invest in quality content and enrich content to maximise discoverability and usability.
• We believe a transparent content mining policy framework is essential, which needs efficient implementation and flexibility to cover multiple scenarios.
• The framework of open innovation enables and facilitates application development within our content.
• Elsevier will continue to manage its content in modern digital formats that facilitate the easy access, use, and re-use of content.
Our approach to providing text and data mining
• Elsevier is receiving an increasing number of content mining requests and we are developing solutions to meet customer needs. We are doing this because we realise that researchers and organisations which to derive even more value from our content, but in a way that they choose. Consequently we have adapted our policies to this primary goal.
• We wish to understand our customers’ text mining requirements and as practically every content mining request has a different goal and there is not a common solution to provide this. Consequently we request that customers looking to mine our content should speak to their Elsevier Account Manager or should contact us directly at firstname.lastname@example.org
• We will then discuss the mining request, access to the content (see below), licensing and (where applicable) pricing for the project.
• Mining requests are often content specific. Customers can choose to mine our full-text content, abstracts, data and other materials. A charge may be applicable dependent on the request.
• Common requests for Content Mining include:
Running extensive searches and using locally loaded content for text mining purposes for research.
Extraction of semantic entities from Elsevier content for the purpose of recognition and classification of the relations between them.
Performing extensive mining operations on subscribed content, including structuring input text, deriving patterns within this text and evaluation and interpretation of the output.
Customers can integrate results on a server used for the subscriber’s own mining system for access and use by its researchers through the subscriber’s internal secure network.
• All commercial usage of content mining results arising from Elsevier content will be subject to licensing and will be chargeable. We will discuss the utilisation of results in accordance to each request.
Facilitating access & technology to empower text and data mining
Elsevier have developed several different methods to allow customers to mine our content. This provides maximum flexibility and multiple options to access the required content. Examples of this include methods to deliver high amounts of content on demand, API access and other solutions associated with specific content types. For example:
• ScienceDirect and Scopus licence agreements – subscribers to these products may have options to search, download, email and extract content to allow them to perform their requisite analyses
• Application Marketplace – Enabling developers who wish to design and implement applications to analyse our content, or who may wish to test applications as part of their research within Elsevier content. For further information on SciVerse Applications, please visit http://www.info.sciverse.com/sciverse-applications