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OpenMinteD Project - building a TDM infrastructure
Building an Open
Text and Data Mining
• Stelios Piperidis
• Institute for Language & Speech Processing
• Athena Research & Innovation Centre
● > 1,08 billion websites and 3,46 billion internet users, on 25 September 2016.
● > 24 million wireless sensors and actuators worldwide (553% up, between 2011 and
● > 16 zettabytes of useful data (16 Trillion GB) by 2020.
● YouTube claims to upload 24 hours of video every minute, making the site a hugely
significant data aggregator.
● “Every second, on average, around 6,000 tweets are tweeted on Twitter, which
corresponds to over 350,000 tweets sent per minute, >500 million tweets per day
and around 200 billion tweets per year”.
● 74,200,000 pages existed on Facebook, with 7 million apps and websites integrated
with Facebook on 30/5/2016.
The global research community generates over 1.5 million new scholarly
articles per annum.
e STM report (2009)
… some 90% of papers … are never cited.
… 50% of papers are never read by anyone other than their authors,
referees and journal editors
… one paper published every 30 seconds
… 70,000 papers published on a single protein, the tumor suppressor p53
e STM report (2009)
process textual sources, organise and classify in various dimensions, extract
main (indexical) information items
identify and extract entities and relations between entities, facilitate the
transformation of unstructured textual sources into structured data
enable the multidimensional analysis of structured data to extract meaningful
insights and improve the ability to predict
Establish an open and sustainable Text and Data
Mining (TDM) platform and infrastructure where
researchers can collaboratively create, discover, share
and re-use knowledge from a wide range of text based
scientific and scholarly related sources.
Text Mining Researchers
End UsersComputing Infrastructures
VALUE ADDED APPS
Via standardised programmatic
interfaces and access rules
discoverable text mining services
and workflows which process,
analyse and annotate text
Operate on public e-Infrastructures
via standarized APIs
Different scientific communities
have different challenges
Community-driven applications to
illustrate the value of the
infastructure. Engage with industry.
From the very beginning…
Requirements, content, barriers, expected outcomes.
… to the very end
Create applications, validate and evaluate the results.
• Document literature content, language/knowledge resources, data categories taxonomies,
• Document language processing/text mining services and workflows
• Generic and domain-specific metadata descriptions
• Combine services into workflows
• Combine content and language resources with services and workflows
• Combine automatic and manual/crowdsourcing annotation services
• Study IPR restrictions for reuse of sources as well as possible exceptions
• Promote clarity and standardisation of legal rights and obligations
• Translate the legal & policy aspects into specifications for lawful user-to-service and
documenting, depositing, managing, publishing and sharing scientific content and
data, text and data mining software tools, services and workflows, language and
to enable both technically but also legally the linking and pipelining of text mining
tools, services and workflows, as well as language and knowledge resources
automatic analysis, annotation and extraction of important information out of
composing, scheduling and orchestrating new processing workflows by combining
existing text mining services and language/knowledge resources
services for advising on lawful use and combination of content, language resource
and text mining services
1. End users
- Researchers, data base curators, …
- Novice: use services to advance their science
- Advanced: use TDM services into complex workflows
2. Content and service providers
- Publishers, libraries, scientific data base centres, …
- TDM researchers