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The document discusses challenges with digital libraries and semantic support. It notes that human information processing is limited and computers do not understand natural language queries. Semantic support adds metadata to information to help humans and computers work together by giving meaning to data. However, communication between digital libraries remains a challenge. While some projects have created ontologies and cross-concordances, researchers often prefer to add their own tags due to difficulties making comprehensive ontologies for research data content. The literature on these topics is very technical and may not effectively communicate with scholars.
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Editor's Notes
- Literature: we searched for example LISTA, a bibliographical database for library and information sciences, Sociological Abstracts, for social sciences, and
- Print editions of scholarly journals are becoming a ‘thing of the past’ But, distributed in different digital libraries: we have more than 300 databases, ranging from full-text (Sage), biographical (PubMed), research data etc, repositories, scattered among university, individual open access journals. Where do you start your search?
- And when you search in Google Scholar or sEURch, which are integrated search engines, covering a lot of databases, you get so many results! You can’t read them all!
- So there are a number of problems. Human information processing capacity is according to some just 300 kb per day! Computers can’t think for us, they use their own language
- What do researchers want, or what would be the ideal situation? We want to find what’s relevant but with as less irrelevant articles as possible: Part A should be as big as possible, and Part B and D as little as possible. (picture is borrowed from http://choo.fis.utoronto.ca/FIS/Courses/LIS1325/Strategy.html)
- When you search with the term mouse the relevant articles will be different among scholars from different disciplines.