Carlos H. Marcondes marcon@vm.uff.br

1. FROM SCIENTIFIC COMMUNICATION TO PUBLIC KNOWLEDGE: THE SCIENTIFIC ARTICLE WEB PUBLISHED AS A KNOWLEDGE BASEICCC 9th International Conference on electronic publishing – ElPub 2005Katholieke Universiteit Leuven, Belgium Carlos H. Marcondes marcon@vm.uff.br Information Science Department, Information Science Post-graduate Program, Universidade Federal Fluminense, Brazil Research funded by CNPq – the Brazilian Council for the Development of Science and Technology

2. PROBLEM • The role of scientific journals in scientific communication • The potentialities of IT has been applied to provide access to full-text documents • The scientific communication process depends on text production, reading, interpretation and citation • Web-published scientific journals are still based on the paper print publishing model

3. QUESTIONS • Can an author publish a scientific article both as a text and also in a machine readable format containing the new knowledge addressed by the article? • How would the structure of the new knowledge contained in a scientific article be? • What would be the consequences of this publication model to scientific communication and to the development of Science?

4. ASSUMPTIONS • Web publishing scientific articles can be new a cognitive tool • Growing availability of scientific Web ontologies • Science hasa formal method of reasoning: the Scientific Method • Scientific articles have a highly structured text format • The Semantic Web Initiative proposes machine readable documents that can be processed by software agents

5. THE HYPOTHESIS “The text of observational and experimental articles is usually (but not necessarily) divided into sections with the headings Introduction, Methods, Results, and Discussion. This so-called “IMRAD” structure is not simply an arbitrary publication format, but rather a direct reflection of the process of scientific discovery” (http://www.icmje.org) • Not only does the scientific article containa formal text structure (named here as“surface structure”),but it also contains a “deep structure”, composed of the Scientific Methodology elements, such as facts, statement of the problem, methodology, assumptions, contextual conditions, hypotheses, results, conclusions and citations

6. OBJECTIVE • To propose a model of the “deep structure” of scientific articles • This model will be the base to enhance authoring, validation and retrieval tools

7. PROPOSED MODEL – context / applications A IMPOR Eas kjjsd dj sdk skdkl skls a fd g gfg ggfgg g Researcher/author A IMPOR Eas kjjsd dj sdk skdkl skls a fd g gfg ggfgg g A IMPOR Eas kjjsd dj sdk skdkl skls a fd g gfg ggfgg g Authoring tool Other scientific articles Web published Semantic citations A IMPOR Eas kjjsd dj sdk skdkl skls a fd g gfg ggfgg g Semantic relations scientific article Web published - text “deep structure” – new knowledge Public knowledge bae/Web (ex: UMLS) Reading, retrieval, validating, annotating tools Researcher/referee/ reader

8. Previous knowledge Existing Theories Hypothetic-Deductive Method, based on Lakatos & Marconi (2004, p.75). 1 Gaps, contradictions, problems, new phenomena Assumptions, solutions or hypotheses Deductions, consequences Testing techniques Tests 2 Analysis of results Evaluation of the hypotheses Refusal Ratification New theory New gaps, contradictions, problems

9. Hypothetic-Deductive Method, based on Lakatos & Marconi, 2004, p.75. • step 1- facts or, more precisely, problematic facts; • step 2- formalization of a research problem or question; • step 3 - development of a hypothesis, which is an (temporary) answer to the research problem; • step 4- empirical testing of the hypothesis; • step 5 - analysis of the test results; • step 6 - conclusion: hypothesis ratification or refusal

10. <scientific_article_deep_structure> <fact>... </fact> ... (new phenomena) <problem> ... </problem> (question) <method> <methodology> ... </methodology> </method> <assumption> ... </assumption> <hypotheses> (provisory answer) <contextual_condition> ... </ contextual_condition > ... <cause> ... <link to knowledge base> ... <link to knowledge base> </cause> ... <consequence> ... <link to knowledge base> ... <link to knowledge base> </consequence> ... </hypotheses> <result> ... </result> ... (data resulting of controlled experiences or empirically collected – also a link to datasets of results) <conclusion> ... (hypotheses ratification or refusal) <link to knowledge base> ... <link to knowledge base> </conclusion> ... <citation> <bibliographic_reference> ... </bibliographic_reference> <link to bibliographic reference> ... </link to bibliographic reference> <reason_to_cite> ... </reason_to_cite> </citation> ... </scientific_article_ deep_structure>

11. RESEARCH DEVELOPMENT AGENDA • Empirical research: • analyze scientific articles in Health Science to validate the model • Theoretical research: • establish the relations between the different elements of the article’s deep structure • enhance the representation of the article’s deep structure: represent it as an ontology - RDF?, DAML+OIL?

12. REMAINING QUESTIONS • Is the “deep structure” common to all scientific areas? • What kind of inferences can be made based on a scientific article´s “deep structure” ? • Is a Scientific Methodology Markup Language – SmMLfeasible ?