Biotea-Biolinks: A semantic infrastructure for exploring and analyzing scientific publications

Castro, G. S. A.; Jael, Leyla

doi:10.6035/14028.2017.513968

Cited by 1 publication

(3 citation statements)

References 48 publications

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“…As such, the content of scientific articles of today is not accessible to algorithms. Methods of semantic interlinking and metadata enrichment of scientific articles exist, especially in life sciences [12,17,18,39], but they are mostly about annotating the existing natural language text in a relatively shallow manner. Other successful applications of semantic technologies focus only on the metadata level, such as provenance representation and versioning of scientific works [17].…”

Section: Introductionmentioning

confidence: 99%

“…While technologies like RDF and OWL grounded in first-order logic are now mature and well-tested, full semantic representation of natural language has remained a task that is too complex for automated approaches. Even the extraction of simpler RDF-based structures from the content of scientific articles typically requires manual curation to achieve sufficient level of quality [3,12,20]. In fact, even for knowledge representation experts it is very challenging to fully represent typical high-level scientific findings in formal logic, as no general scheme or procedure exists that is known to apply to findings across specific fields.…”

Section: Introductionmentioning

confidence: 99%

“…Based on these Semantic Web technologies, there are various approaches that try to automatically extract such semantic information from articles to make them more machine-readable, covering the metadata [18] or the article structure [11], providing summarizations of the main concepts and ideas (human assisted and curated) [20], using annotations to semantically enhance articles [11,17,39] or semantically interlink them [12], and describing bibliographic references and citations [35]. On the one hand, we have rich vocabularies and ontologies like the SPAR ontology suite [34] that support the classical publishing process starting from formal descriptions of the structure of articles and article discourses [4], to the traditional publishing workflows [6], to accounting for all roles involved in the publishing process [22], and even to the current imperfect peer-reviewing process [37].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Expressing High-Level Scientific Claims with Formal Semantics

Bucur

Kuhn

Ceolin

et al. 2021

Proceedings of the 11th Knowledge Capture Conference

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The use of semantic technologies is gaining significant traction in science communication with a wide array of applications in disciplines including the life sciences, computer science, and the social sciences. Languages like RDF, OWL, and other formalisms based on formal logic are applied to make scientific knowledge accessible not only to human readers but also to automated systems. These approaches have mostly focused on the structure of scientific publications themselves, on the used scientific methods and equipment, or on the structure of the used datasets. The core claims or hypotheses of scientific work have only been covered in a shallow manner, such as by linking mentioned entities to established identifiers. In this research, we therefore want to find out whether we can use existing semantic formalisms to fully express the content of high-level scientific claims using formal semantics in a systematic way. Analyzing the main claims from a sample of scientific articles from all disciplines, we find that their semantics are more complex than what a straight-forward application of formalisms like RDF or OWL account for, but we managed to elicit a clear semantic pattern which we call the "super-pattern". We show here how the instantiation of the five slots of this super-pattern leads to a strictly defined statement in higher-order logic. We successfully applied this super-pattern to an enlarged sample of scientific claims. We show that knowledge representation experts, when instructed to independently instantiate the super-pattern with given scientific claims, show a high degree of consistency and convergence given the complexity of the task and the subject. These results therefore open the door on the longer run for allowing researchers to express their high-level scientific findings in a manner they can be automatically interpreted. This in turn will allow for automated consistency checking, question answering, aggregation, and much more.

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