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Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

Chaudhri VK, Elenius D, Goldenkranz A, Gong A, Martone ME, Webb W, Yorke-Smith N - J Biomed Semantics (2014)

Bottom Line: (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks?Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook.We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.

View Article: PubMed Central - PubMed

Affiliation: SRI International, Menlo Park, CA 94025 USA.

ABSTRACT

Background: Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?

Results: Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.

Conclusions: With some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels.

No MeSH data available.


Functions ofBiomembrane. The top half of this figure can be read as follows: every Biomembrane has a function to allow Move-Through of chemical entities that it is permeable to, and that this movement is through its Hydrophobic-Core, which is a region of its Phospholipid-Bilayer.
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Fig2: Functions ofBiomembrane. The top half of this figure can be read as follows: every Biomembrane has a function to allow Move-Through of chemical entities that it is permeable to, and that this movement is through its Hydrophobic-Core, which is a region of its Phospholipid-Bilayer.

Mentions: The numbers on some of the edges indicate cardinality constraints. For example, the instance of Phospholipid-Bilayer in Figure 1 has exactly two phospholipid layers that are in a has-region relationship to it. In Figure 2, we show the functions of a Biomembrane. A portion of this figure can be read analogously to Figure 1 as follows: for every instance of a Biomembrane, there exists a function Block in which the agent is a Hydrophobic-Core, the object is a Hydrophilic-Compound, and an instrument is a Fatty-Acid-Tail. More details about our representation of functions are available elsewhere [32].Figure 2


Comparative analysis of knowledge representation and reasoning requirements across a range of life sciences textbooks.

Chaudhri VK, Elenius D, Goldenkranz A, Gong A, Martone ME, Webb W, Yorke-Smith N - J Biomed Semantics (2014)

Functions ofBiomembrane. The top half of this figure can be read as follows: every Biomembrane has a function to allow Move-Through of chemical entities that it is permeable to, and that this movement is through its Hydrophobic-Core, which is a region of its Phospholipid-Bilayer.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4362633&req=5

Fig2: Functions ofBiomembrane. The top half of this figure can be read as follows: every Biomembrane has a function to allow Move-Through of chemical entities that it is permeable to, and that this movement is through its Hydrophobic-Core, which is a region of its Phospholipid-Bilayer.
Mentions: The numbers on some of the edges indicate cardinality constraints. For example, the instance of Phospholipid-Bilayer in Figure 1 has exactly two phospholipid layers that are in a has-region relationship to it. In Figure 2, we show the functions of a Biomembrane. A portion of this figure can be read analogously to Figure 1 as follows: for every instance of a Biomembrane, there exists a function Block in which the agent is a Hydrophobic-Core, the object is a Hydrophilic-Compound, and an instrument is a Fatty-Acid-Tail. More details about our representation of functions are available elsewhere [32].Figure 2

Bottom Line: (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks?Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook.We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.

View Article: PubMed Central - PubMed

Affiliation: SRI International, Menlo Park, CA 94025 USA.

ABSTRACT

Background: Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a below-introductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms?

Results: Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important.

Conclusions: With some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels.

No MeSH data available.