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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.


A simplified view of the structure ofBiomembranerepresented in AURA. The Biomembrane node (shown in white) is universally quantified, and every other node (shown in gray) is existentially quantified. We can read a portion of this figure as follows: for every instance of Biomembrane, there exists an instance of Phospholipid-Bilayer and an instance of Glycoprotein that are in has-part relationship to it, and further the instance of Glycoproteinis-inside the instance of Phospholipid-Bilayer. The usage of has-part and other relationships corresponds to instance-instance relationships [33].
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Fig1: A simplified view of the structure ofBiomembranerepresented in AURA. The Biomembrane node (shown in white) is universally quantified, and every other node (shown in gray) is existentially quantified. We can read a portion of this figure as follows: for every instance of Biomembrane, there exists an instance of Phospholipid-Bilayer and an instance of Glycoprotein that are in has-part relationship to it, and further the instance of Glycoproteinis-inside the instance of Phospholipid-Bilayer. The usage of has-part and other relationships corresponds to instance-instance relationships [33].

Mentions: As an illustration of the use of CLIB, in Figure 1, we show a simplified representation of the structure of a Biomembrane. From the representational point of view, the graph in Figure 1 represents an existential rule of the sort seen in axioms 1 and 2. In this figure, the node shown in white is universally quantified, and every other node, shown in gray, is existentially quantified. Therefore, we can read a portion of Figure 1 as follows: for every instance of Biomembrane, there exists an instance of Phospholipid-Bilayer and an instance of Glycoprotein that are in has-part relationship to it, and further the instance of Glycoproteinis-inside the instance of Phospholipid-Bilayer. In the context of the relationships used in biomedical ontologies, our usage of has-part and other relationships corresponds to instance-instance relationships [33]. The arrows go from the first argument of a predicate to the second argument. For example, an arrow from Biomembrane to a Phospholipid-Bilayer labeled as has-part corresponds to the predicate has-part(b,p), where b is an instance of a Biomembrane, and p is an instance of a Glycoprotein.Figure 1


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)

A simplified view of the structure ofBiomembranerepresented in AURA. The Biomembrane node (shown in white) is universally quantified, and every other node (shown in gray) is existentially quantified. We can read a portion of this figure as follows: for every instance of Biomembrane, there exists an instance of Phospholipid-Bilayer and an instance of Glycoprotein that are in has-part relationship to it, and further the instance of Glycoproteinis-inside the instance of Phospholipid-Bilayer. The usage of has-part and other relationships corresponds to instance-instance relationships [33].
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig1: A simplified view of the structure ofBiomembranerepresented in AURA. The Biomembrane node (shown in white) is universally quantified, and every other node (shown in gray) is existentially quantified. We can read a portion of this figure as follows: for every instance of Biomembrane, there exists an instance of Phospholipid-Bilayer and an instance of Glycoprotein that are in has-part relationship to it, and further the instance of Glycoproteinis-inside the instance of Phospholipid-Bilayer. The usage of has-part and other relationships corresponds to instance-instance relationships [33].
Mentions: As an illustration of the use of CLIB, in Figure 1, we show a simplified representation of the structure of a Biomembrane. From the representational point of view, the graph in Figure 1 represents an existential rule of the sort seen in axioms 1 and 2. In this figure, the node shown in white is universally quantified, and every other node, shown in gray, is existentially quantified. Therefore, we can read a portion of Figure 1 as follows: for every instance of Biomembrane, there exists an instance of Phospholipid-Bilayer and an instance of Glycoprotein that are in has-part relationship to it, and further the instance of Glycoproteinis-inside the instance of Phospholipid-Bilayer. In the context of the relationships used in biomedical ontologies, our usage of has-part and other relationships corresponds to instance-instance relationships [33]. The arrows go from the first argument of a predicate to the second argument. For example, an arrow from Biomembrane to a Phospholipid-Bilayer labeled as has-part corresponds to the predicate has-part(b,p), where b is an instance of a Biomembrane, and p is an instance of a Glycoprotein.Figure 1

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.