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Ontology-Based Classification of Molecules: a Logic Programming Approach

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We describe a prototype that performs structure-based classification of molecular structures. The software we present implements a sound and com- plete reasoning procedure of a formalism that extends logic programming and builds upon the DLV deductive databases system. We capture a wide range of chemical classes that are not expressible with OWL-based formalisms such as cyclic molecules, saturated molecules and alkanes. In terms of performance, a no- ticeable improvement is observed in comparison with previous approaches. Our evaluation has discovered subsumptions that are missing from the the manually curated ChEBI ontology as well as discrepancies with respect to existing subclass relations. We illustrate thus the potential of an ontology language which is suit- able for the Life Sciences domain and exhibits an encouraging balance between expressive power and practical feasibility.

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Ontology-Based Classification of Molecules: a Logic Programming Approach

  1. 1. O NTOLOGY-BASED C LASSIFICATION OF M OLECULES :A L OGIC P ROGRAMMING A PPROACH Despoina Magka Department of Computer Science, University of Oxford November 30, 2012
  2. 2. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge1
  3. 3. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge Hierarchical organisation of biochemical knowledge1
  4. 4. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge Hierarchical organisation of biochemical knowledge1
  5. 5. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge Hierarchical organisation of biochemical knowledge1
  6. 6. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge Hierarchical organisation of biochemical knowledge Fast, automatic and repeatable classification driven by Semantic technologies1
  7. 7. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge Hierarchical organisation of biochemical knowledge Fast, automatic and repeatable classification driven by Semantic technologies Web Ontology Language, a W3C standard family of logic-based formalisms1
  8. 8. B IOINFORMATICS AND S EMANTIC T ECHNOLOGIES Life sciences data deluge Hierarchical organisation of biochemical knowledge Fast, automatic and repeatable classification driven by Semantic technologies Web Ontology Language, a W3C standard family of logic-based formalisms OWL bio- and chemo-ontologies widely adopted1
  9. 9. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest2
  10. 10. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information2
  11. 11. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information caffeine is a cyclic molecule2
  12. 12. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information serotonin is an organic molecule2
  13. 13. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information ascorbic acid is a carboxylic ester2
  14. 14. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information Pharmaceutical design and study of biological pathways2
  15. 15. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information Pharmaceutical design and study of biological pathways ChEBI is manually incremented2
  16. 16. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information Pharmaceutical design and study of biological pathways ChEBI is manually incremented Currently ~30,000 chemical entities, expands at 3,500/yr2
  17. 17. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information Pharmaceutical design and study of biological pathways ChEBI is manually incremented Currently ~30,000 chemical entities, expands at 3,500/yr Existing chemical databases describe millions of molecules2
  18. 18. T HE C H EBI O NTOLOGY OWL ontology Chemical Entities of Biological Interest Dictionary of molecules with taxonomical information Pharmaceutical design and study of biological pathways ChEBI is manually incremented Currently ~30,000 chemical entities, expands at 3,500/yr Existing chemical databases describe millions of molecules Speed up growth by automating chemical classification2
  19. 19. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles3
  20. 20. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles E XAMPLE C C C C3
  21. 21. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) C C C C3
  22. 22. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) C C C C3
  23. 23. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) C C C C OWL-based reasoning support 1 Is cyclobutane a cyclic molecule? 3
  24. 24. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles 2 No minimality condition on the models hard to axiomatise classes based on the absence of attributes E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) C C C C OWL-based reasoning support 1 Is cyclobutane a cyclic molecule? 3
  25. 25. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles 2 No minimality condition on the models hard to axiomatise classes based on the absence of attributes E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) Oxygen C C C C OWL-based reasoning support 1 Is cyclobutane a cyclic molecule? 3
  26. 26. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles 2 No minimality condition on the models hard to axiomatise classes based on the absence of attributes E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) Oxygen C C C C OWL-based reasoning support 1 Is cyclobutane a cyclic molecule? 2 Is cyclobutane a hydrocarbon? 3
  27. 27. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles 2 No minimality condition on the models hard to axiomatise classes based on the absence of attributes E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) Oxygen C C C C3
  28. 28. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles 2 No minimality condition on the models hard to axiomatise classes based on the absence of attributes E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) Oxygen C C C C Required reasoning support 1 Is cyclobutane a cyclic molecule? 2 Is cyclobutane a hydrocarbon?3
  29. 29. E XPRESSIVITY L IMITATIONS OF OWL 1 At least one tree-shaped model for each consistent OWL ontology problematic representation of cycles 2 No minimality condition on the models hard to axiomatise classes based on the absence of attributes E XAMPLE Cyclobutane ∃(= 4)hasAtom.(Carbon ∃(= 2)hasBond.Carbon) Oxygen C C C C Required reasoning support 1 Is cyclobutane a cyclic molecule? 2 Is cyclobutane a hydrocarbon? 3
  30. 30. R ESULTS OVERVIEW 1 Expressive and decidable formalism for modelling complex objects: Description Graphs Logic Programs4
  31. 31. R ESULTS OVERVIEW 1 Expressive and decidable formalism for modelling complex objects: Description Graphs Logic Programs 2 Modelling that spans a wide range of structure-dependent classes of molecules4
  32. 32. R ESULTS OVERVIEW 1 Expressive and decidable formalism for modelling complex objects: Description Graphs Logic Programs 2 Modelling that spans a wide range of structure-dependent classes of molecules 3 Implementation that draws upon DLV and performs structure-based classification with a significant speedup4
  33. 33. R ESULTS OVERVIEW 1 Expressive and decidable formalism for modelling complex objects: Description Graphs Logic Programs 2 Modelling that spans a wide range of structure-dependent classes of molecules 3 Implementation that draws upon DLV and performs structure-based classification with a significant speedup 4 Evaluation over part of the manually curated ChEBI ontology revealed modelling errors4
  34. 34. R ESULTS OVERVIEW 1 Expressive and decidable formalism for modelling complex objects: Description Graphs Logic Programs 2 Modelling that spans a wide range of structure-dependent classes of molecules 3 Implementation that draws upon DLV and performs structure-based classification with a significant speedup 4 Evaluation over part of the manually curated ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off4
  35. 35. C LASSIFYING S TRUCTURED O BJECTS5
  36. 36. C LASSIFYING S TRUCTURED O BJECTS ascorbicAcid : 0 o 6 o c o o 5 c 11 c 1 c hasAtom h 2 12 10 7 single c c 13 double 9 8 4 o 3 o5
  37. 37. C LASSIFYING S TRUCTURED O BJECTS ascorbicAcid : 0 o 6 o c o o 5 c 11 c 1 c hasAtom h 2 12 10 7 single c c 13 double 9 8 4 o 3 o ascorbicAcid(x) →hasAtom(x, f1 (x)) ∧ . . . ∧ hasAtom(x, f13 (x)) o(f1 (x)) ∧ . . . ∧ c(f7 (x)) ∧ . . . ∧ single(f1 (x), f7 (x)) ∧ double(f7 (x), f2 (x)) ∧ . . .5
  38. 38. C LASSIFYING S TRUCTURED O BJECTS ascorbicAcid : 0 o 6 o c o o 5 c 11 c 1 c hasAtom h 2 12 10 7 single c c 13 double 9 8 4 o 3 o ascorbicAcid(x) →hasAtom(x, f1 (x)) ∧ . . . ∧ hasAtom(x, f13 (x)) o(f1 (x)) ∧ . . . ∧ c(f7 (x)) ∧ . . . ∧ single(f1 (x), f7 (x)) ∧ double(f7 (x), f2 (x)) ∧ . . . hasAtom(x, y1 ) ∧ hasAtom(x, y2 ) ∧ y1 = y2 → polyatomicEntity(x) ∧5 hasAtom(x, yi ) ∧ c(y1 ) ∧ o(y2 ) ∧ o(y3 )∧ i=1 c(y4 ) ∧ horc(y5 ) ∧ double(y1 , y2 )∧ single(y1 , y3 ) ∧ single(y3 , y4 ) ∧ single(y1 , y5 ) → carboxylicEster(x)5
  39. 39. C LASSIFYING S TRUCTURED O BJECTS ascorbicAcid : 0 o 6 o c o o 5 c 11 c 1 c hasAtom h 2 12 10 7 single c c 13 double 9 8 4 o 3 o Input fact: ascorbicAcid(a) Stable model: ascorbicAcid(a), hasAtom(a, af ) for 1 ≤ i ≤ 13, i o(af ) for 1 ≤ i ≤ 6, c(af ) for 7 ≤ i ≤ 12, h(af ), single(af , af ), i i 13 8 3 single(af , af ), single(af , af ) for i ∈ {5, 11}, single(af , af ), 9 4 12 i 11 6 single(af , af ) for i ∈ {1, 9, 11, 13}, single(af , af ) for i ∈ {1, 8}, 10 i 7 i double(af , af ), double(af , af ), horc(af ) for 7 ≤ i ≤ 13, 2 7 8 9 i polyatomicEntity(a), carboxylicEster(a), cyclic(a)5
  40. 40. C LASSIFYING S TRUCTURED O BJECTS ascorbicAcid : 0 o 6 o c o o 5 c 11 c 1 c hasAtom h 2 12 10 7 single c c 13 double 9 8 4 o 3 o Input fact: ascorbicAcid(a) Stable model: ascorbicAcid(a), hasAtom(a, af ) for 1 ≤ i ≤ 13, i o(af ) for 1 ≤ i ≤ 6, c(af ) for 7 ≤ i ≤ 12, h(af ), single(af , af ), i i 13 8 3 single(af , af ), single(af , af ) for i ∈ {5, 11}, single(af , af ), 9 4 12 i 11 6 single(af , af ) for i ∈ {1, 9, 11, 13}, single(af , af ) for i ∈ {1, 8}, 10 i 7 i double(af , af ), double(af , af ), horc(af ) for 7 ≤ i ≤ 13, 2 7 8 9 i polyatomicEntity(a), carboxylicEster(a), cyclic(a) Ascorbic acid is a cyclic polyatomic entity and a carboxylic ester5
  41. 41. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents6
  42. 42. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules6
  43. 43. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters6
  44. 44. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes6
  45. 45. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts6
  46. 46. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons6
  47. 47. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid6
  48. 48. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition6
  49. 49. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules6
  50. 50. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules Hydrocarbons6
  51. 51. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules Hydrocarbons Saturated molecules6
  52. 52. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules Hydrocarbons Saturated molecules 4 Cyclicity-related classes6
  53. 53. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules Hydrocarbons Saturated molecules 4 Cyclicity-related classes Benzenes6
  54. 54. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules Hydrocarbons Saturated molecules 4 Cyclicity-related classes Benzenes Cyclic molecules6
  55. 55. C HEMICAL C LASSES W E C OVERED 1 Existence of subcomponents Carbon molecules Carboxylic acids and carboxylic esters Ketones and aldehydes 2 Exact cardinality of parts Exactly two carbons Dicarboxylic acid 3 Exclusive composition Inorganic molecules Hydrocarbons Saturated molecules 4 Cyclicity-related classes Benzenes Cyclic molecules Alkanes6
  56. 56. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine7
  57. 57. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI7
  58. 58. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs7
  59. 59. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs Quicker than other approaches:7
  60. 60. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs Quicker than other approaches: [Hastings et al., 2010] 140 molecules in 4 hours [Magka et al., 2012] 70 molecules in 450 secs7
  61. 61. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs Quicker than other approaches: [Hastings et al., 2010] 140 molecules in 4 hours [Magka et al., 2012] 70 molecules in 450 secs Subsumptions exposed by our prototype:7
  62. 62. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs Quicker than other approaches: [Hastings et al., 2010] 140 molecules in 4 hours [Magka et al., 2012] 70 molecules in 450 secs Subsumptions exposed by our prototype: ascorbic acid is a polyatomic entity, a carboxylic ester and a cyclic molecule missing from the ChEBI OWL ontology7
  63. 63. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs Quicker than other approaches: [Hastings et al., 2010] 140 molecules in 4 hours [Magka et al., 2012] 70 molecules in 450 secs Subsumptions exposed by our prototype: ascorbic acid is a polyatomic entity, a carboxylic ester and a cyclic molecule missing from the ChEBI OWL ontology Contradictory subclass relation from ChEBI:7
  64. 64. E MPIRICAL E VALUATION Draws upon DLV, a deductive databases engine Evaluation with data extracted from ChEBI 500 molecules under 51 chemical classes in 40 secs Quicker than other approaches: [Hastings et al., 2010] 140 molecules in 4 hours [Magka et al., 2012] 70 molecules in 450 secs Subsumptions exposed by our prototype: ascorbic acid is a polyatomic entity, a carboxylic ester and a cyclic molecule missing from the ChEBI OWL ontology Contradictory subclass relation from ChEBI: Ascorbic acid is asserted to be a carboxylic acid (release 95) Not listed among the subsumptions derived by our prototype7
  65. 65. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects8
  66. 66. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes8
  67. 67. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup8
  68. 68. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors8
  69. 69. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off8
  70. 70. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax8
  71. 71. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax ∧5 hasAtom(x, yi ) ∧ c(y1 ) ∧ o(y2 ) ∧ o(y3 ) ∧ c(y4 )∧ i=1 double(y1 , y2 ) ∧ single(y1 , y3 ) ∧ single(y3 , y4 ) ∧ single(y1 , y5 ) → carboxylicEster(x)8
  72. 72. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax define carboxylicEster some hasAtom SMILES(COC(= O)[∗]) end.8
  73. 73. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes8
  74. 74. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes E.g., Carboxylic ester is an organic molecular entity8
  75. 75. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes Extensions with numerical datatypes8
  76. 76. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes Extensions with numerical datatypes E.g., Small molecules if they weigh less than 800 daltons8
  77. 77. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes Extensions with numerical datatypes Classification of complex biological objects8
  78. 78. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes Extensions with numerical datatypes Classification of complex biological objects Integration with Protégé, Bioclipse, JChemPaint,. . .8
  79. 79. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes Extensions with numerical datatypes Classification of complex biological objects Integration with Protégé, Bioclipse, JChemPaint,. . . Mapping from our logic to RDF8
  80. 80. C ONCLUSION AND F URTHER R ESEARCH Results 1 Expressive and decidable formalism for complex objects 2 Wide range of structure-based classes 3 DLV-based implementation exhibits a significant speedup 4 Evaluation over ChEBI ontology revealed modelling errors Language for representing biochemical structures with a favourable performance/expressivity trade-off Future directions SMILES-based surface syntax Detect subsumptions between classes Extensions with numerical datatypes Classification of complex biological objects Integration with Protégé, Bioclipse, JChemPaint,. . . Mapping from our logic to RDF Thank you! Questions?!?8

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