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Towards an Ontology of Philosophy

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We can distinguish two families of approaches to the building of ontologies -- corresponding roughly to the contrast between 'neats' and 'scruffies' in artificial intelligence research. We describe the implications of each approach for the building of an ontology of philosophy, focusing especially on the Indiana Philosophy Ontology (InPhO) project led by Colin Allen.

A video presentation based on these slides is available here: https://www.youtube.com/watch?v=5HV3M0NvyPM

Publié dans : Sciences
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Towards an Ontology of Philosophy

  1. 1. Towards an Ontology of Philosophy Barry Smith http://ontology.buffalo.edu/smith APA, Vancouver, April 2, 2015
  2. 2. World’s most successful ontology
  3. 3. “Siri: An Ontology-driven Application for the Masses”, A. Cheyer and T. Gruber (2010) 3
  4. 4. 4 Aristotle's Ontology of Constitutions World’s oldest ontology
  5. 5. The problem these ontologies were built to solve You have a lot of data / literature The data is described in heterogeneous ways You need to access and reason with the data in a uniform way 1. Create a controlled vocabulary of preferred labels for describing the data 2. Provide logical (computable) definitions 3. Tag (‘semantically enhance’) the data with ontology term URIs
  6. 6. Ontology-based methodology of information-driven science Most successful example: the Gene Ontology
  7. 7. Old biology data 7
  8. 8. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSF YEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFV EDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLF YLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIV RSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDT ERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNF GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRL RKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVA QETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTD YNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFN HDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYAT FRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYES ATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQ WLGLESDYHCSFSSTRNAEDVDISRIVLYSYMFLNTAKGCLVEYA TFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYE SATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWI New biology data 8
  9. 9. How to do biology across the genome? MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVIS VMVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLER CHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERL KRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVC KLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGIS LLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWM DVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSR FETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVM KVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISV MVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERC HEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLK RDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCK LRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLL AFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMD VVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRF ETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVMK VSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVM VGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCH EIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKR DLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKL RSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLL AFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMD VVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRF ETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVMK VSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVM VGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCH EIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKR 9
  10. 10. how to link the kinds of phenomena represented here 10
  11. 11. or here 11
  12. 12. or here 12
  13. 13. MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRK RSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSL FYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLL HVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNF GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLD IFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTDY NKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDPNQVTNRDIS RIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESA TSELMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVV AGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEFRDLHQLRLCYEIYMADTPSVAVQA PPGYGKTELFHLPLIALASKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPVRNFIEEGYDGVTDL YVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQSQFGGITNLDFDAFEK AIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRSEDLSRGLSSYPTRMFNLIKEKSEVPLGHVHKI RKKVESQPEEALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVVWIHGKLGAAEKVSRTKE FVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDGGLCYLLSRKNSWAARNRKG ELPPKEGCITEQVREFYGLESKKGKKGQHVGCCGSRTDLSADTVELIERMDRLAEKQATASMSIVAL PSSFQESNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTASTNVRTNATTNASTNATTNASTN ASTNATTNASTNATTNSSTNATTTASTNVRTSATTTASINVRTSATTTESTNSSTNATTTESTNSSTNA TTTESTNSNTSATTTASINVRTSATTTESTNSSTSATTTASINVRTSATTTKSINSSTNATTTESTNSNT NATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTNSNTSATTTESTNASAKEDANKDG NAEDNRFHPVTDINKESYKRKGSQMVLLERKKLKAQFPNTSENMNVLQFLGFRSDEIKHLFLYGIDIYF CPEGVFTQYGLCKGCQKMFELCVCWAGQKVSYRRIAWEALAVERMLRNDEEYKEYLEDIEPYHGDP VGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTRGKQGSQVFRMSGRQIKELYFKVWSNL RESKTEVLQYFLNWDEKKCQEEWEAKDDTVVVEALEKGGVFQRLRSMTSAGLQGPQYVKLQFSRH HRQLRSRYELSLGMHLRDQIALGVTPSKVPHWTAFLSMLIGLFYNKTFRQKLEYLLEQISEVWLLPHW LDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCRDSSREVGELIGLFYNKTFRQKLE YLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCRDSSREVG ELIGLFYNKTFRQKLEYLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDG 13 to this?
  14. 14. 14 or this?
  15. 15. answer: by tagging data with terms from a controlled vocabulary such as the Gene Ontology 15 sphingolipid transporter activity Holliday junction helicase complex age-dependent behavioral decline
  16. 16. MouseEcotope GlyProt DiabetInGene GluChem sphingolipid transporter activity such tagging allows virtual integration of heterogeneous databases 16
  17. 17. MouseEcotope GlyProt DiabetInGene GluChem Holliday junction helicase complex 17 fosters discoverability of information in heterogeneous databases
  18. 18. Figure 3. Shotton D, Portwin K, Klyne G, Miles A (2009) Adventures in Semantic Publishing: Exemplar Semantic Enhancements of a Research Article. PLoS Comput Biol 5(4): e1000361. doi:10.1371/journal.pcbi.1000361 http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000361 … allows tagging of literature RB Reis, GS Ribeiro, RDM Felzemburgh, et al., Impact of Environ- ment and Social Gradient n Leptospira Infection in Urban Slums
  19. 19. coordinated tagging of literature and data
  20. 20. Ontology journals 20
  21. 21. 21
  22. 22. Ontology portals 22
  23. 23. Ontology portals 23
  24. 24. Ontology authoring and editing software 24 http://protege.stanford.edu/
  25. 25. Ontologies in domains relevant to philosophy and cognitive science Mental Functioning Ontology (MFO) Ontology for Biomedical Investigations (OBI) – philosophy of science Basic Formal Ontology – analytic metaphysics Information Artifact Ontology – linguistics, aboutness 25
  26. 26. http://bioportal.bioontology.org/ontologies/1666 Saturday, April 4, 2015 26The Emotion Ontology with thanks to Janna Hastings, European Bioinformatics Institute
  27. 27. Example: Emotional personality trait An emotional personality trait =def. a stable enduring characteristic of a person which involves a predisposition (i.e. a disposition which gives rise to an increased risk) to undergo emotions of a particular sort, both occurrents and dispositions. Saturday, April 4, 2015 27
  28. 28. all terms provided with definitions Saturday, April 4, 2015 28 The Emotion Ontology
  29. 29. Types of emotion Saturday, April 4, 2015 29 Types of emotions
  30. 30. Types of appraisal Saturday, April 4, 2015 30 Types of appraisal
  31. 31. Types of feeling 31
  32. 32. 32 Types of Physiological Response to Emotion
  33. 33. built by downward population from MF (which is in turn built from BFO) MFO-EM affective representation is_a MFO:cognitive representation MFO:cognitive representation is_a BFO:specifically dependent continuant 33
  34. 34. BFO:Entity BFO:Continuant BFO:Occurrent BFO:Process BFO:Independent Continuant BFO MFO BFO:Dependent Continuant Cognitive Representation Affective Representation Mental Process Bodily ProcessBFO:Disposition MFO-EM Emotion Occurrent Organism Emotional Action Tendencies Appraisal Subjective Emotional Feeling Physiological Response to Emotion Process inheres_in is_output_of Emotional Behavioural Process Appraisal Process has_part agent_of Emotion Ontology Top Level
  35. 35. http://www.ifomis.org/bfo/users 35 BFO:Entity BFO:Continuant BFO:Occurrent BFO:ProcessBFO:Independent Continuant BFO:Dependent Continuant BFO:Disposition To ensure the interoperability needed for data integration, ontologies must share a common, stable domain-neutral top level BFO = Basic Formal Ontology
  36. 36. Anatomy Ontology (FMA*, CARO) Environment Ontology (EnvO) Infectious Disease Ontology (IDO*) Biological Process Ontology (GO*) Cell Ontology (CL) Cellular Component Ontology (FMA*, GO*) Phenotypic Quality Ontology (PaTO) Subcellular Anatomy Ontology (SAO) Sequence Ontology (SO*) Molecular Function (GO*)Protein Ontology (PRO*) Extension Strategy + Modular Organization 36 top level mid-level domain level Information Artifact Ontology (IAO) Ontology for Biomedical Investigations (OBI) Spatial Ontology (BSPO) Basic Formal Ontology (BFO)
  37. 37. Example: biochemical basis of emotion Emotions are effected in part by neurotransmitters such as dopamine, tryptophan with thanks to Janna Hastings, European Bioinformatics Institute Saturday, April 4, 2015 37 dopamine (CHEBI:25375) molecular entity (CHEBI:25375) biological role (CHEBI:24432) neurotransmitter (CHEBI:25512) has role neurotransmitter receptor activity (GO:0030594) Molecular function (GO:0003674) realized in happiness (MFOEM:42) part of emotion (MFOEM:1) subtype
  38. 38. Is-a overloading Toronto is a city capital city is a city It is a disgrace to the human race that it has chosen to employ the same word ‘is’ for these two entirely different ideas (predication and identity) – a disgrace which a symbolic logic language of course remedies. (Russell 1919:172) 38/
  39. 39. Three kinds of Relations 39 Relations between types (or ‘classes’) is_a (= is a subtype of) Relations between instances (or ‘individuals’) author_of, teacher_of Relations connecting instances to types is_an_expert_on is_allergic_to is_an_instance_of
  40. 40. An ontology is a representation of types of entities and of the relations between them The result of applying an ontology to a body of data about instances is a knowledge base Gene Ontology (GO) vs. Gene Ontology Annotation Database (GOA) 40
  41. 41. Manual ontology building vs. NLP 41 Natural language processing and machine reasoning more generally are making progress But (so far) only ontologies built by manual experts have proven value
  42. 42. Ontology of Philosophy 42 - text vs. structured data - conflicts of interpretation affecting the goals of ontology itself - no neutral perspective - for GO and other scientific ontologies science itself provides a neutral perspective - what can provide the neutral perspective here?
  43. 43. Examples of philosophical knowledge bases 43 1. Low hanging fruit, authoritative data
  44. 44. The Philosophy Family Tree An academic genealogy of philosophers Only one type of link: is_Doktorvater_of • as wiki • as indented list • as linked graph 140,000 entries The largest (and longest) chain of links begins with Leibniz 44/
  45. 45. as wiki (still working) 45/ http://philosophyfamilytree.wikispaces.com
  46. 46. 46/ http://ontology.buffalo.edu/philosophome as indented list
  47. 47. 47/
  48. 48. http://ontology.buffalo.edu/philosophome 48 as linked graph
  49. 49. 49
  50. 50. 50/
  51. 51. 51/
  52. 52. 52/
  53. 53. Examples of philosophical knowledge bases 53 2. Not low hanging fruit
  54. 54. With thanks to Alois Pichler (Wittgenstein Archive, Bremen) 54 Wittgenstein Ontology – http://wab.uib.no/cost-a32_philospace/wittgenstein.owl
  55. 55. 55 Upper Level – http://wab.uib.no/cost-a32_philospace/wittgenstein.owl
  56. 56. Top-Level: Source Alois Pichler (WAB). CCPL BY- NC-SA 56
  57. 57. Top-Level: Subject Alois Pichler (WAB). CCPL BY- NC-SA 57
  58. 58. 58 Subject branch • Place – Instances: Skjolden; Cambridge • Date – Instances: 11 May 1936 • Issue – Instances: philosophy; logical analysis • Point – Example of instance: Logical analysis is essential to philosophy • Field (a field of philosophical discussion) – Has subclasses: • Epistemology – Scepticism » Rule-FollowingScepticism • Perspective – Has subclasses: APichler_Course_TLP; APichler_Course_PI – Instances: contradiction; state_of_affairs …
  59. 59. 59 Examples of Relations isArguedForIn – [Philosophical analysis is essential to philosophy] isArguedForIn [W-TLP] isPublishedInWork − [Ms-114,48v[5]et49r[1]] isPublishedIn [W- PG1969:PartI:II:sect19] isReferredToIn – [Augustinus, Aurelius: Confessiones] isReferredToIn [Ms-114,48v[5]et49r[1]]
  60. 60. Alois Pichler (WAB). CCPL BY- NC-SA 60 Interlinked browsing of texts (data) and relations (metadata)
  61. 61. Alois Pichler (WAB). CCPL BY- NC-SA 61 Checking Wittgenstein’s references to Augustine
  62. 62. Alois Pichler (WAB). CCPL BY- NC-SA 62 Checking PG 1969, Part II, §17, and focusing on one of its sources
  63. 63. 63 http://philosophyideas.com/
  64. 64. 64/
  65. 65. 65/ No controlled vocabulary
  66. 66. 66/ Mixes instances with types
  67. 67. pi 67/http://philpapers.org/
  68. 68. 68/
  69. 69. Simple ontological traffic rules 1. avoid is_a overloading 2. use exclusively singular nouns and noun phrases 3. do not suppose that A is a kind of A & B 4. true path rule (asserting A is_a B is to assert something that is grammatical, and universally true) Principal lesson of scientific ontologies: reasoning power depends on rule 4 69/
  70. 70. 70/
  71. 71. Breaking traffic rules • moral rationalism is_a the a priori • the a priori is_a epistemological sources • epistemological sources is_a epistemology • epistemology is_a metaphysics and epistemology The first generation of scientific ontologies broke these rules too. But they have learned since then to do it right. 71/
  72. 72. Another ontological traffic rule • Do not populate an ontology through multiple unmonitored human sources • Do not create an ontology on the basis of a single source of data – the principal value of a well-built ontology is in its secondary uses, uses which were not anticipated when the ontology was first developed 72/
  73. 73. PhilOnto An example of an Ontology of Philosophy that tries to do it right http://ontology.buffalo.edu/philosophome/pdcp hilontology-v1.owl 73/
  74. 74. 74
  75. 75. philosopher 75 instance_of
  76. 76. 76 Kinds and subkinds Instances
  77. 77. 77
  78. 78. philosopher 78 instance_of
  79. 79. Subkinds of philosopher 79
  80. 80. Features of PhilOnto 80 PRO • Built on the basis of tested best practice principles for ontology development • Built to be extendible through an evolutionary process • Built manually, on the basis of careful thinking about structure and definitions
  81. 81. Features of PhilOnto 81 CON • Still a fragment
  82. 82. Clear distinction in InPhO between is_a and instance_of 82 ethicist is_a [type of] philosopher Carnap instance_of philosopher
  83. 83. 83
  84. 84. 84
  85. 85. 85
  86. 86. 86
  87. 87. InPhO Top-Level in Protégé 87 no definitions
  88. 88. InPhO Top-Level in Protégé 88 only one branch is populated
  89. 89. InPhO second-level under ‘Idea’ 89
  90. 90. “ethics is_a Idea” seems not to conform to the expectations of statistically typical end-users 90
  91. 91. Third-level under ‘ethics’ seems quite coherent 91
  92. 92. change is_a metaphysics metaphysics is_a Idea 92
  93. 93. is_a and subclass 93 change is_a metaphysics metaphysics is_a Idea are not helped if we read ‘subclass of’ in place of ‘is_a’ since ‘subclass of’ is to be understood set- theoretically what would every member of the class change is a member of the class metaphysics mean?
  94. 94. ‘instances’ in InPhO 94
  95. 95. What does ‘instance’ mean? Colin: [it is a] kind of meaning in use, i.e., a specification of how instances are assigned and a contextual interpretation, supplied by end users, in which it makes sense to say that ideas about Japanese Zen Buddhist Philosophy are instances of ideas about Japanese Philosophy more generally. It is this latter, more pragmatist approach to meaning that I prefer …
  96. 96. 6 Put more precisely, we take a computational ontology to be a directed acyclic graph where nodes represent concepts and the links between concepts represent the taxonomic “isa” relation … everything that “is a” instance of Red Wine “is a” instance of Wine … everything that “is a” instance of Racism “is a” instance of African and African-American philosophy
  97. 97. Further mysteries How is it decided what gets listed under ‘Instances’ of feminist philosophy and what gets listed under ‘Related Terms’. Is there any right and wrong for any of this?
  98. 98. And still further mysteries
  99. 99. Eh?
  100. 100. Features of InPhO PRO • Impressive tooling • Authoritative data sources such as the Philosophy Family Tree being used to populate the InPhO knowledge base • Secondary uses being explored (e.g. as part of a robotics application to try to detect contexts in which there are ethically significant issues in play)
  101. 101. Features of InPhO CON • full of mysteries • does not follow established best practices • no concern for interoperability with other ontologies • no concern for correctness of is_a hierarchies and • no concern for logical definitions (as far as I can see) • thus many opportunities for reasoning with the ontology are foreclosed
  102. 102. Challenges for InPhO • OWL provides reasoners to check consistency • Were inconsistencies ever found when building InPhO? • One secondary use for ontologies is to detect errors in databases • Can InPhO be used to detect errors in the SEP? • One secondary use for ontologies is to enhance existing classification and tagging systems • Can InPhO be used to improve the classifications in PhilPapers? • by finding redundancies? • by aiding more coherent classification by identifying subsumption relations? • via semantic enhancement?

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