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| courses:hcai:lab_krr [2026/11/01 21:42] – [Challenge] kkt | courses:hcai:lab_krr [2026/11/02 09:48] (current) – [Learn more!] kkt |
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| * **Reading:** | * **Reading:** |
| * FIXME | * AIMA: **Chapter 2, Section 2.4.7**, and **Chapter 10, Sections 10.1-10.2**. Focus on atomic, factored, and structured representations; ontological engineering; categories and objects; category hierarchies; inheritance; and the trade-off between expressive representations and reasoning complexity. |
| * **Technical preparation:** | * **Technical preparation:** |
| * Open today's notebook and run all setup cells and the first code cell in **Segment 1** to verify that the notebook works in your environment. __The output should display information about two artworks in three different formats.__ | * Open today's notebook and run all setup cells and the first code cell in **Segment 1** to verify that the notebook works in your environment. __The output should display information about two artworks in three different formats.__ |
| ==== Q&A ==== | ==== Q&A ==== |
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| - FIXME | - A museum stores each artwork as one text description. What questions become difficult to answer? |
| | - Two artworks were created by the same person. Should the creator's name be copied into both records? |
| | - If every painting is a cultural object, must we record both categories for every painting? |
| | - Should a museum knowledge model contain every known fact about an artwork? |
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| ==== Notebook ==== | ==== Notebook ==== |
| Work in groups of **2-4** to **curate a small digital exhibition** using cultural heritage data from **Wikidata**. | Work in groups of **2-4** to **curate a small digital exhibition** using cultural heritage data from **Wikidata**. |
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| Choose your own theme and audience, then adapt the SPARQL templates from the FIXME **__starter notebook__** to find suitable objects. Your query should use at least **two meaningful relationships**, such as: <code>artwork -> creator -> place of birth | Choose your own theme and audience, then adapt the SPARQL templates from the **__[[https://colab.research.google.com/drive/1aCoLzRA_AH9XWdQuhjbfiG58MJWJu6j7?usp=sharing|STARTER NOTEBOOK]]__** to find suitable objects. Your query should use at least **two meaningful relationships**, such as: <code>artwork -> creator -> place of birth |
| artwork -> collection -> museum location | artwork -> collection -> museum location |
| artwork -> depicts -> person or place</code> | artwork -> depicts -> person or place</code> |
| ==== Learn more! ==== | ==== Learn more! ==== |
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| * FIXME | * {{https://protege.stanford.edu/publications/ontology_development/ontology101.pdf|Ontology Development 101}} -- A practical guide to defining scope, classes, properties, hierarchies, and competency questions |
| | * [[https://protege.stanford.edu/|Protégé]] -- A free ontology editor for modelling classes, relationships, individuals, and logical constraints. |
| | * [[https://www.w3.org/TR/owl2-primer/|OWL 2 Primer]] -- An accessible introduction to more expressive ontology modelling and automated inference. |
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| | * {{https://www.media.mit.edu/publications/bttj/Paper23Pages211-226.pdf|ConceptNet}} -- A large semantic network representing everyday knowledge used in commonsense reasoning. |
| | * [[https://doi.org/10.18653/v1/2021.conll-1.38|Commonsense knowledge in ConceptNet and word associations]] -- A research perspective on how different graph-based resources represent implicit everyday knowledge. |
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| | * [[https://obofoundry.org/|OBO Foundry]] -- A collection of interoperable ontologies used across biology, medicine, and scientific data integration. |
| | * [[https://saref.etsi.org/|SAREF]] -- Ontologies for connecting smart devices, sensors, measurements, buildings, energy systems, and IoT services. |
| | * [[https://docs.cloud.google.com/enterprise-knowledge-graph/docs/overview|Google Enterprise Knowledge Graph]] -- See how organizations reconcile entities and connect information stored across heterogeneous data sources. |