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![P3. The names are addresses on the Web
x T
[<x> IsOfType <T>]
different
owners & locations
<analgesic>](https://image.slidesharecdn.com/sssw2013-130708050046-phpapp01/75/Semantic-Web-questions-we-couldn-t-ask-10-years-ago-8-2048.jpg)
Uploaded byFrank van Harmelen
Semantic Web questions we couldn't ask 10 years ago
The document is a presentation by Frank van Harmelen discussing the current state and future of the semantic web, emphasizing its foundational principles and challenges faced over the past decade. It highlights advancements in meaningful search, data integration, and content reuse while addressing issues of heterogeneity, self-organization, and trust. The talk raises critical questions regarding the evolution of the semantic web and the complexities associated with information management.
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Semantic Web questions we couldn't ask 10 years ago
- 1. Frank van Harmelen Allthe questions we couldn’t ask 10 years ago Creative Commons License: allowed to share & remix, but must attribute & non-commercial
- 2. The bad news: you’regoing to get 3 talks 1. Where are we now? – The Semantic Web in 4 principles & a movie – Did we get anywhere? 2. Now what? – Questions we couldn’t ask 10 years ago 3. Methodological hobby horse – Science or engineering?
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- 4. a web page inEnglish about Frank And this page is about LarKC and another web page about Frank And this page is about Stefano This page is about the Vrije Uniersitei “The Semantic Web” a.k.a. “The Web of Data”
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- 6. P1. Give allthings a name
- 7. P2. Relations forma graph between things
- 8. P3. The namesare addresses on the Web x T [<x> IsOfType <T>] different owners & locations <analgesic>
- 9. P1+P2+P3 = GiantGlobal Graph
- 10. P4. explicit &formal semantics • assign types to things • assign types to relations • organise types in a hierarchy • impose constraints on possible interpretations
- 11. Examples of “semantics” FrankLynda married-to • Frank is male • married-to relates males to females • married-to relates 1 male to 1 female • Lynda = Hazel lowerbound upperbound Hazel
- 12. Semantic Web: Where arewe now?
- 13. Did we getanywhere? • Google = meaningful search • NXP = data integration • BBC = content re-use • Wallmart= SEO (RDF-a) • data.gov = data-publishing
- 15. NXP: data integration about26.000 products Triple store Triple store Departments Customers Notice the 3-layer architecture
- 16. BBC Notice the 3-layerarchitecture
- 21. Did we getanywhere? • Google = meaningful search • NXP = data integration • BBC = content re-use • BestBuy = SEO (RDF-a) • data.gov = data-publishing Oracle DB, IBM DB2 Reuters, New York Times, Guardian Sears, Kmart, OverStock, Volkswagen, Renault GoodRelations ontology, schema.org
- 22. Size Matters: 25-45billion facts
- 23. The questions that wecouldn’t ask 10 years ago
- 24. • Heterogeneity • Self-organisation,long tails • Distribution • Provenance & trust • Dynamics • Errors & Noise • Scale
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- 31. Bio-medical ontologies in Bio-portal >5 links Self-organisation
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- 33. incidental or universal? impact on mapping? impacton reasoning? impact on storage?
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- 38. Maximally consistent subsets? Modules? Repair? Argumentation? Fuzzy Semantics? Uncertainty Semantics? Rough Semantics? Streams? Incremental reasoning? Non- monotonicity? versioning? Representation? From provenance to trust? (Re)construction? knowledgeabout knowledge? Caching? Subgraphs? Payload priority? incidental or universal? impact on mapping? impact on reasoning? impact on storage? Socio- economic first to market market- share
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- 40. Laws about thephysical universe Laws about the information universe ?
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- 42. Law: |T|<< |A| T= terminological knowledge A = assertional knowledge
- 43. Dataset Closure of T Closureof T + A Ratio LUBM 8sec 1h15min 562 Linked Life Data 332sec 1h05min 11 FactForge 89sec 2h45min 111 We don’t have any good laws on complexity
Editor's Notes
- #14 @TODO@: do a slide on data-integration at NXP@TODO@: find a slide on RDF-a in Wallmart etc
- #22 @TODO@: do a slide on data-integration at NXP@TODO@: find a slide on RDF-a in Wallmart etc@TODO@: replace company names with logo’s?
- #25 @@ Add: trust@@Add: noisy data (inconsistency, misleading, incomplete)
- #27 Suggests to let a 1000 ontologies blossom, to have lots of connections between lots of datasets.
- #33 Some known information laws already apply:Zipf law / long tail distributions are everywhere= vast majority of occurrences are caused by a vast minority of itemsthis phenomen is sometimes a blessing, sometimes a cursenice for compressionawful for load balancingand knowing the law helps us deal with the phenomenonthat’s why it’s worth trying to discover these laws.
- #34 @add another long-tail example@ (e.g. in-degree?)
- #35 Physical distribution doesn’t work the web is not a database (and never will be)@@ADD: even worse for long tail
- #41 - Compare to physics laws: gravity F = G m_1 m_2 / r^2 conservation of energy (dE/dt = 0), increase of entropy (dS/dt \geq 0),we cannot yet hope for such beautifully mathematised laws,in such a concise language that fits on a very compact space computer science is like alchemy, a "protoscience"
- #42 Some known information laws already apply:Zipf law / long tail distributions are everywhere= vast majority of occurrences are caused by a vast minority of itemsthis phenomen is sometimes a blessing, sometimes a cursenice for compressionawful for load balancingand knowing the law helps us deal with the phenomenonthat’s why it’s worth trying to discover these laws.
- #43 this only works because terminologies are in general only simple hierarchies. (it’s easy to build examples where this doesn’t hold, but in practice it turns out to hold).So, this law depends on the previous lawas an aside: the graph is now big enough to do statistics on it.
- #45 use complexity” as a measure, not just “size”. spell out LLD,don’t break FactForge
- #46 - Semantic Web = engineering enterprise.- This talk = what are the scientific observations/facts/theories after 10 yearsWhat are the big CS (or: KR?) lessons we can learn from a decade of SemWeb?(= regard SemWeb adoption as a giant laboratory for CS laws)Did we learn any science? (and of course the laws won’t be specific to SemWeb? Hopefully not. Hopefully they are generic laws about the structure and behaviour of informaiton!)
- #47 a gazillion new open questionsdon’t just try to build things, also try to understand thingsdon’t just ask how, also ask why