1. The Semantic Web in Ten Passages Harold Boley Institute for Information Technology e- Business New Brunswick, Canada

2. Passage 1: Meaningful Search in the Billion- Fold Planetary Network

3.  Like searching for a specific grain of sand in two tightly packed 1m x 1m x 1m boxes  Current search engines: keyword-based, ranked search (good, but …)  Future search engines: “understand” the semantics (answers/services, not just ranked pages)  Knowledge representation: moving into focus on the Web

4. Passage 2: The Search Engine and its Crawler

5.  Crawlers: enter central & frequent words into a huge “address book”  You get the “hit list” for word w when you type in w  Example Wonder drug for head pain (1,160,000 hits) “Wonder drug for head pain” (no hits)  Abmiguities drug = medicine or narcotics head = body part or front or direction pain = ache or hurt or suffering or distress wonder = puzzlement or monumental creation  Missing the relationships among the words

6. Passage 3: Precision and Recall – Conflicting Measures for Search Results

7.  Aspirin (5,860,000 hits) – low precision  Aspirin “head pain” (8,040 hits) Better, but still low precision Recall problems: many “headache” pages missed – Aspirin headache (649,000 hits)  Aspirin “head pain” OR “head hurt” (583,000 hits) But now what about also “migraine” Query starts to get hard

8. Passage 4: Semantics – From Common Words to Standard Concepts

9.  Semantically want the concept that can be named “head pain” OR “headache” OR “migraine”  Semantic search engine would find the pages “meant”  Ideally Recall: complete Precision: perfect as well

10. Passage 5: Semantic Relationships Between Standard Concepts and …

11.  “Asprin cures head pain” vs. “Asprin causes head pain”  Semantic search engine should recognize semantic relationships between concepts  “Address book” becomes a “knowledge base”  Facts in the knowledge base Asprin --- cures --- headache Subject PRECICATE Object  Increases both recall and precision

12. Passage 5: … and Knowledge Derivation

13.  Suppose you want “Asprin CURES Headache AND Asprin CAUSES Headache”  Could store fact: “Asprin AMB Headache” (AMB = ambivalent)  Could instead write a rule IF pharmaceutical CURES sickness AND pharmaceutical CAUSES sickness THEN pharmaceutical AMB sickness Semantic search engine would find pages satisfying the IF part and hence necessarily also the THEN part  How? Semantic relationships between standard concepts Knowledge representation

14. Passage 6: Where do the Standard Concepts and Predicates Come from?

15.  Experts of a specialized field agree to share normative definitions of their concepts and predicates Shared, explicit concept catalogues Ontologies  Hierarchical superconcept-subconcept dubbed most important: Headache ISA Pain

16. Passage 7: Assigning Concepts/Predicates to Common Words: How?

17.  Build ontologies – tough job!  Automating the building of ontologies is very difficult – why?

18. Passage 7: Assigning Concepts/Predicates to Common Words: How?  Build ontologies – tough job!  Automating the building of ontologies is very difficult – why? Meaning often depends on context Granularity: e.g. general “stomach ache” or specific “appendix attack” Sentence analysis – NLP known to be hard Audio and video – can’t apply textual techniques Sometimes necessary to extend ontology, which only domain experts should be allowed to do  Semi-automatic construction System proposes concepts – expert agrees/fixes/completes TANGO

19. Passage 8: Where Will the Assignments be Stored as Metadata?

20.  External E.g. the “address book” Advantages  Possible to annotate pages not owned  Better for multiple annotations for different ontologies  More convenient for queries  Internal Annotations in the pages themselves Advantages  Can be updated when page changes  Compromise: only URL pointer placed in page  Change/maintenance problem for annotations

21. Passage 9: Refined Standard Concepts Inherit Refined Semantic Relationships

22.  Suppose: Headache ISA Pain; Sporadic-Headache ISA Headache; Chronic-Headache ISA Heacache Aspirin --- CURES --- Headache  Now suppose someone decides this should be different: Aspirin --- CURES --- Sporadic-Headache Now, what about all the annotated pages before the change? (two possibilities)  UPDATE all old annotations: But now domain experts should decide which was meant for each “Headache” occurrence – “Sporadic-Headache” or “Chronic- Headache”  SWITCH ontologies but access old via old: eventually leads to versions of versions and … problems

23. Passage 10: Library Catalogues as Metadata Ontologies

24.  “UPDATE” is the “nicer” solution, but many libraries have chosen “SWITCH” – you sometimes have to search in two or more catalogues  Will eventually become a big problem Competing ontologies Complementary ontologies Could be overwhelmed by ontologies