The Semantic Web inTen Passages

Harold BoleyInstitute for Information Technology e-BusinessNew Brunswick, Canada

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

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

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

Passage 2:The Search Engine and its Crawler

Passage 2:The Search Engine and its Crawler 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

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

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

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

Passage 4:Semantics – From Common Words to Standard Concepts

Passage 4:Semantics – From Common Words to Standard Concepts

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

Passage 5:Semantic Relationships Between Standard Concepts and …

Passage 5:Semantic Relationships Between Standard Concepts and … “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

Passage 5:… and Knowledge Derivation

Passage 5:… and Knowledge Derivation 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

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

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

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

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

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

Build ontologies – tough job! Automating the building of ontologies

is very difficult – why?

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

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

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

Passage 9:Refined Standard Concepts Inherit Refined Semantic Relationships

Passage 9:Refined Standard Concepts Inherit Refined Semantic Relationships 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

Passage 10:Library Catalogues as Metadata Ontologies

Passage 10:Library Catalogues as Metadata Ontologies

“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