1. Overall Information Extraction vs. Annotating the Data Conference proceedings by O. Etzioni, Washington U, Seattle; S. Handschuh, Uni Krlsruhe

2. Attempts Overall Extraction Centralized Huge amount of pages Annotation Local Single page

3. KnowItAll Enter a new instance X to the ontology (class or relationship) Generate a set of phrases containing X Pass phrases to WEB interface Analyze the pages grammatically Asses “probability of truth” Put newly found instances to database

4. KnowItAll Huge Web spaces can be assessed and categorized automatically Utilizes a commercial database storing the results which may be easily queried and shared Not domain specific (grammar applies to all fields) -Language dependent (needs rules of used grammar) -May extract too much or too few facts due to threshold sensitivity -Due to ambiguities of language the search can not be fully automated -Statistically something is a truth if many documents say so

5. An Ontology for Hotels

6. S-CREAM Have an ontology like above ready made Call Amilcare Subcall of Annie Annotated text Serialize such that relations are uncovered

7. S-CREAM augments document directly similarity to semantic networks information is stored at source and not at a remote server after several transformations -domain specific – dependent on ontology -pretty complex (not for the naïve users) -Due to ambiguities of language the search can not be fully automated -Huge amount of preparation necessary

8. Comparison KnowItAll globally access the web look for points of interest (phrases) S-CREAM augmet single documents apply complete ontology of a field