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

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

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

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

An Ontology for Hotels

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

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

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