Events are elusive entities; as the authors of [7] argue, even human annotators do not agree on what is an event and what is its boundary in terms of the extension of its participants, temporal and geospatial extent, etc. More aspects of events appear when trying to recognize or extract them from text: polarity of speaker's judgment on events, negation, modality, relations (temporal, causal, declarative, etc.) to other events, etc. For example, the text: The Black Hand might not have decided to barbarously assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th, 1914.
FRED contains several functionalities for event extraction, which can be
summarized according to typical subtasks:
{ Event identity: FRED focuses on events expressed by verbs, propositions, common
nouns, and named entities (typically proper nouns).
{ Event classi cation: FRED uses Linked Data-oriented induction of types for the
identi ed events, reusing e.g. VerbNet7, WordNet8, DBpedia9, schema.org, and
DOLCE10 as reference ontologies.
{ Event unity: FRED applies semantic role labeling [
The beginning and the following sentences are used as a lead example for showing FRED's functionalities:
The Renaissance was a cultural movement that spanned in Italy from the 14th to the 17th century. Some sources report that the Renaissance might have been started by Greek scholars from Constantinople.
In the diagram from Figure 2, the following events are recognized, extracted, classi ed, and aligned to WordNet, VerbNet, and/or DOLCE: Renaissance (classi ed as a Movement, and aligned to the WordNet Motion synset, and to the DOLCE Situation class), span 1, report 1, and start 1 (classi ed as occurrences of the Span, Report and Start frames respectively, and aligned to VerbNet).
Furthermore, the events have participants (e.g. Italy, scholar 1, source 1, etc., also classi ed and linked appropriately) through some roles labelled with properties derived from VerbNet(e.g. vn:Agent), or from the lexicon used in the sentence (e.g. ren:from) In one case, a modal modi er (Possible) to the event start 1 is added. 7 http://verbs.colorado.edu/~mpalmer/projects/verbnet.html? 8 http://wordnet.princeton.edu 9 http://dbpedia.org 10 http://www.ontologydesignpatterns.org/ont/dul/DUL.owl
Finally, some relations between events are detected: report 1 vn:Theme start 1, and span 1 before report 1 (through the now 1 interval).
See also Figure 1 for the graph obtained from the beginning sentence.
The triples given as output by FRED are more than those visualized, for example
they include text spans and their reference to the semantic annotations, through the
Earmark vocabulary [
FRED is therefore an intermediate component for event extraction and representation, which can be augmented with background knowledge, and whose graphs can be combined e.g. in time series for historical tasks.
FRED will be demoed as an event extractor by showing event-intensive sentences, and examples of views that focus on relevant event knowledge. RDF models can be morphed to concentrate on speci c features. For example, Figure 3 semantically summarizes the model from the Black Hand sentence by only showing events with their relations, and their main participant, obtained by means of the following SPARQL query: PREFIX dul: <http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#> PREFIX vnrole: <http://www.ontologydesignpatterns.org/ont/vn/abox/role/> PREFIX boxing: <http://www.ontologydesignpatterns.org/ont/boxer/boxing.owl#> PREFIX boxer: <http://www.ontologydesignpatterns.org/ont/boxer/boxer.owl#> PREFIX : <http://www.ontologydesignpatterns.org/ont/boxer/test.owl#> CONSTRUCT {?e :agent ?x . ?e ?r ?e1} WHERE { {{?e a boxing:Situation} UNION {?e a ?class . ?class rdfs:subClassOf+ dul:Event}} ?e ?p ?x FILTER (?p = vnrole:Agent || ?p = boxer:agent || ?p = vnrole:Experiencer || ?p = vnrole:Actor || ?p = vnrole:Actor1 || ?p = vnrole:Actor2 || ?p = vnrole:Theme) FILTER NOT EXISTS {?e vnrole:Theme ?x . ?e vnrole:Agent ?y FILTER (?x != ?y)} OPTIONAL {{{?e ?r ?e1} UNION {?e ?s ?z . ?z ?t ?e1}} {{?e1 a boxing:Situation} UNION {?e1 a ?class1 . ?class1 rdfs:subClassOf+ dul:Event}} FILTER (?e != ?e1)}}
Fig. 3: A summarized FRED graph showing only event relations and agentive participants for the Black Hand sentence.