Large-scale ontologies have received much attention in various fields such as information retrieval, data integration, and question answering. However, since it is costly to construct and maintain large-scale ontologies manually, several ontology learning methods fromWikipedia have been proposed. YAGO and DBpedia are famous examples of large-scale ontologies automatically constructed from multilingualWikipedia. However, there is room to extract from Wikipedia many kinds of relationships that are not included in YAGO or DBpedia. Previously, we proposed a method for constructing large-scale ontologies named Japanese Wikipedia Ontology (JWO) from Japanese Wikipedia. Since JWO has been constructed using several methods that are different from the extracting methods of YAGO and DBpedia, it is possible to extract relationships that do not exist in YAGO or DBpedia by using these methods. However, some of the methods are Japanese language-dependent, and cannot be applied directly to English Wikipedia. In this paper, we propose methods for constructing large-scale ontologies named English Wikipedia Ontology (EWO) from English Wikipedia. To achieve this, we have modified the Japanese language-dependent methods of JWO and extended several methods for extracting property axioms. EWO includes Is-a relations (rdfs:subClassOf), classinstance relations (rdf:type), property relations and types, and synonyms (owl:sameAs). The property relations are triples: property domain (rdfs:domain), property range (rdfs:range), and property hypernymy-hyponymy relations (rdfs:subPropertyOf). The property types are object (owl:ObjectProperty), data (owl:DatatypeProperty), symmetric (owl:SymmetricProperty), transitive (owl:TransitiveProperty), functional (owl:FunctionalProperty), inverse functional (owl:InverseFunctionalProperty), asymmetric (owl:AsymmetricProperty), reflexive (owl:ReflexiveProperty) and irreflexive (owl:IrreflexiveProperty). To evaluate EWO, we compared it with existing ontologies constructed from English Wikipedia. Thus, we clarified the differences between the existing ontologies and EWO.
Information extraction from unstructured text data has been used essentially to provide new insights by collecting, storing, and analyzing text data in textual analysis. The research on event extraction has been recently getting more attention in information extraction area since lots of events happens and significantly affect our societies and countries. Related studies on event extraction use rules for identifying and extracting events from texts so far. However, rule based approaches have a limitation in terms of accuracy and rule construction. In this paper, we present an event extraction system which takes advantage of the machine learning method, especially SSVM. The system extracts event triggers and predefined event arguments, while existing rule based systems extract unknown event arguments. Ours provides 60.23 F1 score, which is higher than that of previous researches, in which rule based event extractions were performed. Even though rule-based and machine learning-based approaches cannot be compared against each other completely fairly, what is clear is that for the task in which event arguments are defined in advance, applying machine learning method can make better results.
