Name
Abstract | ||
|---|---|---|
In this paper we propose and investigate Ontology Population from Textual Mentions (OPTM), a sub-task of Ontology Population from text where we assume that mentions for several kinds of entities (e.g. PERSON, O RGANIZATION , L OCATION , GEO- POLITICAL_ ENTITY) are already extracted from a document collection. On the one hand, OPTM simplifies the general Ontology Population task, limiting the input textual material; on the other hand, it introduces challenging extensions to Ontology Popula- tion restricted to named entities, being open to a wider spectrum of linguistic phenomena. We describe a manually created benchmark for OPTM and discuss several factors which determine the difficulty of the task. |
Year | Venue | Keywords |
|---|---|---|
2006 | OntologyLearning@COLING/ACL | spectrum |
Field | DocType | Volume |
Ontology (information science),Population,Ontology-based data integration,Ontology,Process ontology,Information retrieval,Computer science,Natural language processing,Artificial intelligence,Suggested Upper Merged Ontology,Upper ontology,Limiting | Conference | W06-05 |
Citations | PageRank | References |
11 | 1.43 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bernardo Magnini | 1 | 2027 | 226.13 |
| Emanuele Pianta | 2 | 441 | 55.12 |
| Octavian Popescu | 3 | 78 | 18.05 |
| Manuela Speranza | 4 | 46 | 10.47 |