Ontology instances are in general stored as triples which associate two related entities with pre-defined relational descriptions. Sometimes such triples can be incomplete in that one entity is known but the other entity is missing. The automatic acquisition of the missing values is closely related to relation extraction systems that extracts binary relations between two identified entities. Relation extraction systems rely on the availability of named entities in that mislabelled entities can decrease the number of relations correctly identified. Although recent results demonstrate over 80% accuracy for recognising named entities, when input texts have less consistent patterns, the performance decreases rapidly. This paper presents OntotripleQA which is the application of question-answering techniques to relation extraction in order to reduce the reliance on the named entities and take into account other assessments when evaluating potential relations. Not only does this increase the number of relations extracted, but it also improves the accuracy in extracting relations by considering features which are not extractable only by comparison with named entities. A small dataset was collected to test the proposed approach and the experiment demonstrates that it is effective on the sentences of the Web documents obtaining 68% performance on average.