Nabila N.F.Basir N.Saudi M.M.Pitchay S.A.Ridzuan F.Mamat A.Deris M.M.2024-05-292024-05-292016978148000000010.1109/UKSim.2015.902-s2.0-84991740107https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991740107&doi=10.1109%2fUKSim.2015.90&partnerID=40&md5=aefded7adfea651d0866695d4bd35615https://oarep.usim.edu.my/handle/123456789/9661Most of the existing techniques on relation extraction focus on extracting relation between subject, predicate and object in a single sentence. However, these techniques unable to handle the situation when the text has sentences that are incomplete: either does not have or unclear subject or object in sentence (i.e. 'unsure' value). Thus this does not properly represent the domain text. This paper proposes an approach to predict and identify the unsure value to complete the sentences in the domain text. The proposed approach is based on the probability theory to identify terms (i.e., subject or object) that are more likely to replace the 'unsure' value. We use voting machine domain text as a case study. � 2015 IEEE.en-USIncomplete information systemmost dominantnon-taxanomicpredicateprobability theoryCircuit simulationComputer scienceComputersSoftware engineeringIncomplete information systemsmost dominantnon-taxanomicpredicateProbability theoryProbabilityConference Paper4975017576591