Although several RDF knowledge bases are available through the Linked Open Data (LOD) initiative, the ontology schema of such linked datasets is not very rich. In particular, they lack object properties. The problem of finding new object properties between any two given classes has not been investigated in detail in the context of Linked Data. In the first part of this paper, we present DARO (Detecting Arbitrary Relations for enriching Ontology of Linked Data)—an unsupervised solution to enrich the LOD cloud with new object properties (and their instances) between two given classes. DARO first identifies text patterns from the web corpus that can potentially represent relations between individuals. These text patterns are then clustered based on semantic similarity to capture the object properties between the two given classes. We have empirically evaluated our approach on several pairs of classes and found that the system can indeed be used for enriching the linked datasets with new object properties and their instances. We have compared DARO with newOntExt which is an offshoot of the NELL (Never-Ending Language Learning) effort. Our experiments reveal that DARO gives better results than newOntExt as a recall-oriented system. In the second part of the paper, we propose a methodology to predict potential pairs of classes which could be connected by object properties but are not yet connected. We claim that evidence obtained from external textual resources and their Word2Vec representations can be made use of, for this purpose. Our approach gives results that are complementary to those given by the traditional techniques found in the literature. Hence our method can be used in combination with the traditional techniques for maximum benefits. © 2020, Ohmsha, Ltd. and Springer Japan KK, part of Springer Nature.