With the rapid adoption of the Internet of Things (IoT) technology in various domains including health, transportation, and manufacturing, the number of IoT devices in the world is expected to increase to 50 billion by the end of 2020 1 . These IoT devices collect an enormous amount of data using the sensors embedded in them. According to a Cisco report, the amount of annual global data traffic will reach 10.4 ZB (zettabytes) by 2019 2 . It is interesting to note here that this data will be multi-modal in nature comprising of various formats including video streams, images, and strings. Handling such large-scale heterogeneous data and processing it in real-time will be a key factor towards building smart applications [1]. Semantic approaches – ontologies – have been used to solve these issues related to largescale heterogeneity. Ontologies are defined as a “well-founded mechanism for the representation and exchange of structured information” [۲]. Existing works have proposed the use of unified ontologies to tackle issues of interoperability and automation associated with heterogeneity of sensor data [3–۵]. However, multiple possible unifications developed by domain experts [6] pose several challenges as every unified ontology proposes its self-defined taxonomy. Figure 1 illustrates an example scenario where the use of several possible unifications of ontologies causes issues. Let us consider two IoT platforms deployed at different geographic locations for building two different applications for smart-health and smart-building domains respectively. Accessing data from both the platforms at another location (e.g., a remote server using data from both the platforms for developing another application) will lead to the challenges described below.