We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. To handle Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision- making. To handle Variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle Veracity, we explore trust models and approaches to glean trustworthiness. Our ultimate goal is to deal with the challenges due to the four Vs of Big Data to derive Value to enable decision-making and action. In what follows, we discuss the primary characteristics of the Big Data problem as it pertains to the Five Vs.