# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 3 AI-SIoT Architecture
### Service Management Layer
#### 3.2.3 Semantic Analysis Module  
The semantic analysis (SA) module provides basic information of semantic analysis for user identification, behavior recognition and service construction in the AI module. Semantic technology can construct a semantic mapping layer through constructing various semantic models, including the device model, user model, knowledge model and reasoning model [52; 53]. The semantic mapping layer enables different IoT platforms to hide their heterogeneity, which is shown in Figure 4.  
There are many coexisting IoT platforms for device access, such as the Haier U-home platform, Apple HomeKit, Google Android Things, the Alljoyn platform, the oneM2M platform, and so on. Specifically, OneM2M is committed to establishing global standards for the IoT [54]. It refers to the areas of health care, energy management, entertainment, security and etc. However, the truly global standard for smart device management in the IoT is yet to come. Smart devices from different providers follow their own standards, including device descriptions, functional instructions, control interfaces. Different standards lead to inflexible and expensive IoT configurations, and they slow down the development of unified IoT services. Therefore, it is necessary to make these different IoT platforms access the same AI module to ensure the interaction among different IoT platforms. Then, people can flexibly enjoy services from different IoT platforms for the same intention. In addition, semantic technologies enable devices to understand human intentions. Semantic technologies describe people's living environments based on natural language processing, which is also the key to the machine understanding. In traditional intelligent service solutions, the voice control technology is implemented and people can acquire services by giving voice commands and instructions. However, these commands and instructions are only segmented and matched with an inherent instruction library. The traditional intelligent service is not based on machine understanding, and it is not intelligent enough. Semantic technology requires everything to be associated to understand users' activities and predict users' intentions. Semantic computing can be implemented through semantic matching [55; 56; 21], including association computing and similarity computing. Association computing determines a series of services that people need. Similarity computing determines the alternative services for people's same intention. For example, when one service is not available, another service can achieve the same effect. There are five submodules in the semantic analysis module. When a new smart device is registered to the IoT platform, the registration data can be leveraged to match the corresponding semantic models in the object recognition submodule. This submodule connects the Cloud, acquires the semantic model of the new device from the device model provider, and constructs an instance according to the semantic model. The instances of the new devices are stored in the device model database. Our proposed semantic models (including device model and knowledge/user model) are illustrated in Figure 5. Specifically, Figure 5a shows that the device model contains two categories of information branches. First, the basic information mainly contains the device ID, device name, device type and location. Second, the properties mainly contain the property name, value, data type and semantic annotation. The semantic annotation is the key element to construct the associations among devices, which is described in Figure 5c. The Semantic Annotation records the association information of various devices, including associated entities, associated devices, associated activities and etc. These contents are usually used for inferences, because they describe the device properties in detail from various aspects. For example, the air conditioner has the property of cooling, which can reduce the environment temperature. Figure 4: The semantic mapping layer for heterogeneous IoT platforms.