# Review of Web Mapping: Eras, Trends and Directions
## 3 Web Mapping Eras of Development
### Web Mapping Eras
#### 3.2.9 Intelligent Web Mapping Era  
The _intelligent web mapping era_ embraces semantic technologies and smart environments that provide knowledge, context and customization to the information delivered to users and applications. Although still evolving and being defined, this era tries to encapsulate the fact that knowledge must be extracted from data and information and intelligently contextualized for users and applications. Both semantics and context must be represented, constructed, discovered and used in a way that intelligently filters appropriate information for better knowledge and decision-making.  
Web 3.0, also referred to as the semantic web, focuses on enabling machine understanding of semantically structured data that provides users with more meaning and relevant information [22; 23; 91]. To facilitate semantic interoperability across different applications, [PERSON] [92] identified two types of semantic heterogeneity to be addressed, namely, cognitive heterogeneity involving different perspectives of the same phenomena, and naming semantics involving different terminology for the same phenomena. This meaning of phenomena is captured using ontologies, which are knowledge in the form of rules and inferences. Ontologies are represented using a language such as the commonly used Web Ontology Language (OWL), which is part of the Resources Description Framework (RDF) [93; 94].  
An example of a semantic web solution is the retrieval of public health information from a disparate range of community databases in a case study at the University of Texas [95]. Structured data such as medical records and unstructured data such as doctor's notes were integrated and queried using ontologies that capture health conditions and contextual information with the outcome to obtain perspectives on community health and disease surveillance [95; 96]. As another example, [PERSON] et al. [94] describes a prototype for the state of Connecticut that enables emergency responders to automatically search geospatial features from multiple semantically heterogeneous data sources.  
The OWL ontologies enrich the natural language interface that can accept queries from non-expert users without prior knowledge. Users can express queries using their own natural language about roads, town boundaries, airports, etc., and the natural language processing parser will use synonyms from WordNet as well as domain ontologies to make the interpretation and linkage of appropriate information to enable query results interpretation. As a further example, [PERSON] et al. [97] developed a brokered system that uses OWL ontologies to integrate multiple heterogeneous datasets for the purpose of responding to a user query. They tested this for queries on state electoral boundaries across multiple state jurisdictions in Australia.  
The developments in the geospatial semantic web include aspects of semantic information representation, automated and semi-automated building of ontologies, semantic web services composition, natural language processing and data discovery, and performance issues [94]. A range of different ontologies needs to be constructed for data, functionality and execution of applications online [98]. In addition to this, intelligence needs to be utilized to apply results of queries to the context of users and applications. Ontologies are also being used to profile users and applications so that additional semantic information regarding query context can be applied. The goal is to provide only relevant information to users and applications in the appropriate context: geographic, temporal, attributed, semantic, historical, etc.  
As Web 3.0 continues to evolve, including in the geospatial domain, there continues to be speculation about what future web developments may embrace. The focus is increasingly on the human-machine interaction with increased automated and semi-automated decision-making.