# A Graph DB-Based Solution for Semantic Technologies in the Future Internet
###### Abstract  
With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must be handled by systems where explicit relationships among objects are represented and processed. For this purpose, the knowledge representation branch of AI proposes Knowledge Graphs, widely used in the Semantic Web, where different online applications may interact by understanding the meaning of the data they process and exchange. This paper describes a framework and online platform for the Internet-based knowledge graph definition, population, and exploitation based on the LPG graph model. Its main advantages are its efficiency and representational power and the wide range of functions that it provides to its users beyond traditional Semantic Web reasoning: network analysis, data mining, multistrategy reasoning, and knowledge browsing. Still, it can also be mapped onto the SW.  
graph databases; ontologies; Knowledge Graphs; Semantic Web; knowledge representation and reasoning; graph mining; network analysis; knowledge exploration +
Footnote †: journal: _Future Internet_  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00  
different and partly incompatible ones: the SW adopts the RDF graph model, and the most prominent graph DB adopts the Labeled Property Graph (LPG) model. The latter allows more expressiveness and produces more compact graphs than the former. This paper proposes a framework that covers the whole range of layers, from back-end machinery to end-user interfaces, and the whole range of functions, from knowledge representation (defined by the GraphBRAIN framework), base creation, population, and maintenance to network mining and analysis (carried out by the GraphBRAIN system), to interoperability among different SW applications (guaranteed by a purposely developed GraphBRAIN API), to Knowledge Base querying, browsing, and mining (provided by the SKATEBOARD system). Moreover, we propose a mapping mechanism to port data expressed in the LPG into the RDF to create a bridge between GraphBRAIN and the SW.  
GraphBRAIN uses an LPG-based knowledge representation that can be mapped directly onto the Neo4j [1] graph DB for instant storage and manipulation and acts as a schema for the DB content. An API, which can be used by any application that adopts the GraphBRAIN framework, wraps the DB and ensures that all operations carried out on it are compliant with a given ontology. A Web Application is available for form-based information entry and retrieval and for user-friendly execution of the basic CRUD operations. The GraphBRAIN framework acts as an intermediate format that can be easily mapped onto different technologies:  
* The Neo4j libraries for graph manipulation, querying, and mining;
* Advanced MultiStrategy Reasoning tools that combine different inference strategies to tackle more complex problems and closely simulate human reasoning (e.g., as provided by GEAR [2]);
* Standard SW representation techniques and reasoning tools to be interoperable with the existing SW world. One of the end-user applications powered by GraphBRAIN is SKATEBOARD, which was purposely developed to support semantic-based, interactive, and dynamic browsing of the KG. It provides various filters for the information to be displayed, tools for expanding the portion of the graph under consideration and looking into the single graph components for their information content, and additional facilities, such as maps, that allow easy location-based analysis and browsing of the data. The novel contributions of this paper are  
1. A description of the current version of the GraphBRAIN knowledge representation structure and formalism, which extends and refines that proposed in [3];
2.