Analysing knowledge in social big data

Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, the semantic web, and social networks. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation and visualising data. In this paper, we will present a new approach that can extract entities and their relationships from social big data, allowing for the inference of new meaningful knowledge. This approach is a hybrid approach of multi-agent systems and K-means algorithm.

• social big data
• knowledge extraction
• named entity recognition
• relation extraction
• multi-agent systems
• K-means clustering
• hybrid approach
• social media
• social networks
• knowledge inference

• Information Retrieval and Data Mining
• Semantic Web and Ontologies
• Big Data Technologies and Applications
• social big data
• knowledge extraction
• named entity recognition
• relation extraction
• multi-agent systems
• K-means clustering
• hybrid approach
• social media
• Information Systems
• Artificial Intelligence
• Information Systems and Management
• Computer Science
• Decision Sciences