We are deluged by a growing amount of data coming from heterogeneous sources, like web sites and social networks, and finding relevant information to extract potentially useful knowledge is becoming every day more challenging. The focus of this chapter is on describing techniques for knowledge representation and management of Web and social media data by semantic information modeling. Specifically, the following methods exploiting semantic information are described: (i) a method that provides a compact and structured representation of the concepts in a document (typically web pages) in form of graphs, ready for classification and agglomeration; (ii) a method to represent and synthesize the information content of Twitter conversations in the form of semantic maps, from which the main topics and orientation of tweeters may easily be read. From the obtained results, we observe that both methods provide promising performance.
|Titolo:||Semantic Maps for Knowledge Management of Web and Social Information|
CIARAMELLA, Angelo (Corresponding)
|Data di pubblicazione:||2020|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|