| dc.description.abstract | The size of data we are producing is exponentially increasing every year. According to
former Google CEO Eric Schmidt, we produce as much information in two days now
as we did from the dawn of mankind through 2003. The Oil & Gas industries produce
millions of linked data each day. However, a vast majority of the data are unstructured
or semi-structured data. To make a good decision, it is very important that we know
our data. Many industries rely on the insights of their data to take any further action.
Therefore, it is very important for the advancement of a company or an institution to
have an overall view of the data they are producing.
For this thesis, we studied some data produced by Oil & Gas industries that are provided
to us by LOOPS, and we found that the data are usually linked data. Two linked data
can be interlinked with each other and become more useful through semantic queries.
However, due to poor presentation of the data, the benefit that can be achieved from
linked data is lacking.
In this thesis, we devised a system that extracts the meaningful information from the
semi-structured data and visualizes the data using the power of graph. We then use the
graph to have the insights of the data. The system can recognize entities in the graph
and give important feedbacks by inferring more knowledge about the recognized entities.
As we said, the data are interlinked with other data. However, usually in liked data,
some of the links between the data might be missing. The more the data are linked, the
more useful information we can learn from it. Therefore, we invested a significant portion
of our research in predicting the possible missing links between data using supervised
and unsupervised link prediction approach. | nb_NO |
