# B.S. in Mathematics and Computer Science

**Undergraduate Thesis:** *Network structural properties and their application to missing property prediction*

The volume of available structured data is increasing, particularly in the form of Linked Data, where relationships between individual pieces of data are encoded by a graph-like structure. Despite increasing scales of the data, the use and applicability of these resources is currently limited by mistakes and omissions in the linked data.

In this diploma thesis, I study the problem of predicting missing relation-types (properties) for nodes (objects) in large-scale knowledge graphs.

Image on the left depicts a tiny example of a knowledge graph with numerious objects and relations between these objects. Three objects are of particular interest: **Audi**, **Mercedes-Benz** and **Fiat**.

My thesis addresses the problem of learning the graph structure to predict the missing relations: learn from objects, Audi and Mercedes-Benz, that Fiat is missing relations **name**, **subsidiary** and **parentCompany**.

I address the problem by encoding the objects with numerous local and global graph descriptors. Local descriptors are relation-distributions to neighbours and directionality of relations, and global descriptors are shortest paths and diffusion kernels on graphs. These object-descriptors were then used to learn to predict the missing properties of objects.

I apply the methods to large knowledge bases, DBpedia (based on Wikipedia) and Freebase (subset of Google Knowledge Graph), with hundreds of millions of nodes (objects) and edges (relations).