  Cyber threat and attack intelligence information are available in
non-standard format from heterogeneous sources. Comprehending them and
utilizing them for threat intelligence extraction requires engaging security
experts. Knowledge graphs enable converting this unstructured information from
heterogeneous sources into a structured representation of data and factual
knowledge for several downstream tasks such as predicting missing information
and future threat trends. Existing large-scale knowledge graphs mainly focus on
general classes of entities and relationships between them. Open-source
knowledge graphs for the security domain do not exist. To fill this gap, we've
built \textsf{TINKER} - a knowledge graph for threat intelligence
(\textbf{T}hreat \textbf{IN}telligence \textbf{K}nowl\textbf{E}dge
g\textbf{R}aph). \textsf{TINKER} is generated using RDF triples describing
entities and relations from tokenized unstructured natural language text from
83 threat reports published between 2006-2021. We built \textsf{TINKER} using
classes and properties defined by open-source malware ontology and using
hand-annotated RDF triples. We also discuss ongoing research and challenges
faced while creating \textsf{TINKER}.
