  Structured and unstructured data and facts about drugs, genes, protein,
viruses, and their mechanism are spread across a huge number of scientific
articles. These articles are a large-scale knowledge source and can have a huge
impact on disseminating knowledge about the mechanisms of certain biological
processes. A domain-specific knowledge graph~(KG) is an explicit
conceptualization of a specific subject-matter domain represented w.r.t
semantically interrelated entities and relations. A KG can be constructed by
integrating such facts and data and be used for data integration, exploration,
and federated queries. However, exploration and querying large-scale KGs is
tedious for certain groups of users due to a lack of knowledge about underlying
data assets or semantic technologies. Such a KG will not only allow deducing
new knowledge and question answering(QA) but also allows domain experts to
explore. Since cross-disciplinary explanations are important for accurate
diagnosis, it is important to query the KG to provide interactive explanations
about learned biomarkers. Inspired by these, we construct a domain-specific KG,
particularly for cancer-specific biomarker discovery. The KG is constructed by
integrating cancer-related knowledge and facts from multiple sources. First, we
construct a domain-specific ontology, which we call OncoNet Ontology (ONO). The
ONO ontology is developed to enable semantic reasoning for verification of the
predictions for relations between diseases and genes. The KG is then developed
and enriched by harmonizing the ONO, additional metadata schemas, ontologies,
controlled vocabularies, and additional concepts from external sources using a
BERT-based information extraction method. BioBERT and SciBERT are finetuned
with the selected articles crawled from PubMed. We listed down some queries and
some examples of QA and deducing knowledge based on the KG.
