  Knowledge is captured in the form of entities and their relationships and
stored in knowledge graphs. Knowledge graphs enhance the capabilities of
applications in many different areas including Web search, recommendation, and
natural language understanding. This is mainly because, entities enable
machines to understand things that go beyond simple tokens. Many modern
algorithms use learned entity embeddings from these structured representations.
However, building a knowledge graph takes time and effort, hence very costly
and nontrivial. On the other hand, many Web sources describe entities in some
structured format and therefore, finding ways to get them into useful entity
knowledge is advantageous. We propose an approach that processes entity centric
textual knowledge sources to learn entity embeddings and in turn avoids the
need for a traditional knowledge graph. We first extract triples into the new
representation format that does not use traditional complex triple extraction
methods defined by pre-determined relationship labels. Then we learn entity
embeddings through this new type of triples. We show that the embeddings
learned from our approach are: (i) high quality and comparable to a known
knowledge graph-based embeddings and can be used to improve them further, (ii)
better than a contextual language model-based entity embeddings, and (iii) easy
to compute and versatile in domain-specific applications where a knowledge
graph is not readily available
