Notations are given only numbers rather than names: Arg0, Arg1, Arg2, and so on. In general, Arg0 : PROTO-AGENT, Arg1 : PROTO-PATIENT, Arg2 : benefactive, instrument, attribute, or end state. Arg3 : start point, benefactive, instrument, or attribute, and Arg4: the end point   
The semantics of the other roles [Arg2,3,4] are less consistent, often being defined specifically for each verb.

Notation

PropBank also has a number of non-numbered arguments called ArgMs which represent modification or adjunct meanings.(ArgM-TMP, ArgM-LOC, etc.)
These are relatively stable across predicates, so they are not listed with each Frame file. Data labeled with these modifiers are helpful in training systems to detect temporal, location, or directional modification across predicates.

ArgMs

NomBank adds annotations to noun predicates. This allows semantic role labelers to assign labels to arguments of both verbal and nominal predicates.

NomBank

PropBank Example 

A resource of sentences annotated with semantic roles.
The semantic roles in PropBank are defined with respect to an individual verb sense. Each sense of each verb thus has a specific set of roles.

State University of New York at Stony Brook

A relational database is a database that explicitly stores information on relations between entities that are significant in our model. While regular training data allows the model to infer and learn relationships, a relational database explicitly states the relations significant for the problem at hand.

A knowledge base is any database intended to convey "commonsense" knowledge that humans normally know from day-to-day experiences for a deep learning model. Significant knowledge bases include *Freebase*, *OpenCyc*, *WordNet*, *Wikibase*, and *GeneOntology*. 

Relational Databases and Knowledge Bases

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

Relation extraction is the first step of the information extraction process.
Within this step, we find and classify semantic relations among entities mentioned in a text.

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