Body of Knowledge (BoK): the set of topics, their definitions, explanations, and other actionable information about a specific domain.
SFIA defines a body of knowledge as:
1) structured knowledge that is used by members of a discipline to guide their practice or work
2) the prescribed aggregation of knowledge in a particular area an individual is expected to have mastered to be considered or certified as a practitioner.
Content curation: the gathering, organizing, and online presentation of content related to a particular theme or topic. (source: whatis.techtarget.com/definition/content-curation).
Critical knowledge: unique, highly specialized knowledge that: 1) is essential to your organization’s success; 2) at risk of being lost or compromised; 3) has not been clearly documented; 4) is known by only one or very few individuals (as opposed to common knowledge).
Critical knowledge essentially disappears from your organization every time the person having that knowledge walks out the door at the end of the workday. And it leaves for good when that person decides to transfer or retire. This creates a serious risk.
Similarly, a person without that critical knowledge may need it at some point – perhaps unexpectedly – such as in a crisis situation, or when transitioning into a new position. Rather than have that person acquire the knowledge over a long, painful period of trial and error, it’s better to have a program in place for transferring and growing that knowledge by design, using a repeatable process.
Curation: the organizing and presenting of physical and digital (virtual) objects.
eBody of Knowledge (eBoK): a body of knowledge rendered in one or more digital formats, including text, wiki, audio, video, and more recently, knowledge graphs.
eKnowledge Library: an organized collection of e-bodies of knowledge; the collection as a whole is curated by one or more knowledge librarians; however, each individual e-body of knowledge typically has its own knowledge curator.
Folksonomy: a classification scheme, similar to taxonomy, in which the users themselves define categories informally by tagging content with keywords that they feel are most appropriate.
Graph database: built upon graph theory, graph databases are based on graph theory in mathematics, comprised of semantic triples (nodes and edges, or <ai, rj, bk>), and are “schema-less,” usually built starting with the query first, rather than last (e.g., the CYPHER query language used in Neo4j, which we’ve used in constructing this eBoK).
For most applications, graph databases exhibit orders-of-magnitude increases in performance vs the relational model.
Information architecture: a discipline and a set of methods that aim to identify and organize information in a purposeful and service-oriented way. 
Knowledge: the capacity to take effective action (the right action, in the right place at the right time) in varied and uncertain situations . The capacity to take effective action also includes observing and deciding which action(s) to take.
Knowledge curation: the capture, classification, organization, transfer, and management of individual, organizational, or community knowledge.
Knowledge graph: an interactive visual representation of an ontology that allows a user to navigate the various concepts in a body of knowledge, along with the various relationships rj between each pair of concepts ai and bk (i.e., semantic triples <ai, rj, bk>).
Knowledge Library: a space for facilitating knowledge flows: the creation, organization, curation, sharing, and application of knowledge; it houses one or more bodies of knowledge; the collection as a whole is curated by one or more knowledge librarians; however, each individual body of knowledge typically has its own knowledge curator.
Knowledge Representation: A means of encoding human knowledge and reasoning into a symbolic language that enables it to be processed by information systems. 
Meronomy: a hierarchical representation of part-whole relationships (also referred to as a partonomy)
Ontology: has many meanings, depending upon the context, or you might even say, depending upon the ontology! In the purest sense, as with most words ending with the suffix ology, it means the study of what is (derived from the Greek root onto, which means “being”).
For the purposes of knowledge curation, we like the following definition: an ontology is a formal, explicit specification of a shared conceptualization. Conceptualization refers to an abstract model of some phenomenon in the world by having identified the relevant concepts of that phenomenon. Explicit means that the type of concepts used, and the constraints on their use are explicitly defined. Formal refers to the fact that the ontology should be machine–readable. Shared reflects the notion that an ontology captures consensual knowledge, that is, it is not private of some individual but accepted by a group. 
Semantic distance: the gap which arises when people with different perspectives or viewpoints think and talk differently about the same thing.
Tacit knowledge: knowledge that is so deeply internalized even a seasoned expert has difficulty explaining it to someone or putting it into writing. As opposed to explicit knowledge, such as a set of clearly defined rules (if X happens, then do Y), tacit knowledge is usually governed by deep intuition. You may have heard someone say, “I can’t tell you what it looks like, but I’ll know it when I see it.” That’s usually an indicator of tacit knowledge at work.
Taxonomy: a means of organizing a set of topics or concepts into a hierarchy of categories and subcategories. Well-known taxonomies are The Library of Congress Online Catalog, and the structure used in biology to organize all living things (kingdom, phylum, class, order, family, genus, species).
Text Analytics: the use of software and content models (taxonomies and ontologies) to analyze text and the applications that are built using this analysis. 
 M. Cummings in The Encyclopedia of Human-Computer Interaction, Interaction Design Foundation, 2nd Ed.
 A. Bennet & D. Bennet, Organizational Survival in the New World: The Intelligent Complex Adaptive System, Elsevier, (2004).
 R Studer, V R Benjamins, and D Fensel, Knowledge Engineering: Principles and Methods, IEEE Transactions on Data and Knowledge Engineering 25(1-2): p. 185.
 T. Reamy, Deep Text: Using text analytics to conquer information overload, get real value from social media, and add big(ger) text to Big Data, Information Today, Inc., (2016), p. 5.
 Martin Swain, Knowledge Representation, in Werner Dubitzky, et al., eds., Encyclopedia of Systems Biology, SpringerLink (2013).