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Hyponymy is a directional semantic relationship in which one word sense denotes a more specific kind of what another denotes. For example, the vehicle sense of car is more specific than vehicle. WordNet records such superordinate/subordinate relationships between synsets, rather than treating shared spelling as evidence of semantic inclusion.^[ref]

A simple set model makes the direction explicit. Let $C$ contain the things counted as cars and $V$ the things counted as vehicles under a fixed interpretation. Then $C\subseteq V$: every member of $C$ is in $V$, while membership in $V$ need not imply membership in $C$. Reversing the arrow changes the claim.

This helps explain a search expansion. A query intended to include all vehicles may benefit from entries for particular vehicle kinds; replacing a request for cars with any vehicle can instead admit unwanted results. The relation also differs from synonymy: two names for one sense do not represent a broader and narrower classification. A reproducible lexical database retains both the relation's direction and its sense-specific endpoints.

A lemma is a conventional citation form used to represent a lexical entry or a set of related grammatical forms. It differs from a lexeme, the abstract vocabulary item being represented, and from an arbitrary truncation of a word's spelling. The convention is language-specific.^[ref]

In a simple English analysis, walks, walked and walking can be assigned the lemma walk. The Universal Dependencies guidelines also distinguish inflection from derivational morphology: organizations has citation form organization, not organize or organ.

Context can be needed before the citation form is chosen. The spelling saw can name a tool or occur as a past form of see; a process that uses spelling alone cannot settle every such ambiguity. Recording the part of speech, intended word sense and the original occurrence makes a text corpus analysis more reproducible than retaining only the normalized label.

A lexical database organizes information about the lexemes of a language. It can record forms, part of speech, definitions, word senses, examples and relationships among entries. A database representation makes these items separately identifiable and queryable rather than treating an entire entry as one unstructured string.^[ref]

For a toy design, an entry for bank can link to distinct word sense records for a financial institution and a river margin, each with an example sentence. A form record for banks can refer to the relevant entry without requiring duplicated definitions. Spelling is consequently an inadequate unique identifier: different lexical analyses can involve the same written form.

A lexical database can also connect entries to attested uses in a text corpus, as in FieldWorks Language Explorer. The stored relations support searches and revision, but the linguistic annotation still represents an analysis whose evidence must be retained.

Synonymy is a semantic relationship between expressions with the same or sufficiently similar meaning in a specified use. It is assessed at the level of word senses and constructions, rather than assuming that every meaning of two written forms matches. WordNet groups expressions such as car and automobile into synsets for a shared concept.^[ref]

Consider an original substitution test. In The car needs a new tire, replacing car with automobile can preserve the intended vehicle reference. That successful substitution does not establish equivalence in every fixed expression, technical register or context. The interpretation and the purpose of the comparison must remain explicit.

Synonymy also differs from hyponymy. A word for a narrower class can be related to a broader label without meaning the same thing. For an information-retrieval system, expanding a query with alternate names and expanding it with names of subtypes are therefore different operations. Recording the relationship type prevents a lexical database from collapsing all semantic similarity into one undifferentiated link.

WordNet is a lexical database that represents English word senses through synsets connected by labeled relationships. It groups meanings by part of speech, while keeping different meanings of the same written form distinct. Its organization therefore supports queries about concepts rather than spelling alone.^[ref]

For a toy information-retrieval task, suppose an annotated query uses the financial sense of bank. Expanding it with terms attached to a river-margin sense would introduce irrelevant results even though the spelling matches. A sense-aware representation supplies the distinction needed to avoid that error; a separate method still has to choose the intended sense from context.

WordNet also records relations such as hyponymy, connecting more specific concepts to broader ones. Following a labeled relation answers a different question from finding another member of the same synset.^[ref] Neither neighborhood nor shared spelling guarantees that two expressions can replace each other in every sentence.

A word sense is a particular meaning distinguished for a word or lexical entry within an explicit analysis. The same spelling can support different interpretations, and a lexical database may represent those interpretations separately. A sense inventory is therefore a set of analytical distinctions, rather than a count of written forms.

For example, bank can refer to a financial institution or a river margin. The context in the bank approved the loan favors a different interpretation from the boat reached the bank. WordNet organizes senses through synsets and semantic relationships; its documentation explicitly distinguishes relations between meanings from associations between word forms.^[ref]

In an original annotation task, suppose two readers agree on the sense labels for three of four uses. Their observed agreement is $3/4$, but the result depends on which distinctions the inventory requires. Merging two fine distinctions can increase agreement without improving every downstream task. Word sense analysis consequently needs both context and documented granularity.