From Word Meaning to Knowledge Graphs: Lexemes, WordNet, FrameNet, and Knowledge Graphs

Follow the purple arrows. Lexemes at the top left are the starting point. WordNet organizes word meanings into a network of concepts. The large purple arc leads right to FrameNet, which moves from single words to event structure, and the path ends at the bottom with knowledge graphs and medical knowledge resources. The note at the bottom right is my main takeaway.

A lexeme is a basic unit of vocabulary: a particular form (its spelling and pronunciation) paired with its meaning. The three aspects are:

Aspect	Term	Example
Spelling	Orthography	content spelled c-o-n-t-e-n-t
Sound	Phonology	the noun is pronounced /ˈkɒntent/, the adjective /kənˈtent/
Meaning	Meaning	noun: what something contains; adjective: satisfied

One spelling is not always one lexeme: content as a noun and as an adjective differs in both sound and meaning, so they are two lexemes. In the other direction, run, runs, and ran look different but belong to one lexeme, listed in a dictionary under run (the citation form, called the lemma). SLP3, Appendix I

Semantic relations between words

Once we know what words mean, the next step is to describe how they relate. Four relations come up most often:

Relation	Meaning	Example
Homonymy	Same form, unrelated meanings	bank: the side of a river / a financial institution
Polysemy	One word with several related senses	bank / blood bank (both a place that stores something)
Synonymy	Different words with the same or similar meaning; the test is whether one can replace the other without changing the meaning	big ≈ large
Hyponymy / hypernymy	The hierarchy between a specific category and a broader one	dog is a hyponym of animal; animal is a hypernym of dog

Synonymy often holds only in some contexts. In "How big is that plane?" big can become large, but "big sister" cannot become "large sister." Substitutability has to be judged in context. SLP3, Appendix I

✎ reading noteReading note: the polysemy panel pairs the financial bank with a blood bank, which share the sense of a place that stores things; the river bank in the homonymy panel is unrelated. In the table, "Form: Same" for homonymy means the same spelling and sound.

WordNet is a lexical database of English built at Princeton University. Its key design choice is that it is centered on synsets, not single words. A synset is a set of near-synonyms that express one concept, with a short definition called a gloss. For example, car, auto, automobile, machine, and motorcar share one synset, the concept of a car.

This standardizes concepts: whichever word is used, the same concept maps to the same node, while a word with several meanings belongs to several synsets. WordNet then defines relations between synsets by hand, which turns them into a network:

• Hyponymy: dog → canine → animal, all the way up to the most abstract node, entity.
• Part–whole (meronymy): an engine is part of a car.
• Antonymy: hot ↔ cold (mainly for adjectives).

WordNet 3.0 contains about 118,000 nouns and 11,500 verbs. The average noun has 1.23 senses and the average verb 2.16, so verbs are more polysemous. SLP3, Appendix I

The problem it addresses: word sense disambiguation

Word sense disambiguation (WSD) is deciding which sense of a word is used in a given context. In "I deposited cash at the bank" and "We sat on the river bank," bank belongs to different synsets. Because WordNet supplies both the list of candidate senses and the relations among them, it has long served as the standard sense inventory for WSD research.

This is close to how I learned English: when a word has several meanings, finding a word that could replace it in each sentence shows which meaning is meant there.

✎ reading noteReading note: "every verb carries an average of at least two distinct meanings" overstates it. The average verb has about 2.16 senses, and many verbs have only one. The counts in the table are distinct word strings and synsets in WordNet 3.0.

WordNet is good at answering "what does this word mean?" but not "who did what to whom in this sentence?" FrameNet fills that gap.

FrameNet is built on semantic frames: background knowledge structures that describe a type of situation and define its participants, called frame elements, which are semantic roles. A word in a sentence evokes a frame. SLP3, Chapter 22

Sentence	Word that evokes the frame	Frame	Frame elements
I ate a turkey sandwich.	ate	Ingestion	Ingestor = I; Ingestibles = a turkey sandwich
The company hired Maria as a nurse.	hired	Hiring	Employer = the company; Employee = Maria; Position = a nurse

Different verbs can evoke the same frame: hire, employ, and retain all belong to Hiring. So even when the wording differs, a computer can recognize that the sentences describe the same kind of event. FrameNet has about 1,200 frames, and frames are linked in a hierarchy, much like hyponymy in WordNet.

Why hand annotation matters

Linguists define FrameNet's frames and roles by hand and annotate many example sentences. These examples standardize human understanding of sentences and can be used directly to train models. Chundru et al. (2025) found that large language models can identify frames reasonably well without task-specific training. Fine-tuning on FrameNet's annotated data substantially improved in-domain accuracy (on text like the training data) while still generalizing well to out-of-domain benchmarks. Chundru et al., EMNLP 2025

✎ reading noteReading note: in the eating example, "I" is labeled "Frame," but "I" fills a role. It is the verb ate that evokes the frame, which FrameNet calls Ingestion, with the roles Ingestor and Ingestibles rather than Eater and Food.

FrameNet answers "how is this sentence expressed?" A knowledge graph cares about "what does this sentence say?" A knowledge graph represents real-world concepts and facts as entity–relation–entity structures, for example:

• amoxicillin —treats→ community-acquired pneumonia
• pneumonia —is a→ lung disease

When many facts share this structure, knowledge stops being scattered across sentences and becomes a network that can be queried, reasoned over, and linked. Lexical resources such as WordNet and FrameNet supplied the skeleton of concepts and relations; later knowledge graphs extracted facts at scale from encyclopedias and the web.

Resource	What stands out
YAGO (from 2007)	Combines Wikipedia with WordNet; facts carry time and place
Freebase → Wikidata	Large collaboratively edited knowledge bases; after Freebase closed in 2016, its data moved to Wikidata
Probase	Microsoft mined about 2.7 million concepts from web pages, including many rare "long-tail" concepts
BabelNet	Integrates WordNet, Wikipedia, and other resources into a multilingual, encyclopedic semantic network covering about 600 languages

BabelNet: broader coverage, more diffuse results

BabelNet is more comprehensive than WordNet because it connects lexical meanings with multilingual and encyclopedic knowledge. About BabelNet However, this broader coverage can also make the results feel more diffuse. When I searched for library, BabelNet returned not only the common concept of a library but also more obscure entities, including a song by a singer. This shows both a strength and a limitation: its broad coverage offers a richer network of meanings and entities, but it can also bring in less relevant or highly specific results that users need to filter out.

✎ reading noteReading note: "600 Languages in Paper.net" should read BabelNet, the multilingual resource described in the text.

In medicine, one concept often has many names: myocardial infarction, MI, heart attack. The two knowledge resources most used in medical NLP both address this, but they do different jobs.

	UMLS (Unified Medical Language System)	MeSH (Medical Subject Headings)
What it is	A Metathesaurus integrated by the US National Library of Medicine (NLM), plus a Semantic Network and a specialist lexicon	A controlled, hierarchical subject-heading vocabulary produced by NLM
How it is organized	Names for the same concept from about 190 source vocabularies (SNOMED CT, ICD-10-CM, RxNorm, LOINC, MeSH, and others) share one concept identifier (CUI)	Headings are arranged in a tree from broad to specific, for example Diseases → Neoplasms → … → Lung Neoplasms
Main use	Linking and integrating terms across systems so that health records, codes, and search tools can map to each other	Indexing PubMed/MEDLINE articles by subject, and cataloging and searching
Relation to knowledge graphs	Concepts (CUIs) are nodes and the relations between them form a network, often used as a foundation for medical knowledge graphs	A vocabulary rather than a full knowledge graph, though its hierarchy is often folded into knowledge graphs

The 2025AB UMLS Metathesaurus holds about 3.49 million concepts drawn from 190 source vocabularies in 29 languages. UMLS 2025AB statistics MeSH is used "for indexing, cataloging, and searching" biomedical and health information. MeSH (NLM)

One practical use of knowledge graphs in medicine is drug repurposing for rare diseases. mediKanren, developed at the University of Alabama at Birmingham, uses knowledge graphs to link disease mechanisms, literature, and databases to find new uses for approved drugs. It has been used to help more than 500 families affected by rare diseases. Foksinska et al., 2022

✎ reading noteReading note: UMLS stands for Unified Medical Language System, and the tool labeled "Medicarin" is mediKanren.

Once an algorithm begins to understand the meaning of words and sentences, it can start building a system of knowledge. Put simply, after understanding what words and sentences mean, it sorts out how they relate and then links those relations into a knowledge graph. I can map this step onto how I learn: after taking in a certain amount of new material, I organize it into a structure.

Linking relations can start with words. A word includes how it sounds, how it is written, and what it means. In the previous note we labeled words, and labels can be parts of speech or broad meaning categories. Relations between words can be defined in different directions, such as synonyms, antonyms, and hyponyms and hypernyms, and that is how WordNet is built. I used this method often when learning English: learning substitute words. It helps with word sense disambiguation. When a word has several meanings, finding its synonym in each sentence shows which meaning is used there.

With WordNet in place, semantic frames go further, from words to the structure of the event a sentence describes: who did what to whom, and which role each part plays. Frames rely on hand-annotated text, which turns human understanding of language into a consistent standard. Training or fine-tuning models on this annotated data can raise accuracy on similar text.

With semantic frames understood, meaning can be used to build concrete facts and concepts and to give knowledge a structure, forming a knowledge graph.

The next question I want to explore: how do simple language models work?

This note is based on my study notes from the University of British Columbia (UBC) micro-certificate program "Natural Language Processing to Improve Patient Care," in the course "Overview of NLP and Large Language Models." Technical details were checked against Jurafsky and Martin, Speech and Language Processing (3rd edition draft, August 2026): Appendix I, Word Senses and WordNet, and Chapter 22, Semantic Role Labeling; Chundru et al. (2025), Do LLMs Encode Frame Semantics? Evidence from Frame Identification; and Foksinska et al. (2022), The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren.

Try it yourself

• Open English WordNet: look up bank to see how many synsets it has and what each one's hypernym is.
• FrameNet: find Ingestion or Hiring in the frame index to see its frame elements and annotated examples.
• BabelNet: search for library and compare the concepts and entities it returns, to see broad coverage and diffuse results side by side.
• MeSH Browser: look up Lung Neoplasms to see where it sits in the tree. Browsing UMLS online requires a free UTS account and license.