docs/terms-view.md

The terms view

Part of MBDef. terms gathers, for each concept, what all its papers say about it, field by field, and merges items that say the same thing. It builds on the other extracted fields of each row.

How items merge

• Names (synonyms, related_terms, subtypes, supertypes) merge by the rule that merges the terms themselves (Term merging, with the vocabulary of all corpus terms): “misinformation” and “Mis-information” (among the related terms of “disinformation”) become one item, stated in 188 papers. A name whose spelling is one of the dataset's concepts links to it (links_to), so the supertypes and subtypes form links between concepts.
• Sentences (definitions, description, examples) merge only when they read the same after case, punctuation and spacing are set aside, as when several papers quote one source.
• papers counts the distinct papers that state an item: “disinformation” as a supertype of “fake news” is stated in 38 papers. variants records every merge, and sources gives each paper's own wording and citations.
• Fields are kept as each paper puts them. The same name can sit under several fields: for “fake news”, “disinformation” is a synonym in 60 papers, a related term in 147 papers, a subtype in 24 papers, a supertype in 38 papers. The view does not choose between them.

Worked example. The terms row of “fake news” (659 papers) says, most stated first:

• supertypes: “disinformation” (38 papers; the concept “disinformation”), “misinformation” (29 papers; the concept “misinformation”), “information disorder” (7 papers; the concept “information disorder”), and 29 more
• subtypes: “propaganda” (28 papers; the concept “propaganda”), “disinformation” (24 papers; the concept “disinformation”), “misinformation” (23 papers; the concept “misinformation”), and 253 more
• synonyms: “disinformation” (60 papers; the concept “disinformation”), “misinformation” (49 papers; the concept “misinformation”), “false news” (37 papers; the concept “false news”), and 137 more
• related_terms: “misinformation” (153 papers; the concept “misinformation”), “disinformation” (147 papers; the concept “disinformation”), “rumors” (48 papers; the concept “rumor”), and 240 more
• definitions: 1,707 after merging; the one stated by most papers (25): “fabricated information that mimics news media content in form but not in organizational…”

Field	Items	After merging	Merged from several forms	Stated by 2+ papers	Name a concept
definitions	103,476	99,259	3,172	562	—
description	41,762	41,377	252	57	—
synonyms	6,434	5,498	146	252	2,878
related_terms	21,269	18,000	373	978	12,299
subtypes	12,682	11,160	467	620	6,496
supertypes	9,105	7,849	335	538	6,167
examples	10,190	9,904	132	109	—

144 name items name their own concept (for example a term listed as its own synonym); they are kept as extracted.

Other extracted fields

Besides its definitions, the extraction lists what a paper says about a term in six more fields, the same fields the ground truth annotates. They come from the same extraction runs as the definitions and stay with their term: Step 5 judged the term, not each item.

• Every item is exactly what the extraction model wrote into that field for that paper.
• Only the definitions were checked by the second model.
• Nothing moves an item to another field or votes across papers: a name a paper lists under several fields (for example as both a synonym and a related term) stays under each, and when papers disagree, every paper's choice is kept.

Field	Items	Rows with any
description	41,762	21,314
synonyms	6,434	4,495
related_terms	21,269	12,277
subtypes	12,682	3,941
supertypes	9,105	8,011
examples	10,190	5,095

In all, 101,442 items on 34,336 of the 62,066 rows. The extraction's quality figures (How it was made, step 3) are measured on definitions; the other fields have no such measurement.