Relevance-weighted topic distillation
Algorithms AIMech:RelevanceWeightedTopicDistillation DRAFT
Aliases: Relevance-conscious topic distillation, Chakrabarti relevance-weighted distillation
An iterative web-link ranking procedure that alternates authority and hub score propagation using destination relevance for forward contributions and source relevance for backward contributions, retaining inter-site links and restricting authority eligibility by relevance.
Identity Notes & Concept Scope
Compared all 442 pinned AIO and 679 native IDs, names, aliases and scoped synonyms before either cohort write; no normalized identity match. Ignored- and hidden-inclusive searches of three checkouts returned 26,0,0 lines, all read: prior deduplication logs and the source lead, not these maintained identities. Semantic search covered 127 AIO and 439 native definitions: 70 AIO and 318 native rows match previously inspected unchanged context; all 57 newly selected AIO and 121 new native definitions were read. Eleven complete AIO nodes, incident edges and generated records were assessed, with full native NaiveBayes, BestFirstSearch, DINOSelfDistillation and FlakeFocusedCrawl context. The classifier-guided acquisition family and relevance-weighted ranking component were compared directly. The concept is the 1999 relevance-weighted hub/authority ranking component on a supplied crawled graph. It differs from the outer acquisition loop, unweighted HITS, Flake cut-based grouping and teacher–student knowledge distillation.
Claims & Grounded Evidence
5 Claims
Mechanism
#asymmetric-propagation
For a retained link u to v, forward weight E_F[u,v] is R(v) and backward weight E_B[u,v] is R(u). Authority updates sum E_F[u,v] times hub score h(u); hub updates sum E_B[u,v] times authority score a(v). The procedure alternates these updates with unit-length scaling. Destination and source relevance therefore act on different propagation directions; one unweighted adjacency matrix does not represent both operations.
Mechanism
#eligibility-and-graph
Construct the graph using links between different sites. A relevance threshold admits potential authorities, described as the most relevant 10–20 percent of nodes, and remains enforced during iteration. Hubs have no corresponding eligibility threshold, although their backward contributions are weighted by their own relevance. Thus a low but positively relevant hub can receive a positive score; lack of an eligibility cutoff is not lack of relevance weighting.
Application
#acquisition-interface
In the focused crawler this component periodically processes the accumulated graph, identifies hubs to revisit and promotes their unvisited citations. It can help reconsider useful routes lost through text-based misclassification. The ranking component itself is distinct from retrieving pages, training a topic classifier or compressing a teacher model into a student.
Limitation
#convergence-and-input-boundary
The source prescribes a suitable number of iterations with score normalization but does not provide a universally applicable stopping rule or zero-norm/tie policy for this weighted procedure. Ranking is conditional on the supplied graph, relevance values and authority selection. The classifier supplying those values has a printed Bayes-denominator discrepancy in Equation 1; this record does not infer implementation behavior from it.
Limitation
#evaluation-boundary
The source compares top-ranked pages and server addresses obtained from separate crawls and presents historical resource lists and integration anecdotes. Section 4.4 and Figure 8 axes refer to top 25 resources, but the caption says 100 best servers. These results do not establish a single unambiguous overlap denominator, independently labeled ranking accuracy, complete-web recall or a general semantic-authority guarantee.
⚠️ Curated Limitations & Negative Results
- The record denotes the 1999 relevance-weighted distiller, not generic HITS, all topic-distillation methods, a learned semantic class or a whole focused-crawling system.
- Authority selection is asymmetric with hub eligibility. Numerical weight definitions do not guarantee that classifier scores are calibrated or that high link authority equals factual correctness.
- The primary text specifies normalization between updates but not every degenerate graph, initialization, zero-score or stopping case. No blanket convergence or uniqueness claim is made.
- The same-source classifier equation and ranking-count ambiguities remain explicit. Constructed exact arithmetic illustrates directional weights and eligibility; it does not replicate author code or experiments.
- All eighteen primary pages and relevant rendered figures/formulas were inspected. The author-hosted PDF pagination differs from publisher metadata, and publisher full-text access failed. No author software, historical URL visits, live crawl, paid service or training was performed; DRAFT remains.
Artificial Intelligence Ontology (AIO) Grounding
Explicit AIO Snapshot Assessment: No Match
Rationale: No suitable AIO target was found for this relevance-weighted hub/authority propagation algorithm. Topic classification supplies its inputs, but the component does not classify documents, learn topic distributions, partition the graph or perform teacher–student distillation. MathematicalFunction is too different in procedural scope to provide a useful asserted match.
Search Scope: All 442 pinned AIO and 679 native identities were checked; ignored- and hidden-inclusive searches covered three checkouts. Semantic assessment covered 127 AIO and 439 native definitions, with unchanged preceding rows verified and 57 new AIO plus 121 new native definitions read. Eleven complete AIO node/edge/generated-record contexts were inspected. This is a bounded assessment of the pinned snapshot.
| Examined AIO Candidate | Rejection Reason |
|---|---|
AIO:Distillation
|
This is relevance-aware hub/authority ranking and guided acquisition, not smaller-model training to mimic a larger teacher. |
AIO:ProbabilisticTopicModel
|
A supplied human topic taxonomy and trained classifier guide retrieval; the procedure does not discover latent topics. |
AIO:RankingBias
|
A belief that higher-ranked results are intrinsically more relevant is a bias, not a link-ranking algorithm. |
AIO:OrdinalLLM
|
Ranking web links does not make this procedure a language model. |
AIO:DataPreparation
|
Focused retrieval and link analysis are not defined here as cleaning or transforming data for a downstream learned model. |
AIO:Clustering
|
These methods select relevant pages and rank hubs/authorities without partitioning the graph into similarity clusters. |
AIO:MathematicalFunction
|
The defined objects are iterative graph ranking or acquisition procedures, not merely scalar mathematical functions. |
AIO:RetrievalAugmentedLLM
|
There is no retrieval-conditioned language generation. |
AIO:GraphLanguageModel
|
A hyperlink graph used for ranking and navigation is not a language model operating on graph representations. |
AIO:Classification
|
The component consumes relevance scores and ranks graph nodes; it does not itself assign topic classes or fit a classifier. |
AIO:HierarchicalClassification
|
The taxonomy belongs to the supplying classifier, not the hub/authority propagation algorithm. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
chakrabarti1999
|
Soumen Chakrabarti, Martin van den Berg, Byron Dom
|
primary paper
full_text
|
ID: doi:10.1016/S1389-1286(99)00052-3
sha256:bd6570f71558…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-14T02:43:30Z
Updated: 2026-10-14T02:43:30Z
Source File:
data/curated/RelevanceWeightedTopicDistillation.yaml