BookIndex: Advanced Document Indexing

Updated 4 December 2025

BookIndex is a composite data structure combining a hierarchical table-of-contents tree with a semantic entity-relationship graph for effective document navigation.
It utilizes layout parsing, LLM-based classification, and vector similarity for entity resolution to achieve precise subject indexing and retrieval.
BookIndex supports advanced applications such as retrieval-augmented generation, digital cataloging, and bibliometric analysis with measurable improvements in recall and accuracy.

A BookIndex is a data structure or metadata construct that enables efficient retrieval, search, or subject-based navigation within book-length documents. In the context of computational bibliometrics, information retrieval, and AI-powered document understanding, BookIndex implementations unify book structure, entity semantics, and retrieval utility, supporting applications from subject indexing in libraries to retrieval-augmented generation (RAG) for LLMs.

1. BookIndex Structures and Formal Definitions

BookIndex as introduced in contemporary information extraction and neural retrieval systems is a composite structure encapsulating both the hierarchical arrangement (table-of-contents tree) and a semantic entity-relationship graph (knowledge graph, KG), interconnected via explicit mappings. Formally, BookIndex for a document $D$ is defined as the triplet

$B = (T,\,G,\,M)$

where:

$T = (N,E_T)$ is the hierarchical tree capturing the logical organization (chapters, sections, subsections, paragraphs, tables, images), with $N$ as nodes and $E_T$ as parent-child edges.
$G = (V,E_G)$ is an entity-relation graph with $V$ the set of extracted entities (concepts, scientific terms, headers, objects) and $E_G$ a set of edges representing semantic or containment relations (e.g. 'ContainedIn', coreference, co-occurrence).
$M: V \to \mathcal{P}(N)$ is a mapping ("GT-link") assigning each entity to the tree nodes it originates from, supporting bidirectional access between local tree context and global semantic network (Wang et al., 3 Dec 2025).

This dual structure preserves both native document layout (critical for books with complex, nested contents) and cross-referencing required for advanced question answering and semantic search.

2. BookIndex Construction Algorithms

Construction of BookIndex proceeds in distinct algorithmic stages, blending layout analysis, LLM-driven structure parsing, and graph-based entity resolution.

Tree Construction

Layout Parsing: Segment document pages into blocks using dedicated parsers (e.g. MinerU). Each block $b_i = (c_i, \tau_i, f_i)$ captures content, block type (Title, Text, Table, etc.), and layout features.
Section Filtering & Hierarchy Assignment: Candidate title blocks are batch-classified by an LLM to assign hierarchy levels $l_j$ (e.g. chapter, section) and final node type. Non-title blocks default to 'Text'.
Tree Assembly: Nodes are linked such that each section of level $\ell$ attaches to the nearest ancestor of level $\ell-1$ . Attach non-structural blocks (Text, Table, Image) as children to their enclosing section node.

The process is linear in number of blocks, with bottleneck in LLM-based classification (Wang et al., 3 Dec 2025).

Entity-Relation Graph (KG) Construction

Entity Extraction: For each node $n_i$ , entities $V_i$ and intra-node relations $E_{Ri}$ are extracted using LLMs or VLMs (for images/tables).
Entity Resolution: New entities are matched using vector similarity search (gradient-based), employing a "sharp drop" criterion to avoid quadratic complexity. Confident matches trigger merges; ambiguous cases defer to LLM adjudication.
GT-Link Mapping: As entities are resolved or merged, $M$ is updated so each entity points to all tree nodes from which it was extracted.

This pipeline yields graphs where semantic and structural relationships are co-indexed at fine granularity (Wang et al., 3 Dec 2025).

3. Methods for Automatic Subject and Concept Indexing

Automatic book indexing has distinct methodological lineages depending on the language, corpus, and application context.

Controlled Vocabulary, Supervised Classification

Systems like "Kratt" for the Estonian National Library integrate Estonian Subject Thesaurus (EMS) with feature extraction and supervised binary logistic regression (Asula et al., 2022):

Preprocessing: PDF/image ingestion, OCR, quality filtering, language detection, lemmatization, POS-tagging.
Feature Engineering: Page-wise lemma frequency vectors, optionally TF–IDF weighting, POS histograms.
Candidate Generation: Nearest neighbor retrieval (Elasticsearch, cosine over TF–IDF), followed by aggregation of frequent subject terms from similar books.
Classification & Thesaurus Mapping: One-vs-rest logistic regression per frequent EMS term; aggregation and frequency thresholding yields final set of subject index entries, mapped to controlled vocabulary identifiers.
Evaluation: F1 ≃ 0.30 at threshold θ=0.4 (precision ≃ 0.39, recall ≃ 0.28); ~1 minute per book on commodity hardware.

Lexical Analysis, Language-specific Pipelines

For morphologically rich languages (e.g. Arabic), pipelines emphasize normalization, root extraction, and unsupervised weighting (Molijy et al., 2012):

Normalization and Root Extraction: Remove orthographic and diacritic variants; apply light stemming (Al-Shalabi weight-and-rank root extraction).
TF–IDF Ranking: Compute weights per candidate index term at the root level, optionally boost by location (heading, title).
Index Assembly: Select top-ranked candidates, map occurrences to page numbers, output inverted lists (term → page set).
Precision/Recall: Average precision = 0.998, recall = 1.000 across test books.

4. BookIndex in Retrieval-Augmented Generation Systems

The BookIndex structure is foundational in hierarchical Retrieval-Augmented Generation (RAG) for LLMs, particularly in QA tasks requiring alignment with document logic and deep entity reasoning (Wang et al., 3 Dec 2025):

Index as RAG Backbone: Queries are classified as single-hop, multi-hop, or global. Operators enable entity extraction, tree or graph selection, reasoning (e.g., graph PageRank, text relevance scoring), and synthesis.
Graph–Tree Fusion: BookIndex enables workflows where the agent "forages" for answer patches using both semantic scent (entities in the KG) and structural cues (sections/subsections in the tree).
Empirical Gains: BookRAG with BookIndex achieves large improvements in retrieval recall (e.g., +31.2 pp on MMLongBench benchmark, recall up to 71.2% on M3DocVQA) and QA accuracy (e.g., F1 up to 66.2%), while preserving computational efficiency (Wang et al., 3 Dec 2025).

5. Integrating BookIndex with Large-scale Digital Catalogues and Bibliometrics

BookIndex concepts generalize beyond book-internal navigation, interfacing with catalogues, bibliometric platforms, and digital library infrastructure.

Digital Literature Catalogues: MajinBook provides a multi-century, multi-lingual digital corpus linking shadow-library EPUB clusters to Goodreads bibliographic entities, including robust entity-resolution and block-level indexing; this supports semantic search and analytics over >500k English works (Mazières et al., 14 Nov 2025).
Publisher-level Index Reports: The Book Citation Index (BCI) aggregates item- and citation-level metadata at the publisher–discipline granularity, supporting six primary indicators (Total Items, Books, Chapters, Total Citations, AvgCit, NonCit) for impact ranking and bibliometric profiling, although coverage and normalization remain active challenges (Torres-Salinas et al., 2012).

6. Evaluation Protocols, Challenges, and Limitations

Evaluation Metrics and Protocols

Automatic Evaluation: Precision, recall, F1-measure on held-out sets, comparison to human-assigned indices (Asula et al., 2022, Molijy et al., 2012).
Human-in-the-loop Assessment: Librarian/user satisfaction for subject relevance, genre, temporality.
Efficiency: Time-to-index comparisons (e.g., 1 min/book in Kratt vs. 15 min for humans).

Challenges

Label Sparsity and Coverage: Limited labeled data (e.g., only 22% of EMS terms covered); per-label data augmentation or external corpus integration is required (Asula et al., 2022).
Noise from Heuristic Label Assignment: Uniform page-level label propagation induces mismatches; attention-based relevance modeling is proposed.
Overfitting: Small trained context sets necessitate pretraining (e.g., multilingual BERT) and broader data acquisition.
Structural Ambiguity and Series Effects: Book series in citation indices, document layout heterogeneity, and language bias in metadata normalization present ongoing obstacles (Torres-Salinas et al., 2012, Mazières et al., 14 Nov 2025).

7. Regulatory and Ethical Dimensions

Metadata vs. Full-Text Legalities: Release of metadata-only catalogues (e.g., MajinBook) aligns with U.S. precedent (Feist v. Rural Telephone) and EU rules (Database Directive 96/9/EC, CDSM Directive 2019/790 Art. 3), provided no full texts are redistributed (Mazières et al., 14 Nov 2025).
Text and Data Mining Exception: Explicit exemptions exist for non-commercial research TDM in EU and U.S. (DMCA, Fair Use), though case law is evolving (e.g., Kadrey v. Meta 2025).
Field Norms: Indexing systems must monitor suitability for formal evaluation, especially for high-stakes assessment in underrepresented fields or languages (Torres-Salinas et al., 2012).

BookIndex, as both a conceptual framework and a concrete instantiation, underpins scalable, structure-aware retrieval, digital bibliography, and AI-driven knowledge management for book-length documents, integrating hierarchical, semantic, and bibliometric strata to support rigorous computational humanities and information science workflows.