Structured Video Knowledge Base

• Structured Video Knowledge Base is an organized system that represents, queries, and reasons about video content using explicit schemas like spatio-temporal graphs, verb–argument tuples, and RDF triples.
• It employs multi-stage extraction pipelines and multi-modal alignment of visual, audio, and textual cues to enable fine-grained, machine-interpretable access to video phenomena.
• Joint reasoning with embedding architectures and graph fusion techniques enhances retrieval accuracy and supports complex tasks such as video question answering and procedural search.

A structured video knowledge base (SVKB) is an organized system for representing, querying, and reasoning about the semantic contents, relations, and procedural or factual knowledge present in video corpora. SVKBs integrate multi-modal signals—visual, audio, textual—and encode their interrelations using explicit schemas such as spatio-temporal graphs, RDF triples, or verb–argument tuples. This approach facilitates fine-grained, machine-interpretable access and multi-video reasoning, supporting downstream tasks such as video question answering (QA), procedural search, and content-based inference.

1. Formal Representations and Schemas

Structured video knowledge bases employ multiple formal schema paradigms, each suited to particular domains and extraction tasks.

• Spatio-Temporal Graphs: Each video is mapped to a directed graph $G = (V, E)$, where nodes represent objects, actors, or regions, and edges encode spatial (within-frame) or temporal (across-frame) relations. Node features combine appearance (e.g., OpenCLIP embeddings), motion descriptors (e.g., tracked bounding-box velocity), and semantic tag embeddings (e.g., subject–predicate–object from scene graphs). Spatial edges are instantiated where subject–predicate–object triplets exist, while temporal edges track object identity across frames (He et al., 16 Sep 2025).
• Verb–Argument Tuples: Particularly for instructional or procedural video (e.g., cooking), procedures are represented as ordered sequences of open-vocabulary tuples $(\text{verb}, \text{arg}_1, \ldots, \text{arg}_k)$, each grounded in video segments and transcript tokens. This structure explicitly links procedural steps to media time segments (Xu et al., 2020).
• RDF/Linked Data Ontologies: Using RDF triples and minimal vocabularies (e.g., prohow:Process, ex:VideoSegment), each process, step, and video segment receives a URI. Additional annotation nodes encode action labels, object use, and segment boundaries, enabling federated SPARQL querying over both text- and video-derived knowledge (Pareti et al., 2014).
• Video–KG Heterogeneous Graphs: Short videos are treated as entities and linked to tags, detected objects, ASR transcripts, and external commonsense/factual KG entities via a wide set of relation types (e.g., isA, PartOf, Occupation, Before, After) (Deng et al., 2022).

2. Extraction and Construction Pipelines

SVKB creation proceeds via data-driven multi-stage extraction pipelines:

• Scene Segmentation and Feature Extraction: Tools such as PySceneDetect partition videos into scenes. From scene keyframes, dense captioning is performed to obtain descriptive sentences; these are parsed to extract subject–predicate–object scene graphs, localized and tracked across frames using, for example, GroundingDINO and DEVA (He et al., 16 Sep 2025).
• Multi-Modal Alignment: Video frames supply visual features via ViT or ResNet-50, while ASR transcribes audio for NLP-based step, action, or argument extraction. Optical character recognition (OCR) augments tag and object extraction. Entity linking harnesses all modalities for high-accuracy alignment with external KGs (Deng et al., 2022).
• Structured Representation Assembly:
  • For procedural domains, key clips containing core actions are first selected (using semantic role labeling, domain lexicons, and BERT/ResNet multimodal classifiers), then action tuples are extracted and aligned to time segments (Xu et al., 2020).
  • In linked data paradigms, shot detection, speech-to-text, imperative parsing, and action recognition enable segment-to-process annotation, with output directly serialized as RDF triples (Pareti et al., 2014).
• Graph Fusion and Multi-Video Reasoning: To address spatio-temporal incompleteness in individual videos, related videos are retrieved and their graphs fused via attention mechanisms (e.g., hierarchical frame graph attention and cross-graph attention), producing compact node representations for collaborative reasoning (He et al., 16 Sep 2025).

3. Joint Reasoning and Embedding Architectures

Recent models unify video understanding and knowledge graph embedding by aligning all modalities into a shared vector space:

• Transformer Backbones: Visual, text, and audio streams are tokenized and embedded (typically using ViT and BERT architectures; BERT-large with H=1024, seq-len=128), then projected to a fixed-dimensional shared space (e.g., ℝ¹²⁸) (Deng et al., 2022).
• Contrastive Pretraining and Alignment: Video–tag pairs are CLIP-aligned via InfoNCE loss, ensuring isomorphic video–text representations (Deng et al., 2022).
• KG Integration: Videos and tags are treated as nodes in KG triplets (h, r, t), scored with translation-style energy functions $f_r(h, t) = \|h + r - t\|_2$, supporting both direct entity- and relation-driven inference (Deng et al., 2022).
• Multi-Video Collaborative Reasoning: In multi-video QA, stacking and fusing spatio-temporal graphs across videos leverages information redundancy, suppresses hallucination, and yields significant gains in zero-shot Video-QA accuracy (He et al., 16 Sep 2025).

4. Querying, Retrieval, and Reasoning Capabilities

SVKBs support a variety of retrieval and inference tasks:

• Video–Tag/Tag–Video Retrieval (VT/TV): Retrieval by semantic similarity in embedding space ($\cos(z_V, z_T)$) (Deng et al., 2022).
• Video-Relation-Tag/Video-Relation-Video (VRT/VRV): Given a video and a relation, return the most relevant tail tag/entity, or retrieve videos related via that relation (e.g., find all videos depicting Galileo's occupation). Metrics include Hits@k and MRR (Deng et al., 2022).
• Procedural Step and Argument Querying: Fine-grained questions such as “Which tools were used to bake?” or “What action precedes pouring?” are enabled by explicit grounding of steps, arguments, and segment links (Xu et al., 2020).
• SPARQL over RDF Triples: Structured querying of step subgraphs, procedural dependencies, and time-anchored segments using federated triples for both video and text resources (Pareti et al., 2014).
• Cross-Video Inference: Fusion architectures support reasoning over ensembles of related videos, enabling domain-specific generalization and hallucination reduction (He et al., 16 Sep 2025).

5. Evaluation Protocols and Empirical Results

SVKB research employs a broad suite of metrics:

• Procedural Extraction (Cooking Domain): Verb–argument tuple extraction from cooking videos (356 videos, 15,523 clips) achieves fuzzy F1=51.7% (verbs) and partial-fuzzy F1=41.9% (arguments), with clear performance dependence on accurate key-clip identification (Xu et al., 2020). Cohen’s κ=0.83 on key-step annotation and Jaccard indices >0.7 for verb/argument consistency demonstrate strong inter-annotator agreement.
• Video–KG Embedding: In a benchmark with 5.7M videos and 832K triplets, joint video–KG embedding outperforms CLIP+TransE by +42.36% (VRV Hits@10) and +17.73% (VRT Hits@10), illustrating the impact of explicit KG integration (Deng et al., 2022).
• Multi-Video Graph Fusion: Structured graph fusion with 5 related videos raises VideoQA accuracy by +2–4% over single-video baselines, with ablation confirming improvements from both graph structuring and fusion modules (He et al., 16 Sep 2025).
• Linked Data Usability: Manual segment-step mapping yields segmentation F1>0.90 (with shot-boundary tools), and MAP≈0.68 for step-queries. User studies show that RDF-linked video browsers can accelerate task completion by 20–30% compared to text-only interfaces (Pareti et al., 2014).

6. Schema Design, Knowledge Base Organization, and Best Practices

Effective SVKBs employ the following schema and organizational techniques:

• Explicit Grounding: All steps, arguments, and actions are grounded to video segments and transcript tokens, ensuring verifiability and supporting fine-grained queries (Xu et al., 2020).
• Core Entity Tables: Each knowledge base features explicit tables linking tasks/recipes, procedural steps (with indices, verbs, arguments, and time stamps), arguments (with role, text span, and optional ontology links), and media segment metadata (Xu et al., 2020Pareti et al., 2014).
• Open Vocabulary and Ontology Enrichment: No closed class sets are assumed; canonicalization and post-hoc mapping to ontologies (e.g., ingredient lists) is performed to support normalization and cross-instance retrieval (Xu et al., 2020).
• Minimal Modular Ontologies: RDF-based systems use a compact vocabulary: classes for Process, Execution, VideoSegment, Annotation, and minimal properties for steps, methods, dependencies, and segment labeling (Pareti et al., 2014).
• Unified Vector Space for Multi-Modal Fusion: End-to-end models align video, text, and audio features into a single space, facilitating both KG embedding and retrieval (Deng et al., 2022).
• Negative Sampling and Multi-Objective Training: In KG embedding, margin-ranking with negative samples and balanced loss terms ($\lambda_1, \lambda_2, \lambda_3$) are critical for stability and generalization (Deng et al., 2022).

7. Challenges, Limitations, and Research Directions

Open challenges in SVKB construction and application include:

• Open Vocabulary and Lexicon Sparsity: Procedural domains involve diverse, ambiguous verbs and arguments (347 unique verbs, 1,237 arguments in 356 videos). Closed-label classifiers generalize poorly in such open settings (Xu et al., 2020).
• Ellipsis, Coreference, and Noisy Modalities: Significant portions of actions and arguments are omitted or pronominalized in speech, requiring visual coreference resolution and robust multi-modal grounding (~14% arguments require cross-modal disambiguation) (Xu et al., 2020).
• Segment Alignment and Procedural Semantics: Automatic segmentation and alignment between video, transcript, and procedural schema remains brittle, especially in settings with asides or hypothetical utterances. Domain lexicon filtering and argument-role constraints offer partial mitigation (Xu et al., 2020).
• Scalability and Heterogeneity: Large-scale SVKBs integrate millions of videos and hundreds of thousands of KG entities/relations; careful schema, storage, and indexing models are essential (Deng et al., 2022).
• Fusion and Reasoning Over Multiple Videos: Hallucination and answer quality degrade if unrelated videos are fused; optimal selection and attention mechanisms remain an active area of research (He et al., 16 Sep 2025).

A plausible implication is that future SVKB systems will couple end-to-end multi-modal foundation models with explicit, globally linked knowledge representations, leveraging advances in cross-modal grounding, open-world entity linking, and unsupervised procedural abstraction for efficient large-scale deployment and advanced video-centric reasoning.