Hybrid Representation Builder

The platform does not stop at one semantic method. It must build a governed representation ensemble.

Input

Each corpus artifact can emit multiple channels from a shared substrate.

Let the input representation be:

$$x\in {\mathbb{R}}^d$$

Channel families

Doc2Vec

A document encoder produces a document-level embedding:

$$r_{\mathrm{d}2\mathrm{v}}=E_{\mathrm{d}2\mathrm{v}}(d)$$

Word2Vec and subvectors

Base token embeddings can be aggregated into subvector families:

$$r_{\mathrm{w}2\mathrm{v}}^{(g)}=\frac{1}{|g|}\sum _{w\in g}e(w)$$

where $g$ may be a topic constituent group, phrase family, ontology label family, or another controlled subvector set.

LSI

Linear projection channel:

$$r_{\mathrm{lsi}}=W_{\mathrm{lsi}}x$$

LDA

Bayesian topic inference channel:

$$r_{\mathrm{lda}}=\mathrm{Infer}(x)$$

Neural

Neural semantic encoder:

$$r_{\mathrm{nn}}=\sigma (Wx+b)$$

Fusion

The platform supports a fused representation rather than a single opaque score:

$$r_{\mathrm{fusion}}=r_{\mathrm{d}2\mathrm{v}}\oplus r_{\mathrm{w}2\mathrm{v}}\oplus r_{\mathrm{lsi}}\oplus r_{\mathrm{lda}}\oplus r_{\mathrm{nn}}$$

where $\oplus$ denotes controlled channel composition rather than naive concatenation by default.

Outputs

These channels feeds downstream capabilities:

• similarity
• clustering
• labeling
• monitoring
• graph edge construction
• temporal topology evolution

Governance requirement

A hybrid builder remains auditable. The platform can say:

• which channels contributed
• what each channel means
• what changed between builds
• how graph edges were derived
• what evidence supports the semantic result