1 Introduction↩︎

The exponential growth of scientific literature demands tools that go beyond keyword search or citation analysis. A complementary perspective is to treat the body of literature as a continuous geometric object, a knowledge manifold, where each document occupies a position determined by its semantic content, and distances encode conceptual dissimilarity.

Our framework has three distinguishing characteristics.

1. Character \(n\)-gram TF-IDF. Unlike word-level or neural-embedding approaches, character \(n\)-gram features are robust to domain-specific compound words, abbreviations, and translingual technical notation, which are prevalent in materials science and aerospace engineering.

2. Physics-inspired interpolation (SPH). Smoothed Particle Hydrodynamics [1] treats each document as a “particle” and estimates the knowledge density at arbitrary query points via a cubic-spline kernel. This provides both an interpolated knowledge vector and its spatial gradients, collectively termed the knowledge gradient, which reveal the directions of fastest semantic change.

3. Geodesic analysis. Geodesics on the knowledge manifold are the intrinsically shortest paths between two research areas, accounting for the curvature of the semantic space induced by the SPH map. Unlike Euclidean straight lines in the projected 2-D space, geodesics traverse regions of high document density and hence represent the most natural conceptual transition routes.

The main contributions of this paper are:

1. A complete, reproducible specification of the knowledge manifold formulation with all hyper-parameters fixed (Section 3).

2. A mathematically precise formulation of the SPH knowledge interpolation and gradient (Section 4).

3. A Bayesian uncertainty quantification scheme via GPR that provides per-document contribution rates at any query point (Section 5).

4. A guaranteed-convergent geodesic solver with deterministic multi-start and energy-based success criteria (Section 7).

5. Experimental results on a composite-materials corpus (Section 8).

2 Related Work↩︎

2.0.0.1 Document representation.

Bag-of-words models, including TF-IDF [2], have long been used for information retrieval. Character \(n\)-gram models, introduced for text categorization [3], were later applied in neural contexts [4] but remain highly competitive for specialized technical corpora. Dense embedding models, including word2vec [5], GloVe [6], and Sentence-BERT [7], offer richer semantic information but sacrifice interpretability.

2.0.0.2 Manifold learning.

ISOMAP [8], UMAP [9], and t-SNE [10] embed high-dimensional point clouds in low dimensions while preserving local or global geometry. SMACOF/MDS minimizes a stress criterion [11] and provides a natural initialization for our constrained optimization. In contrast to these approaches, which are designed primarily for low-dimensional data visualization, the present framework treats the document corpus as a continuous knowledge manifold and extends the analysis to geodesic path search driven by a Riemannian metric induced from SPH interpolation.

2.0.0.3 SPH in non-physical domains.

SPH has been applied to image processing [12] and scattered-data interpolation [13]. Its application to document-space interpolation is, to our knowledge, novel.

2.0.0.4 Gaussian Process Regression on text.

GPR has been used for active learning of document labels [14] and text regression [15], but rarely for knowledge uncertainty quantification in literature analysis.

2.0.0.5 Geodesics for knowledge navigation.

Shortest-path methods on \(k\)-NN graphs [8] are standard in manifold learning; however, geodesics on a continuously interpolated manifold derived from SPH, rather than on a discrete graph, offer smoother trajectories and explicit curvature information.

3 Knowledge Manifold Formulation↩︎

3.1 Document Vectorization↩︎

Let \(\mathcal{D}=\{d_1,\dots,d_N\}\) (\(N=20\)) be the document corpus. Text is extracted in page order from each PDF; preprocessing is limited to Unicode NFKC normalization, ASCII lowercasing, and collapse of consecutive whitespace. No stop-word removal, stemming, or translation is applied.

Each document \(d_i\) is mapped to a TF-IDF feature vector \(\boldsymbol{x}_i\in\mathbb{R}^V\) using the following fixed configuration (scikit-learn TfidfVectorizer equivalent):

• analyzer="char_wb" (within-word character boundary)

• ngram_range=(4,7) (4- to 7-character grams)

• sublinear_tf=True(i.e.\(\mathrm{tf}\leftarrow 1+\log\,\mathrm{tf}\))

• norm="l2",max_features=250 000,min_df=1

After vectorization each document vector is \(\ell^2\)-normalized: \(\hat{\boldsymbol{x}}_i = \boldsymbol{x}_i/\left\|\boldsymbol{x}_i\right\|\). All subsequent similarities are cosine similarities; distances are cosine distances: \[d_{ij} = 1 - \hat{\boldsymbol{x}}_i^{\mathsf{T}}\hat{\boldsymbol{x}}_j \in [0,\,2]. \label{eq:cosine95distance}\tag{1}\]

3.2 Selection of Four Representative Documents↩︎

To anchor the two-dimensional map, four representative documents \(\{r_1,r_2,r_3,r_4\}\subset\mathcal{D}\) are chosen by exhaustive search to maximize the sum of pairwise cosine distances: \[\{r_1,r_2,r_3,r_4\} = \arg\max_{\{a,b,c,d\}\subset\mathcal{D}} \sum_{(i,j)\in\binom{\{a,b,c,d\}}{2}} d_{ij}. \label{eq:rep95selection}\tag{2}\] Ties are broken by lexicographic filename order. The four representatives are fixed to the corners of the unit square in lexicographic order: \[\boldsymbol{y}_{r_1}=(-1,-1),\quad \boldsymbol{y}_{r_2}=(1,-1),\quad \boldsymbol{y}_{r_3}=(1,1),\quad \boldsymbol{y}_{r_4}=(-1,1). \label{eq:corners}\tag{3}\]

3.3 Two-Dimensional Knowledge Map↩︎

The two-dimensional coordinates \(\{\boldsymbol{y}_i\in\mathbb{R}^2\}_{i=1}^{N}\) for the remaining 16 documents are obtained by minimizing the objective function \[J = E_{\text{stress}} + \lambda_{\text{rep}}\,E_{\text{rep}} + \lambda_{\text{var}}\,E_{\text{var}} + \lambda_{\text{ctr}}\,E_{\text{ctr}}, \label{eq:objective}\tag{4}\] with \(\lambda_{\text{rep}}=0.10\), \(\lambda_{\text{var}}=0.05\), \(\lambda_{\text{ctr}}=0.01\). The stress term follows the standard MDS formulation [11], while the repulsion, variance, and centering terms are introduced as regularizers to obtain a stable and well-spread knowledge map.

3.3.0.1 Distance-preservation (stress) term.

\[E_{\text{stress}} = \frac{ \displaystyle\sum_{i<j} \Bigl(\left\|\boldsymbol{y}_i-\boldsymbol{y}_j\right\| - \alpha\,d_{ij}\Bigr)^2 }{ \displaystyle\sum_{i<j}\!\bigl(\alpha\,d_{ij}\bigr)^2 }, \label{eq:stress}\tag{5}\] where \(\alpha\) is a fixed scale factor chosen so that the mean 2-D distance between the four corner documents equals the mean cosine distance among those four documents.

3.3.0.2 Repulsion term.

\[E_{\text{rep}} = \frac{2}{N(N-1)} \sum_{i<j}\exp\!\left(-\frac{\left\|\boldsymbol{y}_i-\boldsymbol{y}_j\right\|^2}{2\sigma^2}\right), \quad\sigma=0.35. \label{eq:repulsion}\tag{6}\]

3.3.0.3 Variance term.

\[E_{\text{var}} = \frac{(\mathrm{var}_x - \tfrac{1}{3})^2 +(\mathrm{var}_y - \tfrac{1}{3})^2}{(1/3)^2}. \label{eq:variance}\tag{7}\]

3.3.0.4 Centring term.

\[E_{\text{ctr}} = \overline{x}^2 + \overline{y}^2. \label{eq:centering}\tag{8}\]

The 16 free coordinates are constrained to \([-0.98,0.98]^2\) and optimized with L-BFGS-B, initialized from a SMACOF/MDS solution affinely aligned to the four corner documents (random_state=0). The resulting coordinate set is locked as locked_knowledge_map_coordinates.csv and reused in all subsequent analyses without recomputation.

Figure 1 illustrates the overall workflow.

4 SPH Knowledge Interpolation and Gradient↩︎

4.1 SPH Cubic-Spline Kernel↩︎

Given the two-dimensional coordinates \(\{\boldsymbol{y}_i\}\), the knowledge at an arbitrary query point \(\boldsymbol{p}\in[-1,1]^2\) is estimated via SPH. Let \(r_i=\left\|\boldsymbol{p}-\boldsymbol{y}_i\right\|\) and \(q_i=r_i/h\), where the smoothing length is \[h = \frac{\max_i\,r_i}{1.98}, \label{eq:bandwidth}\tag{9}\] so that all \(N=20\) documents contribute. The cubic-spline kernel is \[W(q) = \begin{cases} 1 - \tfrac{3}{2}q^2 + \tfrac{3}{4}q^3, & 0\le q < 1,\\[4pt] \tfrac{1}{4}(2-q)^3, & 1\le q < 2,\\[4pt] 0, & q \ge 2. \end{cases} \label{eq:kernel}\tag{10}\] Normalized SPH weights are \[w_i = \frac{W(q_i)}{\displaystyle\sum_{j=1}^N W(q_j)}. \label{eq:sph95weights}\tag{11}\]

4.2 Interpolated Knowledge Vector↩︎

The interpolated (virtual) knowledge vector at \(\boldsymbol{p}\) is \[\boldsymbol{v}(\boldsymbol{p}) = \mathrm{normalize}\!\left(\sum_{i=1}^N w_i\,\hat{\boldsymbol{x}}_i\right). \label{eq:sph95interp}\tag{12}\] Linguistic characterization is obtained by extracting the top-ranked TF-IDF features of \(\boldsymbol{v}(\boldsymbol{p})\); no concepts absent from the TF-IDF vocabulary are introduced.

4.3 Knowledge Gradient↩︎

The knowledge gradient is defined as the spatial derivative of the normalized SPH map \(\boldsymbol{v}(x,y)\): \[\nabla\boldsymbol{v} = \left(\dfrac{\partial \boldsymbol{v}}{\partial x},\;\dfrac{\partial \boldsymbol{v}}{\partial y}\right). \label{eq:grad}\tag{13}\] Partial derivatives are evaluated by central differences with step \(\delta=10^{-3}\): \[\dfrac{\partial \boldsymbol{v}}{\partial x}\approx \frac{\boldsymbol{v}(x+\delta,y)-\boldsymbol{v}(x-\delta,y)}{2\delta}.\]

4.3.0.1 Directional gradients.

Three directional knowledge gradients are computed at the evaluation point \(\boldsymbol{p}=(0.25,\,0.75)\): \[\begin{align} \boldsymbol{g}_{0^\circ} &= \dfrac{\partial \boldsymbol{v}}{\partial x},\tag{14}\\ \boldsymbol{g}_{45^\circ} &= \frac{1}{\sqrt{2}}\!\left(\dfrac{\partial \boldsymbol{v}}{\partial x}+\dfrac{\partial \boldsymbol{v}}{\partial y}\right),\tag{15}\\ \boldsymbol{g}_{90^\circ} &= \dfrac{\partial \boldsymbol{v}}{\partial y}.\tag{16} \end{align}\] For each direction the TF-IDF features that increase along the positive direction and those that decrease are identified and linguistically interpreted separately. Pairwise directional similarity is quantified by both inner product and cosine similarity: \[\mathrm{sim}(\boldsymbol{g}_{\theta_1},\boldsymbol{g}_{\theta_2}) = \frac{\boldsymbol{g}_{\theta_1}^{\mathsf{T}}\boldsymbol{g}_{\theta_2}}{\left\|\boldsymbol{g}_{\theta_1}\right\|\left\|\boldsymbol{g}_{\theta_2}\right\|}. \label{eq:dir95sim}\tag{17}\]

5 Gaussian Process Regression↩︎

5.1 Model↩︎

Direct GPR in \(V\)-dimensional TF-IDF space is computationally prohibitive. We first compress the document vectors to 10 latent dimensions via Truncated SVD (random_state=0) and fit kernel hyper-parameters in this low-dimensional space. The kernel is \[k(\boldsymbol{y},\boldsymbol{y}') = c_0 \cdot \exp\!\left[-\tfrac{1}{2}\left( \frac{(x-x')^2}{\ell_x^2}+\frac{(y-y')^2}{\ell_y^2} \right)\right] + c_{\text{white}}\,\delta(\boldsymbol{y},\boldsymbol{y}'), \label{eq:kernel95gpr}\tag{18}\] where \(c_0\) (ConstantKernel), \(\ell_x\), \(\ell_y\) (anisotropic RBF), and \(c_{\text{white}}\) (WhiteKernel) are optimized with n_restarts_optimizer=10, random_state=0, normalize_y=False.

5.2 Posterior Mean and Uncertainty↩︎

The GPR posterior mean at \(\boldsymbol{p}\) is expressed as a linear combination of document vectors in the original TF-IDF space: \[\hat{\boldsymbol{v}}_{\text{GPR}}(\boldsymbol{p}) = \sum_{i=1}^N \beta_i\,\hat{\boldsymbol{x}}_i, \label{eq:gpr95mean}\tag{19}\] where \(\{\beta_i\}\) are the posterior linear coefficients derived from the learned kernel. Uncertainty is reported as the GPR posterior standard deviation \(\sigma_{\text{post}}(\boldsymbol{p})\), the prior standard deviation \(\sigma_{\text{prior}}\), and the relative uncertainty \(\sigma_{\text{post}}/\sigma_{\text{prior}}\).

5.3 Document Contribution Rates↩︎

The contribution of document \(d_i\) to the prediction at \(\boldsymbol{p}\) is \[c_i = \frac{|\beta_i|\cdot\max\!\bigl(\hat{\boldsymbol{x}}_i^{\mathsf{T}}\hat{\boldsymbol{v}}_{\text{GPR}},\,0\bigr)}{\sum_j |\beta_j|\cdot\max\!\bigl(\hat{\boldsymbol{x}}_j^{\mathsf{T}}\hat{\boldsymbol{v}}_{\text{GPR}},\,0\bigr)}. \label{eq:contribution}\tag{20}\] A negative \(\beta_i\) does not imply irrelevance; it indicates a corrective contribution that adjusts the local mean field.

6 Virtual Paper Generation↩︎

At the evaluation point \(\boldsymbol{p}=(0.25,\,0.75)\), the SPH interpolated vector \(\boldsymbol{v}(\boldsymbol{p})\) and GPR posterior mean \(\hat{\boldsymbol{v}}_{\text{GPR}}\) are fused to characterize the hypothetical research area at that location. A virtual paper abstract of approximately 500 characters (Japanese) is generated subject to the following constraints:

1. The abstract describes the target object, methodology, expected results, and significance.

2. No concepts absent from the TF-IDF vocabulary are introduced.

3. No fabricated experimental data or non-existent numerical values are included.

4. The writing style follows the conventions of academic paper abstracts.

7 Geodesic Analysis↩︎

7.1 SPH-Induced Riemannian Metric↩︎

The SPH map \(\boldsymbol{v}(x,y):\mathbb{R}^2\to S^{V-1}\) (normalized TF-IDF sphere) induces a pull-back Riemannian metric on the knowledge map: \[g_{ij}(\boldsymbol{p}) = \left\langle\dfrac{\partial \boldsymbol{v}}{\partial p^i},\dfrac{\partial \boldsymbol{v}}{\partial p^j}\right\rangle, \quad p^1=x,\;p^2=y. \label{eq:metric}\tag{21}\] Central differences (\(\delta=10^{-3}\)) are used to evaluate the partial derivatives. When the metric is numerically singular, regularization \(\boldsymbol{g}\leftarrow\boldsymbol{g}+\varepsilon\boldsymbol{I}\) with \(\varepsilon=10^{-10}\) is applied, and the occurrence is recorded.

7.2 Discrete Path Energy↩︎

A geodesic from \(\boldsymbol{p}_0=(0.25,\,0.75)\) to \(\boldsymbol{p}_L=(-0.3,\,0.35)\) is represented by \(L+1=31\) equally indexed points \(\{\boldsymbol{p}_0,\boldsymbol{p}_1,\dots,\boldsymbol{p}_L\}\). The discrete path energy is \[E = \sum_{k=0}^{L-1} \Delta\boldsymbol{p}_k^{\mathsf{T}}\, \boldsymbol{g}\!\left(\frac{\boldsymbol{p}_k+\boldsymbol{p}_{k+1}}{2}\right) \Delta\boldsymbol{p}_k, \quad \Delta\boldsymbol{p}_k = \boldsymbol{p}_{k+1}-\boldsymbol{p}_k. \label{eq:path95energy}\tag{22}\] The interior 29 points are the optimization variables, constrained to \([-1,1]^2\). The endpoints are held fixed.

7.3 Multi-Start Solver↩︎

To prevent the solver from reporting the straight-line initial path as the geodesic (a known failure mode), we employ seven deterministic candidates. Let \(\hat{\boldsymbol{n}}_\perp\) be the unit vector perpendicular to the straight line from \(\boldsymbol{p}_0\) to \(\boldsymbol{p}_L\). Candidate \(c\) is \[\boldsymbol{p}_k^{(c)} = \bar{\boldsymbol{p}}_k + a_c\sin\!\left(\frac{\pi k}{L}\right)\hat{\boldsymbol{n}}_\perp, \quad k=1,\dots,L-1, \label{eq:candidates}\tag{23}\] where \(\bar{\boldsymbol{p}}_k\) is the straight-line interpolation and the amplitudes are \(a_0=0,\;a_1=0.01,\;a_2=-0.01,\;a_3=0.03,\;a_4=-0.03,\;a_5=0.05,\;a_6=-0.05\). Points that violate \([-1,1]^2\) are clipped.

Each candidate is optimized with L-BFGS-B using maxiter=2000, maxfun=200 000, ftol=1e-12, gtol=1e-8, maxls=50.

7.4 Adoption Criterion↩︎

1. If any candidate achieves optimizer_success=True, the one with minimum final energy is adopted.

2. If no candidate succeeds but some achieve \(E_{\text{final}}<E_{\text{straight}}-10^{-12}\), the best is adopted with status energy_improved_but_optimizer_not_success.

3. Otherwise, the status is set to failed; the straight line is not reported as a geodesic.

7.5 Metric Variation Along the Geodesic↩︎

To characterize the curvature of the knowledge space without computing the full Riemann curvature tensor, the following quantities are evaluated at each path point (or midpoint): \[\kappa_{\text{aniso}}(\boldsymbol{p}) = \frac{\lambda_{\max}(\boldsymbol{g}(\boldsymbol{p}))}{\lambda_{\min}(\boldsymbol{g}(\boldsymbol{p}))}, \quad \kappa_{\text{couple}}(\boldsymbol{p}) = \frac{|g_{xy}|}{\sqrt{g_{xx}\,g_{yy}}}, \label{eq:curvature95proxy}\tag{24}\] where \(\lambda_{\max}(\boldsymbol{g}(\boldsymbol{p}))\) and \(\lambda_{\min}(\boldsymbol{g}(\boldsymbol{p}))\) denote the maximum and minimum eigenvalues of the metric tensor \(\boldsymbol{g}(\boldsymbol{p})\), respectively. These quantities are evaluated together with \(\left\|d\boldsymbol{g}/ds\right\|\), \(\mathrm{tr}\,\boldsymbol{g}\), and \(\det\boldsymbol{g}\) as functions of arc length \(s\).

8 Experimental Results↩︎

8.1 Corpus and Processing↩︎

The corpus consists of \(N=20\) peer-reviewed papers from the fields of fiber-reinforced composite materials and aerospace structural mechanics. Text was extracted in page order from PDF files, including captions and references; no OCR was applied. The TF-IDF vocabulary after applying the 250 000-feature ceiling comprised \(V=227\,213\) character \(n\)-grams (4-7 grams, min_df=1). Principal settings are summarized in Table 1.

8.2 Two-Dimensional Knowledge Map↩︎

The four corner representatives selected by maximum pairwise cosine-distance sum (Eq. 2 ) are P04 (laminate stiffness/CDM) at \((-1,-1)\), P06 (aeroelastic wing NSGA-II) at \((1,-1)\), P07 (epoxy oxidation/ReaxFF) at \((1,1)\), and P18 (P3HT thermal conductivity) at \((-1,1)\). Figure 2 shows the locked two-dimensional knowledge map used for all subsequent interpolation, gradient, and geodesic analyses. The map reproduces the expected semantic separation: molecular/polymer papers cluster in the upper half while structural/damage papers concentrate in the lower half.

8.3 SPH Interpolation and GPR at \(\mathbf{p}=(0.25,0.75)\)↩︎

The smoothing length \(h=1.0862\) (Eq. 9 ) ensures all 20 documents contribute to the SPH estimate. Table 2 lists the top contributors; Figure 3 shows the GPR posterior contribution rates.

The learned GPR kernel was \(0.231^2\!\times\!\mathrm{RBF}([0.628,0.538])+\mathrm{White}(0.014)\). Posterior standard deviation \(\sigma_{\mathrm{post}}=0.1440\); relative uncertainty \(\sigma_{\mathrm{post}}/\sigma_{\mathrm{prior}}=0.5539\), indicating moderate but meaningful corpus support at this point. Documents P14 and P12 dominate the GPR prediction with normalized contribution rates of 0.381 and 0.331, respectively (Figure 3). Negative posterior coefficients \(\beta_i\) are corrective local-field terms and do not indicate that those papers are unimportant.

8.4 Direction Vectors as Linguistic Information↩︎

Table 3 summarizes the direction-vector language at \(\boldsymbol{p}=(0.25,0.75)\). The \(x\)- and \(y\)-gradient norms are 0.4785 and 0.4582; their cosine similarity is 0.0296, confirming near-orthogonality of the two coordinate directions in the high-dimensional semantic field. Figure 4 visualises the three canonical direction vectors.

8.5 Geodesic from \((0.25,0.75)\) to \((-0.3,0.35)\)↩︎

Candidate 5 (sinusoidal perturbation, amplitude \(a_5=0.05\)) was adopted; optimization converged with message “CONVERGENCE: RELATIVE REDUCTION OF F \(\le\) FACTR*EPSMCH”. Table 4 reports the full comparison. Figures 5 and 6 show the optimized path and the metric variation along it.

9 Discussion↩︎

9.0.0.1 Character \(n\)-gram vs.word-level features.

Character 4-7-gram features effectively capture compound technical terms, unit symbols, and cross-lingual cognates without requiring domain-specific tokenization. The large vocabulary ceiling (\(V=250\,000\)) ensures that even rare but discriminative character sequences are retained.

9.0.0.2 SPH vs.global interpolation.

The cubic-spline SPH kernel has compact support (\(q<2\)), but by construction of the smoothing length \(h\) (Eq. 9 ) all documents contribute to each query point, making it equivalent to global interpolation while retaining the SPH framework’s gradient formulae. This choice prioritises robustness to corpus sparsity.

9.0.0.3 GPR complementarity.

SPH provides a deterministic, physics-inspired interpolation; GPR provides a Bayesian posterior with uncertainty. Together they offer two independent perspectives on the knowledge at a query point, and their agreement or disagreement serves as a consistency check. At \(\boldsymbol{p}=(0.25,0.75)\), the relative uncertainty \(0.5539\) indicates moderate but not saturated corpus support, consistent with the point lying between several moderately close documents.

9.0.0.4 Why linguistic direction vectors matter.

A two-dimensional embedding is usually difficult to interpret because its axes are arbitrary. The present method avoids manually naming the axes: it computes the semantic derivative at the point of interest and lets the feature space describe what each local direction means. At \(\boldsymbol{p}=(0.25,0.75)\), the cosine similarity between the \(x\)- and \(y\)-direction vectors is only \(0.0296\), confirming near-orthogonality. The \(x\)-direction separates resin-characterization vocabulary from resin-structure vocabulary; the \(y\)-direction separates molecular-structure language from damage/fracture language. A researcher navigating the map can therefore move in specific conceptual directions with predictable linguistic consequences.

9.0.0.5 How to read the geodesic.

The optimized geodesic reduces path energy by \(8.14\times10^{-6}\) relative to the straight baseline, with a maximum lateral deviation of \(0.014\). The small improvement is itself informative: the local region between the chosen endpoints is relatively smooth under the learned metric. The metric-variation plot (Figure 6) reveals that \(\left\|d\boldsymbol{g}/ds\right\|\) rises toward the endpoint \(\boldsymbol{q}=(-0.30,0.35)\), indicating increasing semantic curvature near the polymer/resin cluster. The strict reporting rule (no straight-line fallback when optimization fails) is essential for reproducibility.

9.0.0.6 Geodesic energy and curvature.

The path energy (Eq. 22 ) quantifies the “semantic cost” of a conceptual trajectory. Paths that pass through densely populated regions of the knowledge map are geometrically shorter in the induced Riemannian metric, because the SPH map changes slowly in such regions, making \(\boldsymbol{g}\) small. Geodesics therefore naturally follow the contours of the existing literature.

9.0.0.7 Reproducibility.

All random seeds are fixed (random_state=0); the 2-D coordinate set is locked before subsequent analyses. Potential sources of numerical non-reproducibility (PDF extraction library, BLAS/LAPACK variant, and L-BFGS-B stopping tolerance) are recorded in manifest.json.

10 Conclusion↩︎

We have presented the knowledge manifold, a complete, reproducible formulation for geometric analysis of academic literature. The key contributions are a constrained 2-D knowledge map with physics-motivated regularization, SPH-based knowledge interpolation and directional gradient analysis, Bayesian uncertainty quantification via GPR, virtual paper generation from the estimated knowledge vector, and a guaranteed- convergent geodesic solver with deterministic multi-start initialization. Experiments on a composite-materials corpus demonstrate that the proposed method recovers meaningful semantic structure and enables principled gap detection and hypothesis generation. While manifold learning methods such as UMAP and t-SNE are primarily used for low-dimensional visualization, the proposed method is distinctive in that it treats the literature space as a continuous knowledge manifold and further performs geodesic analysis based on an SPH-induced Riemannian metric.

Future work includes: (i) extension to multilingual corpora; (ii) integration of citation-network topology as an additional geometric signal; (iii) real-time interactive navigation of the knowledge manifold; and (iv) automatic identification of high-uncertainty regions as research opportunity indicators.

References↩︎

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1. E-mail: tomonaga.okabe.a8@tohoku.ac.jp↩︎

2. Corresponding author. E-mail: komatsu@tohoku.ac.jp↩︎