# Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction

arXiv: https://arxiv.org/abs/2608.01936 · HTML: https://mlsanity.com/paper/2608.01936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 172 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [The Linear Representation Hypothesis and the Geometry of Large Language Models](https://mlsanity.com/paper/2311.03658/) (2023)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Simple Unsupervised Keyphrase Extraction using Sentence Embeddings](https://mlsanity.com/paper/1801.04470/) (2018)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases](https://mlsanity.com/paper/1810.05241/) (2018)
- [Unsupervised Keyphrase Extraction with Multipartite Graphs](https://mlsanity.com/paper/1803.08721/) (2018)
- [Revisiting the Platonic Representation Hypothesis: An Aristotelian View](https://mlsanity.com/paper/2602.14486/) (2026)
- [A Review of Keyphrase Extraction](https://mlsanity.com/paper/1905.05044/) (2019)
- [MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2110.06651/) (2021)
- [ChatGPT vs State-of-the-Art Models: A Benchmarking Study in Keyphrase Generation Task](https://mlsanity.com/paper/2304.14177/) (2023)
- [PromptRank: Unsupervised Keyphrase Extraction Using Prompt](https://mlsanity.com/paper/2305.04490/) (2023)
- [A Comprehensive Review of Community Detection in Graphs](https://mlsanity.com/paper/2309.11798/) (2023)
- [Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization](https://mlsanity.com/paper/1306.4886/) (2013)
- [The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias](https://mlsanity.com/paper/2002.10283/) (2020)
- [Large network community detection by fast label propagation](https://mlsanity.com/paper/2209.13338/) (2022)
- [Enhancing Keyphrase Extraction from Long Scientific Documents using Graph Embeddings](https://mlsanity.com/paper/2305.09316/) (2023)
- [Network Disruption via Continuous Batch Removal: The Case of Sicilian Mafia](https://mlsanity.com/paper/2310.09987/) (2023)

## Abstract (arXiv)

The subject matter of this paper is twofold. One is to review the theoretical foundation of a specific type of Semantic Networks (SNs) representing textual non-propositional knowledge. The other involves proposing a framework (ClueNetwork) for ranking candidate SNs generated through various Semantic Network Construction (SNC) processes for the type. In the first fold, it is clarified that the type serves as clues, not surrogates, of reality, making gold standards elusive. Then, it is discussed why this type nevertheless holds scientific legitimacy in terms of abduction. Grounded in this legitimacy, the three main stages of SNC, comprising Automatic Keyphrase Extraction (AKE), Edge Weighting (EW), and Community Detection (CD), are reviewed alongside their objectives and operations. In the second fold, evaluation criteria (comprising two established and one reformulated) for achieving the objectives are first defined and justified, followed by illustrative experiments based on the criteria. Thereafter, SNC is reformulated as a Process Optimization Problem (POP), and its global objective function that integrates the local criteria is defined and justified. Based on these, ClueNetwork is ultimately proposed.