XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models

• URL: http://arxiv.org/abs/2507.04070v1
• Date: Sat, 05 Jul 2025 15:21:42 GMT
• Title: XISM: an eXploratory and Interactive Graph Tool to Visualize and Evaluate Semantic Map Models
• Authors: Zhu Liu, Zhen Hu, Lei Dai, Ying Liu,
• Abstract summary: Semantic map models represent meanings or functions as nodes in a graph constrained by the local connectivity hypothesis.<n>Traditionally built manually, they are inefficient for large datasets and lack visualization and evaluation tools.<n>This paper introduces XISM, an interactive tool based on our prior algorithm, which constructs semantic maps from user data via a top-down approach.
• Score: 10.479648773745442
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Semantic map models represent meanings or functions as nodes in a graph constrained by the local connectivity hypothesis, with edges indicating their associations. Widely used in typological linguistics, these models compare interrelated meanings across languages. Traditionally built manually in a bottom-up manner, they are inefficient for large datasets and lack visualization and evaluation tools. This paper introduces XISM, an interactive tool based on our prior algorithm, which constructs semantic maps from user data via a top-down approach, displays candidate maps, and evaluates them using multiple metrics. Users can refine maps by editing edges, combining data-driven efficiency with expert knowledge. This human-in-the-loop design benefits both typologists and computational linguists. The system https://770103knev48.vicp.fun/ and a demonstration video https://youtu.be/S-wsVDF2HSI?si=1OrcF41tRznaifhZ are publicly available.

Related papers

• An Automatic Graph Construction Framework based on Large Language Models for Recommendation [49.51799417575638]
  We introduce AutoGraph, an automatic graph construction framework based on large language models for recommendation.<n>LLMs infer the user preference and item knowledge, which is encoded as semantic vectors.<n>Latent factors are incorporated as extra nodes to link the user/item nodes, resulting in a graph with in-depth global-view semantics.
  arXiv  Detail & Related papers  (2024-12-24T07:51:29Z)
• A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Crosslinguistic Case Study of Supplementary Adverbs [50.982315553104975]
  Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms.<n>Most SMMs are manually built by human experts using bottom-up procedures.<n>We propose a novel graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner.
  arXiv  Detail & Related papers  (2024-12-02T12:06:41Z)
• LICO: Explainable Models with Language-Image Consistency [39.869639626266554]
  This paper develops a Language-Image COnsistency model for explainable image classification, termed LICO. We first establish a coarse global manifold structure alignment by minimizing the distance between the distributions of image and language features. We then achieve fine-grained saliency maps by applying optimal transport (OT) theory to assign local feature maps with class-specific prompts.
  arXiv  Detail & Related papers  (2023-10-15T12:44:33Z)
• Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
  We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
  arXiv  Detail & Related papers  (2023-05-04T16:04:41Z)
• Learnable Graph Matching: A Practical Paradigm for Data Association [74.28753343714858]
  We propose a general learnable graph matching method to address these issues. Our method achieves state-of-the-art performance on several MOT datasets. For image matching, our method outperforms state-of-the-art methods on a popular indoor dataset, ScanNet.
  arXiv  Detail & Related papers  (2023-03-27T17:39:00Z)
• Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation [15.55971302563369]
  A key feature of neural models is that they can produce semantic vector representations of objects (texts, images, speech, etc.) ensuring that similar objects are close to each other in the vector space. While much work has focused on learning representations for other modalities, there are no aligned cross-modal representations for text and knowledge base elements.
  arXiv  Detail & Related papers  (2023-02-28T17:39:43Z)
• Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods [6.018950511093273]
  Saliency maps can explain a neural model's predictions by identifying important input features. We formalize the underexplored task of translating saliency maps into natural language. We compare two novel methods (search-based and instruction-based verbalizations) against conventional feature importance representations.
  arXiv  Detail & Related papers  (2022-10-13T17:48:15Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Spatial Pyramid Based Graph Reasoning for Semantic Segmentation [67.47159595239798]
  We apply graph convolution into the semantic segmentation task and propose an improved Laplacian. The graph reasoning is directly performed in the original feature space organized as a spatial pyramid. We achieve comparable performance with advantages in computational and memory overhead.
  arXiv  Detail & Related papers  (2020-03-23T12:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.