Trajectories of Change: Approaches for Tracking Knowledge Evolution

• URL: http://arxiv.org/abs/2501.00391v1
• Date: Tue, 31 Dec 2024 11:09:37 GMT
• Title: Trajectories of Change: Approaches for Tracking Knowledge Evolution
• Authors: Raphael Schlattmann, Malte Vogl,
• Abstract summary: We explore local vs. global evolution of knowledge systems through the framework of socio-epistemic networks (SEN) We first use information-theoretic measures based on relative entropy to detect semantic shifts, assess their significance, and identify key driving features. Second, variations in document embedding reveal changes in semantic neighbourhoods, tracking how concentration of similar documents increase, remain stable, or disperse.
• Score: 0.0
• License:
• Abstract: We explore local vs. global evolution of knowledge systems through the framework of socio-epistemic networks (SEN), applying two complementary methods to a corpus of scientific texts. The framework comprises three interconnected layers-social, semiotic (material), and semantic-proposing a multilayered approach to understanding structural developments of knowledge. To analyse diachronic changes on the semantic layer, we first use information-theoretic measures based on relative entropy to detect semantic shifts, assess their significance, and identify key driving features. Second, variations in document embedding densities reveal changes in semantic neighbourhoods, tracking how concentration of similar documents increase, remain stable, or disperse. This enables us to trace document trajectories based on content (topics) or metadata (authorship, institution). Case studies of Joseph Silk and Hans-J\"urgen Treder illustrate how individual scholar's work aligns with broader disciplinary shifts in general relativity and gravitation research, demonstrating the applications, limitations, and further potential of this approach.

Related papers

• Integration of Contextual Descriptors in Ontology Alignment for Enrichment of Semantic Correspondence [13.69268253901738]
  A formalization was developed that enables the integration of essential and contextual descriptors to create a comprehensive knowledge model. The hierarchical structure of the semantic approach and the mathematical apparatus for analyzing potential conflicts between concepts are demonstrated.
  arXiv  Detail & Related papers  (2024-11-28T12:59:32Z)
• Harmonizing knowledge Transfer in Neural Network with Unified Distillation [20.922545937770085]
  Knowledge distillation (KD) is known for its ability to transfer knowledge from a cumbersome network (teacher) to a lightweight one (student) without altering the architecture. This paper introduces a novel perspective by leveraging diverse knowledge sources within a unified KD framework.
  arXiv  Detail & Related papers  (2024-09-27T09:09:45Z)
• Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning [7.086262532457526]
  We present a novel method which generates context-dependent definitions of concept mentions by retrieving full-text literature. We further generate relational definitions, which describe how two concept mentions are related or different, and design an efficient re-ranking approach to address the explosion involved in inferring links across papers.
  arXiv  Detail & Related papers  (2024-09-23T15:20:27Z)
• Finding Pragmatic Differences Between Disciplines [14.587150614245123]
  We learn a fixed set of domain-agnostic descriptors for document sections and "retrofit" the corpus to these descriptors. We analyze the position and ordering of these descriptors across documents to understand the relationship between discipline and structure. Our findings lay the foundation for future work in assessing research quality, domain style transfer, and further pragmatic analysis.
  arXiv  Detail & Related papers  (2023-09-30T00:46:14Z)
• Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection [82.94413676131545]
  We propose a novel knowledge-enhanced hierarchical information correlation learning approach (KhiCL) for multi-modal rumor detection. KhiCL exploits cross-modal joint dictionary to transfer the heterogeneous unimodality features into the common feature space. It extracts visual and textual entities from images and text, and designs a knowledge relevance reasoning strategy.
  arXiv  Detail & Related papers  (2023-06-28T06:08:20Z)
• Dual Semantic Knowledge Composed Multimodal Dialog Systems [114.52730430047589]
  We propose a novel multimodal task-oriented dialog system named MDS-S2. It acquires the context related attribute and relation knowledge from the knowledge base. We also devise a set of latent query variables to distill the semantic information from the composed response representation.
  arXiv  Detail & Related papers  (2023-05-17T06:33:26Z)
• Variational Cross-Graph Reasoning and Adaptive Structured Semantics Learning for Compositional Temporal Grounding [143.5927158318524]
  Temporal grounding is the task of locating a specific segment from an untrimmed video according to a query sentence. We introduce a new Compositional Temporal Grounding task and construct two new dataset splits. We argue that the inherent structured semantics inside the videos and language is the crucial factor to achieve compositional generalization.
  arXiv  Detail & Related papers  (2023-01-22T08:02:23Z)
• Topic Scaling: A Joint Document Scaling -- Topic Model Approach To Learn Time-Specific Topics [0.0]
  This paper proposes a new methodology to study sequential corpora by implementing a two-stage algorithm that learns time-based topics with respect to a scale of document positions. The first stage ranks documents using Wordfish to estimate document positions that serve as a dependent variable to learn relevant topics. The second stage ranks the inferred topics on the document scale to match their occurrences within the corpus and track their evolution.
  arXiv  Detail & Related papers  (2021-03-31T12:35:36Z)
• Concept Learners for Few-Shot Learning [76.08585517480807]
  We propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation.
  arXiv  Detail & Related papers  (2020-07-14T22:04:17Z)
• Learnability with Indirect Supervision Signals [74.39088325025378]
  Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly. We develop a unified theoretical framework for multi-class classification when the supervision is provided by a variable that contains nonzero mutual information with the gold label. Our framework relaxes assumptions made in the literature, and supports learning with unknown, non-invertible and instance-dependent transitions.
  arXiv  Detail & Related papers  (2020-06-15T21:57:11Z)
• End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data [99.00520068425759]
  We propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. We show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy.
  arXiv  Detail & Related papers  (2020-02-03T17:46:13Z)

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.