SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular Data

• URL: http://arxiv.org/abs/2203.09521v1
• Date: Thu, 17 Mar 2022 17:14:21 GMT
• Title: SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular Data
• Authors: Marco Ripamonti, Flavio De Paoli, Matteo Palmonari (University of Milan-Bicocca)
• Abstract summary: SemTUI is a framework to make the enrichment process flexible, complete, and effective through the use of semantics. A task-driven user evaluation proved SemTUI to be understandable, usable, and capable of achieving table enrichment with little effort and time.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large availability of datasets fosters the use of \acrshort{ml} and \acrshort{ai} technologies to gather insights, study trends, and predict unseen behaviours out of the world of data. Today, gathering and integrating data from different sources is mainly a manual activity that requires the knowledge of expert users at an high cost in terms of both time and money. It is, therefore, necessary to make the process of gathering and linking data from many different sources affordable to make datasets ready to perform the desired analysis. In this work, we propose the development of a comprehensive framework, named SemTUI, to make the enrichment process flexible, complete, and effective through the use of semantics. The approach is to promote fast integration of external services to perform enrichment tasks such as reconciliation and extension; and to provide users with a graphical interface to support additional tasks, such as refinement to correct ambiguous results provided by automatic enrichment algorithms. A task-driven user evaluation proved SemTUI to be understandable, usable, and capable of achieving table enrichment with little effort and time with user tests that involved people with different skills and experiences.

Related papers

• Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs [0.061446808540639365]
  This work explores the usage of Knowledge Graphs (KG) as a basic framework for capturing a human-centered manner complex analytics. The data stored in the generated KG can then be exploited to provide assistance (e.g., recommendations) to the users interacting with these systems.
  arXiv  Detail & Related papers  (2024-11-01T20:45:23Z)
• AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data [14.328402787379538]
  We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines.
  arXiv  Detail & Related papers  (2024-10-15T12:05:58Z)
• Automatic Identification and Visualization of Group Training Activities Using Wearable Data [7.130450173185638]
  Human Activity Recognition (HAR) identifies daily activities from time-series data collected by wearable devices like smartwatches. This paper presents a comprehensive framework for imputing, analyzing, and identifying activities from wearable data. Our approach is based on data collected from 135 soldiers wearing Garmin 55 smartwatches over six months.
  arXiv  Detail & Related papers  (2024-10-07T19:35:15Z)
• AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning [93.96463520716759]
  Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and hallucinations. Here, we introduce AvaTaR, a novel and automated framework that optimize an LLM agent to effectively leverage provided tools, improving performance on a given task.
  arXiv  Detail & Related papers  (2024-06-17T04:20:02Z)
• Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement [79.2400720115588]
  We introduce Persona-DB, a simple yet effective framework consisting of a hierarchical construction process to improve generalization across task contexts. In the evaluation of response prediction, Persona-DB demonstrates superior context efficiency in maintaining accuracy with a significantly reduced retrieval size. Our experiments also indicate a marked improvement of over 10% under cold-start scenarios, when users have extremely sparse data.
  arXiv  Detail & Related papers  (2024-02-16T20:20:43Z)
• AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets [56.052803235932686]
  We propose a novel automatic dataset synthesis approach that can generate both large-scale and high-quality recommendation dialogues. In doing so, we exploit: (i) rich personalized user profiles from traditional recommendation datasets, (ii) rich external knowledge from knowledge graphs, and (iii) the conversation ability contained in human-to-human conversational recommendation datasets.
  arXiv  Detail & Related papers  (2023-06-16T05:27:14Z)
• Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming [77.38174112525168]
  We present Nemo, an end-to-end interactive Supervision system that improves overall productivity of WS learning pipeline by an average 20% (and up to 47% in one task) compared to the prevailing WS supervision approach.
  arXiv  Detail & Related papers  (2022-03-02T19:57:32Z)
• Single-Modal Entropy based Active Learning for Visual Question Answering [75.1682163844354]
  We address Active Learning in the multi-modal setting of Visual Question Answering (VQA) In light of the multi-modal inputs, image and question, we propose a novel method for effective sample acquisition. Our novel idea is simple to implement, cost-efficient, and readily adaptable to other multi-modal tasks.
  arXiv  Detail & Related papers  (2021-10-21T05:38:45Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

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.