251 research outputs found
Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.Comment: 20 pages, 4 figures, 5 tables. Submitted to IEEE TKD
Constructing a Personal Knowledge Graph from Disparate Data Sources
This thesis revolves around the idea of a Personal Knowledge Graph as a uniform coherent structure of personal data collected from multiple disparate sources: A knowledge base consisting of entities such as persons, events, locations and companies interlinked with semantically meaningful relationships in a graph structure where the user is at its center. The personal knowledge graph is intended to be a valuable resource for a digital personal assistant, expanding its capabilities to answer questions and perform tasks that require personal knowledge about the user.
We explored techniques within Knowledge Representation, Knowledge Extraction/ Information Extraction and Information Management for the purpose of constructing such a graph. We show the practical advantages of using Knowledge Graphs for personal information management, utilizing the structure for extracting and inferring answers and for handling resources like documents, emails and calendar entries.
We have proposed a framework for aggregating user data and shown how existing ontologies can be used to model personal knowledge.
We have shown that a personal knowledge graph based on the user's personal resources is a viable concept, however we were not able to enrich our personal knowledge graph with knowledge extracted from unstructured private sources. This was mainly due to sparsity of relevant information, the informal nature and the lack of context in personal correspondence
A Survey on Interpretable Cross-modal Reasoning
In recent years, cross-modal reasoning (CMR), the process of understanding
and reasoning across different modalities, has emerged as a pivotal area with
applications spanning from multimedia analysis to healthcare diagnostics. As
the deployment of AI systems becomes more ubiquitous, the demand for
transparency and comprehensibility in these systems' decision-making processes
has intensified. This survey delves into the realm of interpretable cross-modal
reasoning (I-CMR), where the objective is not only to achieve high predictive
performance but also to provide human-understandable explanations for the
results. This survey presents a comprehensive overview of the typical methods
with a three-level taxonomy for I-CMR. Furthermore, this survey reviews the
existing CMR datasets with annotations for explanations. Finally, this survey
summarizes the challenges for I-CMR and discusses potential future directions.
In conclusion, this survey aims to catalyze the progress of this emerging
research area by providing researchers with a panoramic and comprehensive
perspective, illuminating the state of the art and discerning the
opportunities
Bridging Low-level Geometry to High-level Concepts in Visual Servoing of Robot Manipulation Task Using Event Knowledge Graphs and Vision-Language Models
In this paper, we propose a framework of building knowledgeable robot control
in the scope of smart human-robot interaction, by empowering a basic
uncalibrated visual servoing controller with contextual knowledge through the
joint usage of event knowledge graphs (EKGs) and large-scale pretrained
vision-language models (VLMs). The framework is expanded in twofold: first, we
interpret low-level image geometry as high-level concepts, allowing us to
prompt VLMs and to select geometric features of points and lines for motor
control skills; then, we create an event knowledge graph (EKG) to conceptualize
a robot manipulation task of interest, where the main body of the EKG is
characterized by an executable behavior tree, and the leaves by semantic
concepts relevant to the manipulation context. We demonstrate, in an
uncalibrated environment with real robot trials, that our method lowers the
reliance of human annotation during task interfacing, allows the robot to
perform activities of daily living more easily by treating low-level
geometric-based motor control skills as high-level concepts, and is beneficial
in building cognitive thinking for smart robot applications
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