18,525 research outputs found
An Answer Explanation Model for Probabilistic Database Queries
Following the availability of huge amounts of uncertain data, coming from diverse ranges of applications such as sensors, machine learning or mining approaches, information extraction and integration, etc. in recent years, we have seen a revival of interests in probabilistic databases. Queries over these databases result in probabilistic answers. As the process of arriving at these answers is based on the underlying stored uncertain data, we argue that from the standpoint of an end user, it is helpful for such a system to give an explanation on how it arrives at an answer and on which uncertainty assumptions the derived answer is based. In this way, the user with his/her own knowledge can decide how much confidence to place in this probabilistic answer. \ud
The aim of this paper is to design such an answer explanation model for probabilistic database queries. We report our design principles and show the methods to compute the answer explanations. One of the main contributions of our model is that it fills the gap between giving only the answer probability, and giving the full derivation. Furthermore, we show how to balance verifiability and influence of explanation components through the concept of verifiable views. The behavior of the model and its computational efficiency are demonstrated through an extensive performance study
Fast and Simple Relational Processing of Uncertain Data
This paper introduces U-relations, a succinct and purely relational
representation system for uncertain databases. U-relations support
attribute-level uncertainty using vertical partitioning. If we consider
positive relational algebra extended by an operation for computing possible
answers, a query on the logical level can be translated into, and evaluated as,
a single relational algebra query on the U-relation representation. The
translation scheme essentially preserves the size of the query in terms of
number of operations and, in particular, number of joins. Standard techniques
employed in off-the-shelf relational database management systems are effective
for optimizing and processing queries on U-relations. In our experiments we
show that query evaluation on U-relations scales to large amounts of data with
high degrees of uncertainty.Comment: 12 pages, 14 figure
08421 Abstracts Collection -- Uncertainty Management in Information Systems
From October 12 to 17, 2008 the Dagstuhl Seminar 08421 \u27`Uncertainty Management in Information Systems \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. The abstracts of the plenary and session talks given during the seminar as well as those of the shown demos are put together in this paper
Student questioning : a componential analysis
This article reviews the literature on student questioning, organized through a modified version of Dillon's (1988a, 1990) componential model of questioning. Special attention is given to the properties of assumptions, questions, and answers. Each of these main elements are the result of certain actions of the questioner, which are described. Within this framework a variety of aspects of questioning are highlighted. One focus of the article is individual differences in question asking. The complex interactions between students' personal characteristics, social factors, and questioning are examined. In addition, a number of important but neglected topics for research are identified. Together, the views that are presented should deepen our understanding of student questioning
Construction contract risk identification based on knowledge-augmented language model
Contract review is an essential step in construction projects to prevent
potential losses. However, the current methods for reviewing construction
contracts lack effectiveness and reliability, leading to time-consuming and
error-prone processes. While large language models (LLMs) have shown promise in
revolutionizing natural language processing (NLP) tasks, they struggle with
domain-specific knowledge and addressing specialized issues. This paper
presents a novel approach that leverages LLMs with construction contract
knowledge to emulate the process of contract review by human experts. Our
tuning-free approach incorporates construction contract domain knowledge to
enhance language models for identifying construction contract risks. The use of
a natural language when building the domain knowledge base facilitates
practical implementation. We evaluated our method on real construction
contracts and achieved solid performance. Additionally, we investigated how
large language models employ logical thinking during the task and provide
insights and recommendations for future research
Reason Maintenance - Conceptual Framework
This paper describes the conceptual framework for reason maintenance developed as part of
WP2
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