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result(s) for
"Conceptual structures (Information theory)"
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An introduction to description logic
Description logics (DLs) have a long tradition in computer science and knowledge representation, being designed so that domain knowledge can be described and so that computers can reason about this knowledge. DLs have recently gained increased importance since they form the logical basis of widely used ontology languages, in particular the web ontology language OWL. Written by four renowned experts, this is the first textbook on description logics. It is suitable for self-study by graduates and as the basis for a university course. Starting from a basic DL, the book introduces the reader to their syntax, semantics, reasoning problems and model theory and discusses the computational complexity of these reasoning problems and algorithms to solve them. It then explores a variety of reasoning techniques, knowledge-based applications and tools and it describes the relationship between DLs and OWL.-- Source other than Library of Congress.
Book
A conceptual DFT and information-theoretic approach towards QSPR modeling in polychlorobiphenyls
Quantitative structure–property relationship (QSPR) of various chlorine substituted biphenyl systems on the basis of linear and multi-linear regression (MLR) analysis is presented in this study. The determination of lipophilicity (log
K
OW
) of the selected 133 polychlorobiphenyl (PCB) congeners is carried out taking the experimental log
K
OW
as the dependent variable and the conceptual density functional theory (CDFT) and information theory (IT) based descriptors (global electrophilicity index (
ω
), its square term (
ω
2
), and Shannon entropy (
S
S
), GBP entropy (
S
GBP
)) as independent variables. These are used to map the relationship between experimental log
K
OW
and predicted log
K
OW
. The best model is obtained using CDFT descriptor (
ω
) along with IT quantities (
S
S
,
S
GBP
) when combined linearly. The results show a very good coefficient of determination value (
R
2
= 0.9261) along with a high internal predicting ability (
R
2
CV
= 0.9208) which indicates the importance of the mentioned descriptors for the quantitative structure–property analysis of selected PCBs.
Journal Article
Handbook of Learning from Multiple Representations and Perspectives
In and out of formal schooling, online and off, today’s learners must consume and integrate a level of information that is exponentially larger and delivered through a wider range of formats and viewpoints than ever before. The Handbook of Learning from Multiple Representations and Perspectives provides a path for understanding the cognitive, motivational, and socioemotional processes and skills necessary for learners across educational contexts to make sense of and use information sourced from varying inputs. Uniting research and theory from education, psychology, literacy, library sciences, media and technology, and more, this forward-thinking volume explores the common concerns, shared challenges, and thematic patterns in our capacity to make meaning in an information-rich society.
eBook
Linked Data for the Perplexed Librarian
Linked data has become a punchline in certain circles of the GLAM (galleries, libraries, archives, and museums) community, derided as a much-hyped project that will ultimately never come to fruition.
eBook
Towards a general user model to develop intelligent user interfaces
The way end-users interact with a system plays a crucial role in the high acceptance of software. Related to this, the concept of Intelligent User Interfaces has emerged as a solution to learn from user interactions with the system and adapt interfaces to the user’s characteristics and preferences. However, existing approaches to designing intelligent user interfaces are limited by their user models, which are not capable of representing each and every user characteristic valid for any context. This work aims to address this limitation by presenting a user model that can abstractly represent a wide set of user characteristics in any context of interaction. The model is based on a synthesis of previous works that have proposed specific user models. After the analysis of these works, a more sophisticated user model has been defined, including some required characteristics not existing in previous works. This model has been validated with 62 real end-users who have expressed the users’ characteristics that they consider as relevant to adapt the interaction. The results show that most of these characteristics can be represented by the proposed user model. This user model is the first step towards creating intelligent user interfaces that can adapt interactions to users with similar characteristics and preferences in similar contexts.
Journal Article
Why are information-theoretic descriptors powerful predictors of atomic and molecular polarizabilities
Context
We rationalize the excellent performance of information-theoretic descriptors for predicting atomic and molecular polarizabilities. It seems that descriptors which capture information about the change in valence-shell structure, especially the relative Fisher information measures, are particularly useful. Using this, we can rationalize why the
G
3 form of the relative Fisher information, which measures the deviation of effective nuclear charge between an atom-in-a-molecule and the reference pro-atom, is especially effective as a predictor of molecular polarizability.
Methods
There are no methods used in this paper, which relies on mathematical derivation and analysis.
Journal Article
From clicks to consequences: a multi-method review of online grocery shopping
The academic interest in Online Grocery Shopping (OGS) has proliferated in retailing and business management over the past two decades. Previous research on OGS was primarily focused on consumer-level consequences such as purchase intention, purchase decision, and post-purchase behavior. However, there is a lack of literature integrating intrinsic and extrinsic factors that influence the growth of OGS and its impact on purchase outcomes. To address this, we conduct a multi-method review combining traits of a systematic literature review and bibliometric analysis. Analyzing 145 articles through word cloud and keyword co-occurrence analysis, we identify publication trends (top journals, articles) and nine thematic clusters. We develop an integrated conceptual framework encompassing the antecedents, mediators, moderators, and consequences of OGS. Finally, we outline future research directions using Theory-Context-Characteristics-Methods framework to serve as a reference point for future researchers working in OGS.
Journal Article
HyperQuaternionE: A hyperbolic embedding model for qualitative spatial and temporal reasoning
Qualitative spatial/temporal reasoning (QSR/QTR) plays a key role in research on human cognition, e.g., as it relates to navigation, as well as in work on robotics and artificial intelligence. Although previous work has mainly focused on various spatial and temporal calculi, more recently representation learning techniques such as embedding have been applied to reasoning and inference tasks such as query answering and knowledge base completion. These subsymbolic and learnable representations are well suited for handling noise and efficiency problems that plagued prior work. However, applying embedding techniques to spatial and temporal reasoning has received little attention to date. In this paper, we explore two research questions: (1) How do embedding-based methods perform empirically compared to traditional reasoning methods on QSR/QTR problems? (2) If the embedding-based methods are better, what causes this superiority? In order to answer these questions, we first propose a hyperbolic embedding model, called HyperQuaternionE, to capture varying properties of relations (such as symmetry and anti-symmetry), to learn inversion relations and relation compositions (i.e., composition tables), and to model hierarchical structures over entities induced by transitive relations. We conduct various experiments on two synthetic datasets to demonstrate the advantages of our proposed embedding-based method against existing embedding models as well as traditional reasoners with respect to entity inference and relation inference. Additionally, our qualitative analysis reveals that our method is able to learn conceptual neighborhoods implicitly. We conclude that the success of our method is attributed to its ability to model composition tables and learn conceptual neighbors, which are among the core building blocks of QSR/QTR.
Journal Article