15,075 research outputs found
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Knowledge-based Transfer Learning Explanation
Machine learning explanation can significantly boost machine learning's
application in decision making, but the usability of current methods is limited
in human-centric explanation, especially for transfer learning, an important
machine learning branch that aims at utilizing knowledge from one learning
domain (i.e., a pair of dataset and prediction task) to enhance prediction
model training in another learning domain. In this paper, we propose an
ontology-based approach for human-centric explanation of transfer learning.
Three kinds of knowledge-based explanatory evidence, with different
granularities, including general factors, particular narrators and core
contexts are first proposed and then inferred with both local ontologies and
external knowledge bases. The evaluation with US flight data and DBpedia has
presented their confidence and availability in explaining the transferability
of feature representation in flight departure delay forecasting.Comment: Accepted by International Conference on Principles of Knowledge
Representation and Reasoning, 201
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
The unification of statistical (data-driven) and symbolic (knowledge-driven)
methods is widely recognised as one of the key challenges of modern AI. Recent
years have seen large number of publications on such hybrid neuro-symbolic AI
systems. That rapidly growing literature is highly diverse and mostly
empirical, and is lacking a unifying view of the large variety of these hybrid
systems. In this paper we analyse a large body of recent literature and we
propose a set of modular design patterns for such hybrid, neuro-symbolic
systems. We are able to describe the architecture of a very large number of
hybrid systems by composing only a small set of elementary patterns as building
blocks.
The main contributions of this paper are: 1) a taxonomically organised
vocabulary to describe both processes and data structures used in hybrid
systems; 2) a set of 15+ design patterns for hybrid AI systems, organised in a
set of elementary patterns and a set of compositional patterns; 3) an
application of these design patterns in two realistic use-cases for hybrid AI
systems. Our patterns reveal similarities between systems that were not
recognised until now. Finally, our design patterns extend and refine Kautz'
earlier attempt at categorising neuro-symbolic architectures.Comment: 20 pages, 22 figures, accepted for publication in the International
Journal of Applied Intelligenc
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
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