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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Automated Generation of Cross-Domain Analogies via Evolutionary Computation
Analogy plays an important role in creativity, and is extensively used in
science as well as art. In this paper we introduce a technique for the
automated generation of cross-domain analogies based on a novel evolutionary
algorithm (EA). Unlike existing work in computational analogy-making restricted
to creating analogies between two given cases, our approach, for a given case,
is capable of creating an analogy along with the novel analogous case itself.
Our algorithm is based on the concept of "memes", which are units of culture,
or knowledge, undergoing variation and selection under a fitness measure, and
represents evolving pieces of knowledge as semantic networks. Using a fitness
function based on Gentner's structure mapping theory of analogies, we
demonstrate the feasibility of spontaneously generating semantic networks that
are analogous to a given base network.Comment: Conference submission, International Conference on Computational
Creativity 2012 (8 pages, 6 figures
An Interpretable Knowledge Transfer Model for Knowledge Base Completion
Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information.Comment: Accepted by ACL 2017. Minor updat
What is Computational Intelligence and where is it going?
What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Text Summarization Techniques: A Brief Survey
In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.Comment: Some of references format have update
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