44,682 research outputs found
The 'what' and 'how' of learning in design, invited paper
Previous experiences hold a wealth of knowledge which we often take for granted and use unknowingly through our every day working lives. In design, those experiences can play a crucial role in the success or failure of a design project, having a great deal of influence on the quality, cost and development time of a product. But how can we empower computer based design systems to acquire this knowledge? How would we use such systems to support design? This paper outlines some of the work which has been carried out in applying and developing Machine Learning techniques to support the design activity; particularly in utilising previous designs and learning the design process
The SP theory of intelligence: benefits and applications
This article describes existing and expected benefits of the "SP theory of
intelligence", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the "SP machine" -- an expression
of the SP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The SP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is at least $190 billion each year. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the SP machine, available to researchers everywhere.Comment: arXiv admin note: substantial text overlap with arXiv:1212.022
User experiments with the Eurovision cross-language image retrieval system
In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval.
The system is evaluated by multilingual users for two search tasks with the system configured in
English and five other languages. To our knowledge this is the first published set of user
experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual
search engine using little knowledge of any language other than English, (2) categorizing images
assists the user's search, and (3) there are differences in the way users search between the proposed
search tasks. Based on the two search tasks and user feedback, we describe important aspects of
any CL image retrieval system
An experiment with ontology mapping using concept similarity
This paper describes a system for automatically mapping between concepts in different ontologies. The motivation for the research stems from the Diogene project, in which the project's own ontology covering the ICT domain is mapped to external ontologies, in order that their associated content can automatically be included in the Diogene system. An approach involving measuring the similarity of concepts is introduced, in which standard Information Retrieval indexing techniques are applied to concept descriptions. A matrix representing the similarity of concepts in two ontologies is generated, and a mapping is performed based on two parameters: the domain coverage of the ontologies, and their levels of granularity. Finally, some initial experimentation is presented which suggests that our approach meets the project's unique set of requirements
Extracting tag hierarchies
Tagging items with descriptive annotations or keywords is a very natural way
to compress and highlight information about the properties of the given entity.
Over the years several methods have been proposed for extracting a hierarchy
between the tags for systems with a "flat", egalitarian organization of the
tags, which is very common when the tags correspond to free words given by
numerous independent people. Here we present a complete framework for automated
tag hierarchy extraction based on tag occurrence statistics. Along with
proposing new algorithms, we are also introducing different quality measures
enabling the detailed comparison of competing approaches from different
aspects. Furthermore, we set up a synthetic, computer generated benchmark
providing a versatile tool for testing, with a couple of tunable parameters
capable of generating a wide range of test beds. Beside the computer generated
input we also use real data in our studies, including a biological example with
a pre-defined hierarchy between the tags. The encouraging similarity between
the pre-defined and reconstructed hierarchy, as well as the seemingly
meaningful hierarchies obtained for other real systems indicate that tag
hierarchy extraction is a very promising direction for further research with a
great potential for practical applications.Comment: 25 pages with 21 pages of supporting information, 25 figure
Enriching very large ontologies using the WWW
This paper explores the possibility to exploit text on the world wide web in
order to enrich the concepts in existing ontologies. First, a method to
retrieve documents from the WWW related to a concept is described. These
document collections are used 1) to construct topic signatures (lists of
topically related words) for each concept in WordNet, and 2) to build
hierarchical clusters of the concepts (the word senses) that lexicalize a given
word. The overall goal is to overcome two shortcomings of WordNet: the lack of
topical links among concepts, and the proliferation of senses. Topic signatures
are validated on a word sense disambiguation task with good results, which are
improved when the hierarchical clusters are used.Comment: 6 page
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