11,847 research outputs found
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process
Automatic detection of accommodation steps as an indicator of knowledge maturing
Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed
prototypical implementations
In this technical report, we present prototypical implementations of
innovative tools and methods developed according to the working plan outlined
in Technical Report TR-B-09-05 [23]. We present an ontology modularization and
integration framework and the SVoNt server, the server-side end of an SVN-
based versioning system for ontologies in the Corporate Ontology Engineering
pillar. For the Corporate Semantic Collaboration pillar, we present the
prototypical implementation of a light-weight ontology editor for non-experts
and an ontology based expert finder system. For the Corporate Semantic Search
pillar, we present a prototype for algorithmic extraction of relations in
folksonomies, a tool for trend detection using a semantic analyzer, a tool for
automatic classification of web documents using Hidden Markov models, a
personalized semantic recommender for multimedia content, and a semantic
search assistant developed in co-operation with the Museumsportal Berlin. The
prototypes complete the next milestone on the path to an integral Cor- porate
Semantic Web architecture based on the three pillars Corporate Ontol- ogy
Engineering, Corporate Semantic Collaboration, and Corporate Semantic Search,
as envisioned in [23]
Entities as topic labels : combining entity linking and labeled LDA to improve topic interpretability and evaluability
Digital humanities scholars strongly need a corpus exploration method that provides topics easier
to interpret than standard LDA topic models. To move towards this goal, here we propose a
combination of two techniques, called Entity Linking and Labeled LDA. Our method identifies
in an ontology a series of descriptive labels for each document in a corpus. Then it generates a specific topic for each label. Having a direct relation between topics and labels makes interpretation
easier; using an ontology as background knowledge limits label ambiguity. As our topics are described with a limited number of clear-cut labels, they promote interpretability and support
the quantitative evaluation of the obtained results. We illustrate the potential of the approach by
applying it to three datasets, namely the transcription of speeches from the European Parliament
fifth mandate, the Enron Corpus and the Hillary Clinton Email Dataset. While some of these
resources have already been adopted by the natural language processing community, they still
hold a large potential for humanities scholars, part of which could be exploited in studies that
will adopt the fine-grained exploration method presented in this paper
On relational learning and discovery in social networks: a survey
The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements
Semantic Knowledge Graphs for the News: A Review
ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.publishedVersio
- …