33,255 research outputs found
The role of linked data and the semantic web in building operation
Effective Decision Support Systems (DSS) for building service managers require adequate performance data from many building data silos in order to deliver a complete view of building performance. Current performance analysis techniques tend to focus on a limited number of data sources, such as BMS measured data (temperature, humidity, C02), excluding a wealth of other data sources increasingly available in the modern building, including weather data, occupant feedback, mobile sensors & feedback systems, schedule information, equipment usage information. This paper investigates the potential for using Linked Data and Semantic Web technologies to improve interoperability across AEC domains, overcoming many of the roadblocks hindering information transfer currently
Automatic Concept Discovery from Parallel Text and Visual Corpora
Humans connect language and vision to perceive the world. How to build a
similar connection for computers? One possible way is via visual concepts,
which are text terms that relate to visually discriminative entities. We
propose an automatic visual concept discovery algorithm using parallel text and
visual corpora; it filters text terms based on the visual discriminative power
of the associated images, and groups them into concepts using visual and
semantic similarities. We illustrate the applications of the discovered
concepts using bidirectional image and sentence retrieval task and image
tagging task, and show that the discovered concepts not only outperform several
large sets of manually selected concepts significantly, but also achieves the
state-of-the-art performance in the retrieval task.Comment: To appear in ICCV 201
Distributed Holistic Clustering on Linked Data
Link discovery is an active field of research to support data integration in
the Web of Data. Due to the huge size and number of available data sources,
efficient and effective link discovery is a very challenging task. Common
pairwise link discovery approaches do not scale to many sources with very large
entity sets. We here propose a distributed holistic approach to link many data
sources based on a clustering of entities that represent the same real-world
object. Our clustering approach provides a compact and fused representation of
entities, and can identify errors in existing links as well as many new links.
We support a distributed execution of the clustering approach to achieve faster
execution times and scalability for large real-world data sets. We provide a
novel gold standard for multi-source clustering, and evaluate our methods with
respect to effectiveness and efficiency for large data sets from the geographic
and music domains
Predicting the Quality of Short Narratives from Social Media
An important and difficult challenge in building computational models for
narratives is the automatic evaluation of narrative quality. Quality evaluation
connects narrative understanding and generation as generation systems need to
evaluate their own products. To circumvent difficulties in acquiring
annotations, we employ upvotes in social media as an approximate measure for
story quality. We collected 54,484 answers from a crowd-powered
question-and-answer website, Quora, and then used active learning to build a
classifier that labeled 28,320 answers as stories. To predict the number of
upvotes without the use of social network features, we create neural networks
that model textual regions and the interdependence among regions, which serve
as strong benchmarks for future research. To our best knowledge, this is the
first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc
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