89,895 research outputs found
Utilizing Data and Knowledge Mining for Probabilistic Knowledge Bases
Problems can arise whenever inferencing is attempted on a knowledge base that is incomplete. Our work shows that data mining techniques can be applied to fill in incomplete areas in Bayesian Knowledge Bases (BKBs), as well as in other knowledge-based systems utilizing probabilistic representations. The problem of inconsistency in BKBs has been addressed in previous work, where reinforcement learning techniques from neural networks were applied. However, the issue of automatically solving incompleteness in BKBs has yet to be addressed. Presently, incompleteness in BKBs is repaired through the application of traditional knowledge acquisition techniques. We show how association rules can be extracted from databases in order to replace excluded information and express missing relationships. A methodology for incorporating those results while maintaining a consistent knowledge base is also included
Structurally Tractable Uncertain Data
Many data management applications must deal with data which is uncertain,
incomplete, or noisy. However, on existing uncertain data representations, we
cannot tractably perform the important query evaluation tasks of determining
query possibility, certainty, or probability: these problems are hard on
arbitrary uncertain input instances. We thus ask whether we could restrict the
structure of uncertain data so as to guarantee the tractability of exact query
evaluation. We present our tractability results for tree and tree-like
uncertain data, and a vision for probabilistic rule reasoning. We also study
uncertainty about order, proposing a suitable representation, and study
uncertain data conditioned by additional observations.Comment: 11 pages, 1 figure, 1 table. To appear in SIGMOD/PODS PhD Symposium
201
Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language
interfaces to databases for new domains, whose performance improves over time
based on user feedback, and requires minimal intervention. To achieve this, we
adapt neural sequence models to map utterances directly to SQL with its full
expressivity, bypassing any intermediate meaning representations. These models
are immediately deployed online to solicit feedback from real users to flag
incorrect queries. Finally, the popularity of SQL facilitates gathering
annotations for incorrect predictions using the crowd, which is directly used
to improve our models. This complete feedback loop, without intermediate
representations or database specific engineering, opens up new ways of building
high quality semantic parsers. Experiments suggest that this approach can be
deployed quickly for any new target domain, as we show by learning a semantic
parser for an online academic database from scratch.Comment: Accepted at ACL 201
Web and Semantic Web Query Languages
A number of techniques have been developed to facilitate
powerful data retrieval on the Web and Semantic Web. Three categories
of Web query languages can be distinguished, according to the format
of the data they can retrieve: XML, RDF and Topic Maps. This article
introduces the spectrum of languages falling into these categories
and summarises their salient aspects. The languages are introduced using
common sample data and query types. Key aspects of the query
languages considered are stressed in a conclusion
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