81,715 research outputs found
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
Qualitative Effects of Knowledge Rules in Probabilistic Data Integration
One of the problems in data integration is data overlap: the fact that different data sources have data on the same real world entities. Much development time in data integration projects is devoted to entity resolution. Often advanced similarity measurement techniques are used to remove semantic duplicates from the integration result or solve other semantic conflicts, but it proofs impossible to get rid of all semantic problems in data integration. An often-used rule of thumb states that about 90% of the development effort is devoted to solving the remaining 10% hard cases. In an attempt to significantly decrease human effort at data integration time, we have proposed an approach that stores any remaining semantic uncertainty and conflicts in a probabilistic database enabling it to already be meaningfully used. The main development effort in our approach is devoted to defining and tuning knowledge rules and thresholds. Rules and thresholds directly impact the size and quality of the integration result. We measure integration quality indirectly by measuring the quality of answers to queries on the integrated data set in an information retrieval-like way. The main contribution of this report is an experimental investigation of the effects and sensitivity of rule definition and threshold tuning on the integration quality. This proves that our approach indeed reduces development effort — and not merely shifts the effort to rule definition and threshold tuning — by showing that setting rough safe thresholds and defining only a few rules suffices to produce a ‘good enough’ integration that can be meaningfully used
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
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