4,932 research outputs found
NOUS: Construction and Querying of Dynamic Knowledge Graphs
The ability to construct domain specific knowledge graphs (KG) and perform
question-answering or hypothesis generation is a transformative capability.
Despite their value, automated construction of knowledge graphs remains an
expensive technical challenge that is beyond the reach for most enterprises and
academic institutions. We propose an end-to-end framework for developing custom
knowledge graph driven analytics for arbitrary application domains. The
uniqueness of our system lies A) in its combination of curated KGs along with
knowledge extracted from unstructured text, B) support for advanced trending
and explanatory questions on a dynamic KG, and C) the ability to answer queries
where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU
The WHY in Business Processes: Discovery of Causal Execution Dependencies
A crucial element in predicting the outcomes of process interventions and
making informed decisions about the process is unraveling the genuine
relationships between the execution of process activities. Contemporary process
discovery algorithms exploit time precedence as their main source of model
derivation. Such reliance can sometimes be deceiving from a causal perspective.
This calls for faithful new techniques to discover the true execution
dependencies among the tasks in the process. To this end, our work offers a
systematic approach to the unveiling of the true causal business process by
leveraging an existing causal discovery algorithm over activity timing. In
addition, this work delves into a set of conditions under which process mining
discovery algorithms generate a model that is incongruent with the causal
business process model, and shows how the latter model can be methodologically
employed for a sound analysis of the process. Our methodology searches for such
discrepancies between the two models in the context of three causal patterns,
and derives a new view in which these inconsistencies are annotated over the
mined process model. We demonstrate our methodology employing two open process
mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery
technique. We apply it on a synthesized dataset and on two open benchmark data
sets.Comment: 20 pages, 19 figure
Accelerating Innovation Through Analogy Mining
The availability of large idea repositories (e.g., the U.S. patent database)
could significantly accelerate innovation and discovery by providing people
with inspiration from solutions to analogous problems. However, finding useful
analogies in these large, messy, real-world repositories remains a persistent
challenge for either human or automated methods. Previous approaches include
costly hand-created databases that have high relational structure (e.g.,
predicate calculus representations) but are very sparse. Simpler
machine-learning/information-retrieval similarity metrics can scale to large,
natural-language datasets, but struggle to account for structural similarity,
which is central to analogy. In this paper we explore the viability and value
of learning simpler structural representations, specifically, "problem
schemas", which specify the purpose of a product and the mechanisms by which it
achieves that purpose. Our approach combines crowdsourcing and recurrent neural
networks to extract purpose and mechanism vector representations from product
descriptions. We demonstrate that these learned vectors allow us to find
analogies with higher precision and recall than traditional
information-retrieval methods. In an ideation experiment, analogies retrieved
by our models significantly increased people's likelihood of generating
creative ideas compared to analogies retrieved by traditional methods. Our
results suggest a promising approach to enabling computational analogy at scale
is to learn and leverage weaker structural representations.Comment: KDD 201
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