72,476 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
Passively mode-locked laser using an entirely centred erbium-doped fiber
This paper describes the setup and experimental results for an entirely centred erbium-doped fiber laser with passively mode-locked output. The gain medium of the ring laser cavity configuration comprises a 3 m length of two-core optical fiber, wherein an undoped outer core region of 9.38 μm diameter surrounds a 4.00 μm diameter central core region doped with erbium ions at 400 ppm concentration. The generated stable soliton mode-locking output has a central wavelength of 1533 nm and pulses that yield an average output power of 0.33 mW with a pulse energy of 31.8 pJ. The pulse duration is 0.7 ps and the measured output repetition rate of 10.37 MHz corresponds to a 96.4 ns pulse spacing in the pulse train
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines
Artificial Neural Network Pruning to Extract Knowledge
Artificial Neural Networks (NN) are widely used for solving complex problems
from medical diagnostics to face recognition. Despite notable successes, the
main disadvantages of NN are also well known: the risk of overfitting, lack of
explainability (inability to extract algorithms from trained NN), and high
consumption of computing resources. Determining the appropriate specific NN
structure for each problem can help overcome these difficulties: Too poor NN
cannot be successfully trained, but too rich NN gives unexplainable results and
may have a high chance of overfitting. Reducing precision of NN parameters
simplifies the implementation of these NN, saves computing resources, and makes
the NN skills more transparent. This paper lists the basic NN simplification
problems and controlled pruning procedures to solve these problems. All the
described pruning procedures can be implemented in one framework. The developed
procedures, in particular, find the optimal structure of NN for each task,
measure the influence of each input signal and NN parameter, and provide a
detailed verbal description of the algorithms and skills of NN. The described
methods are illustrated by a simple example: the generation of explicit
algorithms for predicting the results of the US presidential election.Comment: IJCNN 202
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