转载自：https://analytixon.com/2018/12/14/document-worth-reading-ai-reasoning-systems-pac-and-applied-methods/

Michael Laux
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Learning and logic are distinct and remarkable approaches to prediction. Machine learning has experienced a surge in popularity because it is robust to noise and achieves high performance; however, ML experiences many issues with knowledge transfer and extrapolation. In contrast, logic is easily intepreted, and logical rules are easy to chain and transfer between systems; however, inductive logic is brittle to noise. We then explore the premise of combining learning with inductive logic into AI Reasoning Systems. Specifically, we summarize findings from PAC learning (conceptual graphs, robust logics, knowledge infusion) and deep learning (DSRL, $\partial$ILP, DeepLogic) by reproducing proofs of tractability, presenting algorithms in pseudocode, highlighting results, and synthesizing between fields. We conclude with suggestions for integrated models by combining the modules listed above and with a list of unsolved (likely intractable) problems. AI Reasoning Systems: PAC and Applied Methods

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转载自：https://analytixon.com/2018/12/13/if-you-did-not-already-know-575/

Michael Laux
发表于

Agnostic Disambiguation of Named Entities Using Linked Open Data (AGDISTIS) AGDISTIS is an Open Source Named Entity Disambiguation Framework able to link entities against every Linked Data Knowledge Base. The ongoing transition from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework). One of the key steps towards extracting RDF from natural-language corpora is the disambiguation of named entities. AGDISTIS combines the HITS algorithm with label expansion strategies and string similarity measures. Based on this combination, it can efficiently detect the correct URIs for a given set of named entities within an input text. Furthermore, AGDISTIS is agnostic of the underlying knowledge base. AGDISTIS has been evaluated on different datasets against state-of-the-art named entity disambiguation frameworks. http://…/public.pdf …

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