We present a new knowledge-base of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old's vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet. The knowledge base is available at https://allenai.org/data/haspartkb

Knowledge Graphs (KGs) extracted from text sources are often noisy and lead to poor performance in downstream application tasks such as KG-based question answering.While much of the recent activity is focused on addressing the sparsity of KGs by using embeddings for inferring new facts, the issue of cleaning up of noise in KGs through KG refinement task is not as actively studied. Most successful techniques for KG refinement make use of inference rules and reasoning over ontologies. Barring a few exceptions, embeddings do not make use of ontological information, and their performance in KG refinement task is not well understood. In this paper, we present a KG refinement framework called IterefinE which iteratively combines the two techniques - one which uses ontological information and inferences rules, PSL-KGI, and the KG embeddings such as ComplEx and ConvE which do not. As a result, IterefinE is able to exploit not only the ontological information to improve the quality of predictions, but also the power of KG embeddings which (implicitly) perform longer chains of reasoning. The IterefinE framework, operates in a co-training mode and results in explicit type-supervised embedding of the refined KG from PSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarks show that the embeddings that we produce are able to reject noisy facts from KG and at the same time infer higher quality new facts resulting in up to 9% improvement of overall weighted F1 score

The significant advances in autonomous systems together with an immensely wider application domain have increased the need for trustable intelligent systems. Explainable artificial intelligence is gaining considerable attention among researchers and developers to address this requirement. Although there is an increasing number of works on interpretable and transparent machine learning algorithms, they are mostly intended for the technical users. Explanations for the end-user have been neglected in many usable and practical applications. In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users. This method explains the prediction results without transforming the model into an interpretable one. We present an example of providing explanations for linear and non-linear models to demonstrate the generalizability of the method. CI and CU are numerical values that can be represented to the user in visuals and natural language form to justify actions and explain reasoning for individual instances, situations, and contexts. We show the utility of explanations in car selection example and Iris flower classification by presenting complete (i.e. the causes of an individual prediction) and contrastive explanation (i.e. contrasting instance against the instance of interest). The experimental results show the feasibility and validity of the provided explanation methods.

Managing rich content in a QnA Maker chatbot has always been a challenge, since the users had to edit raw markdown. Now QnA Maker enables your to add and edit rich content right in the portal, so what you see in the edit experience is what you see in the Bot response. Also introducing new access roles (Editor and Reader) which can be assigned to a QnA Maker service, to restrict allowed operations. The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show https://aka.ms/aishowsubscribe

The common thread across all AI technologies is the ability to impart human-like decision-making capabilities into applications and systems. Artificial intelligence (AI) refers to the simulation of human intelligence in systems programmed to think like humans and mimic their actions. AI includes a broad range of technologies, including cognitive computing, deep learning, expert systems, machine learning, natural language processing, and IBM Watson. The common thread across these areas, and all of AI, for that matter, is the ability to impart human-like decision-making capabilities into applications and systems. Experts predict AI will be rapidly adopted because they believe it will be a disruptive technology across many industries.

This section focuses on "Expert System" in Artificial Intelligence. These Multiple Choice Questions (mcq) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations. Explanation: Expert System introduced by the researchers at Stanford University, Computer Science Department. Explanation: Expanding is not Capabilities of Expert Systems. Explanation: The components of ES include: Knowledge Base, Inference Engine, User Interface.

ABSTRACT We describe an Object Oriented Model for building Expert Systems. This model and the detection of similarities allow to implement reasoning modes as induction, deduction and simulation. We specially focus on similarity and its use in induction. We propose original algorithms which deal with total and partial structural similitude of objects to facilitate knowledge acquisition. Keywords: Knowledge acquisition, object oriented model, structural similarity, induction. Colloc, J. & Boulanger, D. Automatic knowledge acquisition for object oriented expert systems AVIGNON'93, 13th International Conference Artificial Intelligence, Expert Systems, Natural Language, 1993, 99-108 (Preprint version) 1. INTRODUCTION This paper proposes an object oriented model for building expert systems. While this model enhances the knowledge modularity, it supports some other reasoning modes than traditional deduction. First, we present the characteristics of our object oriented model (COLL 89), then we highlight the features used to implement reasoning and allow knowledge acquisition.

While for classical logics, the motto ``Truth is invariant under the change of notation'' has been studied extensively, less attention has been paid to this aspect in defeasible logics. In this paper, we address equivalences and transformations among conditional knowledge bases that take renamings of the underlying signature into account. Extending previous proposals, we introduce the concepts of \emph{renaming normal form} and \emph{renaming antecedent normal form} for arbitrary knowledge bases and across different signatures. We present procedures to transform every knowledge base to corresponding, up to propositional normalization uniquely determined normal forms and study their properties. Using the obtained normal forms allows for systematically identifying equivalences among knowledge bases, for easier and more transparent comparisons, and for simplified descriptions of algorithms operating on knowledge bases by avoiding tedious, but uninteresting borderline cases.

These are the initial findings from Expert System's Artificial Intelligence platform, Clinical Research Navigator (CRN), which is collecting biomedical research information from official reports and studies published worldwide. Following the launch of its AI-based Clinical Research Navigator (CRN), which is focused on accelerating research on COVID-19, Expert System mined over 620,000 clinical trials, including more than 1,700 trials related to the virus that are taking place around the globe. Clinical landscape is changing rapidly in the context of the current pandemic situation. It is therefore critical to have a global coverage of the trial registries to serve clinical experts with appropriate and effective means to conduct their research on the disease. Expert System analyzed data collected with its Artificial Intelligence CRN platform to gain some insight on key trends correlated to official reports and studies published worldwide.