A question answering system (QA System) was developed that uses graph-pattern association rules on the YAGO knowledge base. The answer as output of the system is provided based on a user question as input. If the answer is missing or unavailable in the database, then graph-pattern association rules are used to get the answer. The architecture of this question answering system is as follows: question classification, graph component generation, query generation, and query processing. The question answering system uses association graph patterns in a waterfall model. In this paper, the architecture of the system is described, specifically discussing its reasoning and performance capabilities. The results of this research is that rules with high confidence and correct logic produce correct answers, and vice versa.

Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.

One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is to map new questions to formerly answered questions that are `similar'. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare machine learning and deep learning methods for RQE using different kinds of datasets, including textual inference, question similarity and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources, that we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results also support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.

In e-commerce portals, generating answers for product-related questions has become a crucial task. In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes. Unlike existing question-answering problems, answer generation in e-commerce confronts three main challenges: (1) Reviews are informal and noisy; (2) joint modeling of reviews and key-value product attributes is challenging; (3) traditional methods easily generate meaningless answers. To tackle above challenges, we propose an adversarial learning based model, named PAAG, which is composed of three components: a question-aware review representation module, a key-value memory network encoding attributes, and a recurrent neural network as a sequence generator. Specifically, we employ a convolutional discriminator to distinguish whether our generated answer matches the facts. To extract the salience part of reviews, an attention-based review reader is proposed to capture the most relevant words given the question. Conducted on a large-scale real-world e-commerce dataset, our extensive experiments verify the effectiveness of each module in our proposed model. Moreover, our experiments show that our model achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations.

As data people, we very typically spend a great deal of time summarizing our findings to stakeholders in a clear, concise and impactful way. Often times, due to the lack of infrastructure, we end up using presentation files with chart images. This can become a real pain when we need to make modifications or when the analysis needs to "live on". Typically, this is where BI (business intelligence) or dashboard tools shine. Unfortunately, this can be a major stumbling block for smaller shops who rely on a lot of local analysis and may not have the budget for a BI tool.

Google voice search is an omnipresent choice for users on the move via their mobile devices. Voice search has made its way into our everyday life and the trends suggest that it's here to stay. Voice search is a function that makes it possible for users to make a query via a search engine using their voice. They can either do this using a smartphone or home assistant. This article analyses the uniqueness of voice search and why it should be part of your marketing strategy.

It's been almost two years since Pandora launched its on-demand music streaming service. In that time, the company has done a solid job of fixing some of the issues that cropped up at launch and even adding some features the competition hasn't got to yet (like downloading songs to an Apple Watch for offline playback). Today, Pandora's adding another feature that some of its competitors have: Voice Mode. But, as usual, Pandora believes that the amount of information it has on both the music in its catalog as well as its users will set its voice features apart. For starters, Pandora built Voice Mode internally, from the ground up, something Chief Product Officer Chris Phillips says was key in Voice Mode being a more personal music assistant.

Workday held its European Rising conference last year. One of the key themes from the event was how it is embedding AI into its solutions. Having spoken to Chano Fernandez, Co-President Workday early in the week, we also spoke to Barbry McGann, SVP Product Management at Workday later in the conference. The conversation centred around the main message that Workday delivered at its latest conference, AI. One of its recent product innovations was Skills Cloud.

Any system which performs goal-directed continual learning must not only learn incrementally but process and absorb information incrementally. Such a system also has to understand when its goals have been achieved. In this paper, we consider these issues in the context of question answering. Current state-of-the-art question answering models reason over an entire passage, not incrementally. As we will show, naive approaches to incremental reading, such as restriction to unidirectional language models in the model, perform poorly. We present extensions to the DocQA [2] model to allow incremental reading without loss of accuracy. The model also jointly learns to provide the best answer given the text that is seen so far and predict whether this best-so-far answer is sufficient.

Decision Optimization is now available in the Watson Studio ecosystem with a seamless integration of the CPLEX solvers in the Python runtime environment. Watson Studio now provides everything you need to describe your data, gain insight, and make an optimal decision in the very same ecosystem. Get started right away and learn how to make more intelligent marketing and targeting decisions. Decision Optimization is a subset of data science techniques frequently used for prescriptive analytics. Most documented data science use cases are dedicated to revealing or predicting unknown or future data that is not under your control.