External knowledge (a.k.a side information) plays a critical role in zero-shot learning (ZSL) which aims to predict with unseen classes that have never appeared in training data. Several kinds of external knowledge such as text and attribute have been widely investigated, but they alone are limited with incomplete semantics. Therefore, some very recent studies propose to use Knowledge Graph (KG) due to its high expressivity and compatibility for representing kinds of knowledge. However, the ZSL community is still short of standard benchmarks for studying and comparing different KG-based ZSL methods. In this paper, we proposed 5 resources for KG-based research in zero-shot image classification (ZS-IMGC) and zero-shot KG completion (ZS-KGC). For each resource, we contributed a benchmark and its KG with semantics ranging from text to attributes, from relational knowledge to logical expressions. We have clearly presented how the resources are constructed, their statistics and formats, and how they can be utilized with cases in evaluating ZSL methods' performance and explanations. Our resources are available at https://github.com/China-UK-ZSL/Resources_for_KZSL.

Recently, low-dimensional vector space representations of knowledge graphs (KGs) have been applied to find answers to conjunctive queries (CQs) over incomplete KGs. However, the current methods only focus on inductive reasoning, i.e. answering CQs by predicting facts based on patterns learned from the data, and lack the ability of deductive reasoning by applying external domain knowledge. Such (expert or commonsense) domain knowledge is an invaluable resource which can be used to advance machine intelligence. To address this shortcoming, we introduce a neural-symbolic method for ontology-mediated CQ answering over incomplete KGs that operates in the embedding space. More specifically, we propose various data augmentation strategies to generate training queries using query-rewriting based methods and then exploit a novel loss function for training the model. The experimental results demonstrate the effectiveness of our training strategies and the new loss function, i.e., our method significantly outperforms the baseline in the settings that require both inductive and deductive reasoning.

According to a 2020 report by Emergence, 80% of the global workforce does not sit behind a desk. According to a 2020 report by Emergence, 80% of the global workforce does not sit behind a desk. That’s an overwhelming majority of workers who are deskless and increasingly reliant on technology to do their jobs in industries impacted by factors like the growing skills gap and, most recently, a global pandemic. While employers have done much to address the needs of deskless workers over the past year, there’s untapped opportunity to make these workers – and, in turn, the industries they support – more efficient, resilient, and safe in the current working environment and beyond. On Wednesday, June 23, Rolls Royce’s XXX will join industry experts YYY from PwC and ZZZ from Librestream to teach enterprises about the power of Augmented Reality (AR) and Artificial Intelligence (AI) to enable deskless workers around the world and build knowledge networks capable of sustaining the deskless workforce for decades to come. In this webinar, you will learn: Why traditional deskless worker solutions have fallen short at a time when effective remote collaboration is of peak importance How AR plus AI can improve knowledge sharing among distributed workforces, reduce knowledge loss, eliminate inefficiencies, enhance safety, improve sustainability, lower costs, and more Real-world use cases of AR and AI on devices like Microsoft’s HoloLens and the generated ROI Why organizations with large deskless workforces prefer solutions like Librestream’s AI Connected Expert Vision: Broad device support, specialized accessories, etc. Realizing true IoT: Where AI and AR converge to create the fully connected, deskless worker of tomorrow

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework. We provide a thorough analysis to gain insights into model performance.

This paper introduces our position on the critical issue of bias that recently appeared in AI applications. Specifically, we discuss the combination of current technologies used in AI applications i.e., Machine Learning and Knowledge Graphs, and point to their involvement in (de)biased applications of the C4I domain. Although this is a wider problem that currently emerges from different application domains, bias appears more critical in C4I than in others due to its security-related nature. While proposing certain actions to be taken towards debiasing C4I applications, we acknowledge the immature aspect of this topic within the Knowledge Graph and Semantic Web communities.

The intelligent question answering (IQA) system can accurately capture users' search intention by understanding the natural language questions, searching relevant content efficiently from a massive knowledge-base, and returning the answer directly to the user. Since the IQA system can save inestimable time and workforce in data search and reasoning, it has received more and more attention in data science and artificial intelligence. This article introduced a domain knowledge graph using the graph database and graph computing technologies from massive heterogeneous data in electric power. It then proposed an IQA system based on the electrical power knowledge graph to extract the intent and constraints of natural interrogation based on the natural language processing (NLP) method, to construct graph data query statements via knowledge reasoning, and to complete the accurate knowledge search and analysis to provide users with an intuitive visualization. This method thoroughly combined knowledge graph and graph computing characteristics, realized high-speed multi-hop knowledge correlation reasoning analysis in tremendous knowledge. The proposed work can also provide a basis for the context-aware intelligent question and answer.

Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. Another advantage of using ontologies is that they do not need a learning process, meaning that we do not need the train data or time before using them. This paper presents a practical use of an ontology for a classification problem from the financial domain. It first transforms a given ontology to a graph and proceeds with generalization with the aim to find common semantic descriptions of the input sets of financial concepts. We present a solution to the shared task on Learning Semantic Similarities for the Financial Domain (FinSim-2 task). The task is to design a system that can automatically classify concepts from the Financial domain into the most relevant hypernym concept in an external ontology - the Financial Industry Business Ontology. We propose a method that maps given concepts to the mentioned ontology and performs a graph search for the most relevant hypernyms. We also employ a word vectorization method and a machine learning classifier to supplement the method with a ranked list of labels for each concept.

Policies are authoritative assets that are present in multiple domains to support decision-making. They describe what actions are allowed or recommended when domain entities and their attributes satisfy certain criteria. It is common to find policies that contain geographical rules, including distance and containment relationships among named locations. These locations' polygons can often be found encoded in geospatial datasets. We present an approach to transform data from geospatial datasets into Linked Data using the OWL, PROV-O, and GeoSPARQL standards, and to leverage this representation to support automated ontology-based policy decisions. We applied our approach to location-sensitive radio spectrum policies to identify relationships between radio transmitters coordinates and policy-regulated regions in Census.gov datasets. Using a policy evaluation pipeline that mixes OWL reasoning and GeoSPARQL, our approach implements the relevant geospatial relationships, according to a set of requirements elicited by radio spectrum domain experts.

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few annotations for each field. The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model. We use Schema2QA to generate Q&A systems for five Schema.org domains, restaurants, people, movies, books and music, and obtain an overall accuracy between 64% and 75% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a Schema.org schema, no additional manual effort is needed to create a Q&A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, obtaining a 64% accuracy on crowdsourced questions with no manual effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions that can be answered using Schema.org. Its performance is comparable to Google Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all these assistants by at least 18% on more complex, long-tail questions.

A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion (KACC), which are crucial for human to recognize the world and manage learned knowledge. Existing studies mainly focus on partial aspects of KACC. In order to promote thorough analyses for KACC abilities of models, we propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty. Specifically, we collect new datasets that contain larger concept graphs, abundant cross-view links as well as dense entity graphs. Based on the datasets, we propose novel tasks such as multi-hop knowledge abstraction (MKA), multi-hop knowledge concretization (MKC) and then design a comprehensive benchmark. For MKA and MKC tasks, we further annotate multi-hop hierarchical triples as harder samples. The experimental results of existing methods demonstrate the challenges of our benchmark. The resource is available at https://github.com/thunlp/KACC.