Many large-scale knowledge bases simultaneously represent two views of knowledge graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. Existing KG embedding models, however, merely focus on representing one of the two views alone. In this paper, we propose a novel two-view KG embedding model, JOIE, with the goal to produce better knowledge embedding and enable new applications that rely on multi-view knowledge. JOIE employs both cross-view and intra-view modeling that learn on multiple facets of the knowledge base. The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities. The intra-view models are trained to capture the structured knowledge of instance and ontology views in separate embedding spaces, with a hierarchy-aware encoding technique enabled for ontologies with hierarchies. We explore multiple representation techniques for the two model components and investigate with nine variants of JOIE. Our model is trained on large-scale knowledge bases that consist of massive instances and their corresponding ontological concepts connected via a (small) set of cross-view links. Experimental results on public datasets show that the best variant of JOIE significantly outperforms previous models on instance-view triple prediction task as well as ontology population on ontologyview KG. In addition, our model successfully extends the use of KG embeddings to entity typing with promising performance.

Artificial intelligences (AI) are increasingly being embodied and embedded in the world to carry out tasks and support decision-making with and for people. Robots, recommender systems, voice assistants, virtual humans - do these disparate types of embodied AI have something in common? Here we show how they can manifest as "socially embodied AI." We define this as the state that embodied AI "circumstantially" take on within interactive contexts when perceived as both social and agentic by people. We offer a working ontology that describes how embodied AI can dynamically transition into socially embodied AI. We propose an ontological heuristic for describing the threshold: the Tepper line. We reinforce our theoretical work with expert insights from a card sort workshop. We end with two case studies to illustrate the dynamic and contextual nature of this heuristic.

In this paper we establish a link between preferential semantics for description logics and self-organising maps, which have been proposed as possible candidates to explain the psychological mechanisms underlying category generalisation. In particular, we show that a concept-wise multipreference semantics, which takes into account preferences with respect to different concepts and has been recently proposed for defeasible description logics, can be used to to provide a logical interpretation of SOMs. We also provide a logical interpretation of SOMs in terms of a fuzzy description logic as well as a probabilistic account.

Ontology encompasses problems about the most general properties and relations of the entities which do exist. Ontology is the way we can connect entities and understand their relationships, their types & tokens. With ontology one can enable such a description, but first we need to formally specify components such as individuals (tokens, instances of objects), classes (types), attributes (properties) and relations (limitations & restrictions, rules & axioms). Formal ontology gives precise mathematical formulations of the properties and relations of certain entities. Such theories usually propose axioms about these entities in question, represented as mathematical models or in some formal language based on some system of formal logic.

Context: Conflicts between software requirements bring uncertainties to product development. Some great approaches have been proposed to identify these conflicts. However, they usually require the software requirements represented with specific templates and/or depend on other external source which is often uneasy to build for lots of projects in practice. Objective: We aim to propose an approach Finer Semantic Analysis-based Requirements Conflict Detector (FSARC) to automatically detecting the conflicts between the given natural language functional requirements by analyzing their finer semantic compositions. Method: We build a harmonized semantic meta-model of functional requirements with the form of eight-tuple. Then we propose algorithms to automatically analyze the linguistic features of requirements and to annotate the semantic elements for their semantic model construction. And we define seven types of conflicts as long as their heuristic detecting rules on the ground of their text pattern and semantical dependency. Finally, we design and implement the algorithm for conflicts detection. Results: The experiment with four requirement datasets illustrates that the recall of FSARC is nearly 100% and the average precision is 83.88% on conflicts detection. Conclusion: We provide a useful tool for detecting the conflicts between natural language functional requirements to improve the quality of the final requirements set. Besides, our approach is capable of transforming the natural language functional requirements into eight semantic tuples, which is useful not only the detection of the conflicts between requirements but also some other tasks such as constructing the association between requirements and so on.

Using of funcation in different place in program with same name or the process of have two or more function with the same name but parameter are different called function overloading. Public data member are also accessed in the same way given however private data member are not allowed to accessed directly by object.

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues.

The 1980s saw the evolution of computing as it transitioned from industry to homes through the boom of personal computers. In the field of data management, the Relational Database industry was developing rapidly (Oracle, Sybase, IBM, among others). Object-oriented abstractions were developed as a new form of representational independence. The Internet changed the way people communicated and exchanged information.

Drug discovery and development is an extremely complex process, with high attrition contributing to the costs of delivering new medicines to patients. Recently, various machine learning approaches have been proposed and investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Among these techniques, it is especially those using Knowledge Graphs that are proving to have considerable promise across a range of tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In such a knowledge graph-based representation of drug discovery domains, crucial elements including genes, diseases and drugs are represented as entities or vertices, whilst relationships or edges between them indicate some level of interaction. For example, an edge between a disease and drug entity might represent a successful clinical trial, or an edge between two drug entities could indicate a potentially harmful interaction. In order to construct high-quality and ultimately informative knowledge graphs however, suitable data and information is of course required. In this review, we detail publicly available primary data sources containing information suitable for use in constructing various drug discovery focused knowledge graphs. We aim to help guide machine learning and knowledge graph practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. Overall we hope this review will help motivate more machine learning researchers to explore combining knowledge graphs and machine learning to help solve key and emerging questions in the drug discovery domain.

Zero-shot Learning (ZSL), which aims to predict for those classes that have never appeared in the training data, has arisen hot research interests. The key of implementing ZSL is to leverage the prior knowledge of classes which builds the semantic relationship between classes and enables the transfer of the learned models (e.g., features) from training classes (i.e., seen classes) to unseen classes. However, the priors adopted by the existing methods are relatively limited with incomplete semantics. In this paper, we explore richer and more competitive prior knowledge to model the inter-class relationship for ZSL via ontology-based knowledge representation and semantic embedding. Meanwhile, to address the data imbalance between seen classes and unseen classes, we developed a generative ZSL framework with Generative Adversarial Networks (GANs). Our main findings include: (i) an ontology-enhanced ZSL framework that can be applied to different domains, such as image classification (IMGC) and knowledge graph completion (KGC); (ii) a comprehensive evaluation with multiple zero-shot datasets from different domains, where our method often achieves better performance than the state-of-the-art models. In particular, on four representative ZSL baselines of IMGC, the ontology-based class semantics outperform the previous priors e.g., the word embeddings of classes by an average of 12.4 accuracy points in the standard ZSL across two example datasets (see Figure 4).