Since the appearance of the ‘Semantic Web’ and the development of ‘RDF’ and ’OWL’, ontologies gained new importance in computer science. Ontological structures can be used to make knowledge available to artificial intelligent systems. But such systems need commonsense knowledge to simulate human reasoning beyond the boundaries given by specific domains. For this purpose commonsense ontologies a...

To simulate human reasoning, artificial intelligent systems need background knowledge. This knowledge can be represented by an ontology. For specific domains domain ontologies [1] are used. But human inferences beyond the boundaries given by specific domains can only be imitated by means of commonsense knowledge. However, the generation of commonsense ontologies is a very difficult challenge [1...

Zero-shot learning — the problem of training and testing on a completely disjoint set classes relies greatly its ability to transfer knowledge from train test classes. Traditionally semantic embeddings consisting human-defined attributes or distributed word are used facilitate this by improving association between visual embeddings. In paper, we take advantage explicit relations nodes defined i...

The real life intelligent applications such as agents, expert systems, dialog understanding systems, weather forecasting systems, robotics etc. mainly focus on commonsense knowledge And basically these works on the knowledgebase which contains large amount of commonsense knowledge. The main intention of this work is to create a commonsense knowledgebase by using an effective methodology to retr...

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game d...

A temporal knowledge graph (TKG) stores the events derived from data involving time. Predicting is extremely challenging due to time-sensitive property of events. Besides, previous TKG completion (TKGC) approaches cannot represent both timeliness and causality properties events, simultaneously. To address these challenges, we propose a Logic Commonsense-Guided Embedding model (LCGE) jointly lea...

Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in expressiveness to subject-predicate-object (SPO) triples with simple S strings P O. This paper presents a method called ASCENT++ automatically build large-scale base (KB) of assertions, refined both ...

The central component of commonsense reasoning about causdity is the envisionment: a description of the behavior of a phvsical system that is derived from its structural description by qualitative simulation. Two problems with creating the envisionmcnt are the qualitative representation of quentlty and the detection of previously-unsuspcctcd points ot qualitative change. The representation pres...

A central goal of Artificial Intelligence is to create systems that embody commonsense knowledge in a reliable enough form that it can be used for reasoning in novel situations. Knowledge Infusion is an approach to this problem in which the commonsense knowledge is acquired by learning. In this paper we report on experiments on a corpus of a half million sentences of natural language text that ...

This paper reports on our recent work on modeling and automatically extracting vague, implicit event durations from text (Pan et al., 2006a, 2006b). It is a kind of commonsense knowledge that can have a substantial impact on temporal reasoning problems. We have also proposed a method of using normal distributions to model judgments that are intervals on a scale and measure their interannotator ...