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Knowledge graph reasoning, vital for addressing incompleteness and supporting applications, faces challenges with the continuous growth of graphs. To address this challenge, several inductive reasoning models for encoding emerging entities have ...

• Introduces entity type features for fine-grained entity encoding.
• Proposes attention mechanism combining co-occurrence and similarity.
• Alleviates KG sparsity with soft facts and confidence scores.

Extrapolating future events based on historical information in temporal knowledge graphs (TKGs) holds significant research value and practical applications. In this field, the methods currently utilized can be classified as either embedding-based ...

• Proposed a hybrid rule-based and embedding-based method for inductive reasoning.
• Integrated logical rules inherent in relations with interaction preferences.
• Demonstrated zero-shot transferability of the proposed method on related ...

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and ...

Proposal of a tool to support dissonance discovery and control.Dissonances defined as conflicts into a knowledge base or between knowledge bases.Formalism based on rules to identify dissonances as affordances and inconsistencies.Feasibility study on car ...

OCF-networks provide the possibility to combine qualitative information expressed by rankings of (conditional) formulas with the strong structural information of a network, in this respect being a qualitative variant of the better known Bayesian ...

Progressive matrix problems are frequently used in modern IQ tests. In a progressive matrix problem, the task is to identify the missing element that completes the pattern of a pictorial matrix. We present a method for solving progressive matrix ...

Number sequence problems appear frequently in IQ tests, where the task is to extrapolate finite sequences of integers. This paper presents a computational method for solving number sequence problems appearing in IQ tests. The assumption that these ...

In this paper, there will be a particular focus on mental models and their application to inductive reasoning within the realm of instruction. A basic assumption of this study is the observation that the construction of mental models and related ...

Semantic Web search is currently one of the hottest research topics in both Web search and the Semantic Web. In previous work, we have presented a novel approach to Semantic Web search, which allows for evaluating ontology-based complex queries that ...

The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective ...

The paper proposes a multi-viewpoint system to support human abductive reasoning for diagnosis, prognosis and trial-and-error activities for supervising automated systems. This multi-viewpoint approach interprets the same set of events from the ...

Content adaptation is a well-known technique to help portable devices present Web pages as smoothly as desktops do. Because of limited I/O and weak transmission capability, adaptations are usually performed by either transcoding or resizing multimedia ...

Earlier studies have examined the issues of brain spatial localization in human sentential inductive reasoning using PET/fMRI. However, the temporal characteristics of such reasoning remain unclear. In our present study, we have used ERP (Event-related ...

Recent studies indicated that the pre-motor area may be recruited in human higher level cognitive functions, including inductive reasoning. In the present study, a typical task of inductive reasoning, function-finding, was explored using functional MRI. ...

If artificial neural networks are ever to form the foundation for higher level cognitive behaviors in machines or to realize their full potential as explanatory devices for human cognition, they must show signs of autonomy, multifunction operation, and ...

We lay down plans to study how Inductive Reasoning Ability (IRA) affects the analyzing and understanding of Program Visualization (PV) systems. Current PV systems do not take into account the abilities of the user but show always the same visualization ...

In the line of realizing the Semantic-Web by means of mechanized practices, we tackle the problem of building ontologies, assisting the knowledge engineers' job by means of Machine Learning techniques. In particular, we investigate on solutions for the ...

This paper introduces a novel transductive neuro-fuzzy inference model with weighted data normalization (TWNFI). In transductive systems a local model is developed for every new input vector, based on a certain number of data that are selected from the ...