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Reasoning01:30

Reasoning

98
Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Deductive Reasoning01:16

Deductive Reasoning

55.4K
Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
55.4K
Inductive Reasoning00:59

Inductive Reasoning

60.6K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
60.6K
Ogive Graph01:07

Ogive Graph

5.7K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Related Experiment Video

Updated: Jul 17, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Learning and reasoning with graph data.

Manfred Jaeger1

  • 1Department of Computer Science, Aalborg University, Aalborg, Denmark.

Frontiers in Artificial Intelligence
|September 7, 2023
PubMed
Summary
This summary is machine-generated.

This review maps the field of artificial intelligence for learning and reasoning with graphs, unifying diverse methods like graph neural networks and logical deduction under a common model concept. It provides a framework for analyzing and integrating various graph data approaches.

Keywords:
graph datagraph neural networksinductive logic programmingneuro-symbolic integrationrepresentation learningstatistical relational learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Graphs are fundamental structures in AI, logic, and statistical learning.
  • Graph representation learning and graph neural networks are rapidly advancing fields.
  • Diverse methods exist for reasoning and learning with graph data.

Purpose of the Study:

  • To provide a unified conceptual framework for learning and reasoning with graphs.
  • To map the diverse landscape of graph-based AI methods.
  • To facilitate theoretical analysis and integration of different modeling paradigms.

Main Methods:

  • Introduced a general semantic model concept applicable to various frameworks (e.g., knowledge bases, GNNs, SVMs).
  • Surveyed common strategies for model specification (probabilistic factorization, feature construction).
  • Developed a taxonomy of reasoning tasks and expressed learning via maximum likelihood principle.

Main Results:

  • Established a unified perspective across logical deduction, node embeddings, and other graph learning techniques.
  • Provided a common semantic foundation for diverse graph modeling approaches.
  • Created a taxonomy for reasoning tasks and a principle for learning across frameworks.

Conclusions:

  • The review offers a coherent framework for understanding strengths and limitations of graph data approaches.
  • The proposed framework aids in combining and integrating different modeling paradigms.
  • This work serves as a basis for further theoretical advancements in graph-based AI.