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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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Embedding Open-domain Common-sense Knowledge from Text.

Travis Goodwin1, Sanda Harabagiu1

  • 1Human Language Technology Research Institute, University of Texas at Dallas, Richardson, TX 75083-0688, USA.

LREC ... International Conference on Language Resources & Evaluation : [Proceedings]. International Conference on Language Resources & Evaluation
|June 27, 2017
PubMed
Summary
This summary is machine-generated.

This study demonstrates that knowledge embeddings effectively capture both common-sense and domain-specific knowledge, such as medical information. Acquired common-sense knowledge showed less neutrality and plausibility compared to medical knowledge.

Keywords:
Common-sense knowledgeknowledge embeddingmedical domain knowledge

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Area of Science:

  • Natural Language Processing
  • Knowledge Representation
  • Machine Learning

Background:

  • Language comprehension relies on common-sense and domain-specific knowledge.
  • Open information extraction and knowledge embeddings are key to capturing this knowledge.
  • Knowledge graphs represent concepts and relations for learning embeddings.

Purpose of the Study:

  • To acquire and represent common-sense and medical knowledge using knowledge embeddings.
  • To evaluate the effectiveness of a unified knowledge acquisition methodology.
  • To compare the characteristics of acquired common-sense and medical knowledge.

Main Methods:

  • Utilized open information extraction techniques to build knowledge graphs.
  • Employed knowledge embeddings to represent concepts and relations.
  • Acquired common-sense knowledge from blogs, books, and WordNet.
  • Acquired medical knowledge from electronic health records.

Main Results:

  • The knowledge acquisition methodology using knowledge embeddings proved effective for both common-sense and medical knowledge.
  • Learned knowledge embeddings captured semantic compositionality and implied knowledge.
  • Acquired common-sense knowledge was less neutral and less plausible than medical knowledge.
  • Differences in neutrality and plausibility reflect the complexities of common-sense knowledge acquisition.

Conclusions:

  • Knowledge embeddings offer a robust method for acquiring diverse forms of knowledge.
  • The methodology is adaptable for both general common-sense reasoning and specialized domains like medicine.
  • Further research is needed to address the nuances and challenges in acquiring common-sense knowledge.