2026-09-19T10:36:08Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1227312023-08-24T15:59:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Julie A. Shah.
Zhou, Yilun(Computer scientist)Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-11-04T20:21:30Z
2019-11-04T20:21:30Z
2019
2019
https://hdl.handle.net/1721.1/122731
1124679389
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 49-52).
In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, meaning that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this thesis I instead propose to learn to predict path quality from crowdsourced human assessments. Since a generic task-independent notion of quality is concerned, human participants are asked to rank paths according to their subjective assessment of the paths' naturalness, without being given specific definitions or guidelines. Experiments show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths in various applications.
by Yilun Zhou.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
52 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Data-driven path filtering in ConceptNet
Thesis
application/pdf
2019-11-04T20:21:29Z
Publication
Master
EECS
unknown
unknown
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<Abstract>In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, meaning that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this thesis I instead propose to learn to predict path quality from crowdsourced human assessments. Since a generic task-independent notion of quality is concerned, human participants are asked to rank paths according to their subjective assessment of the paths' naturalness, without being given specific definitions or guidelines. Experiments show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths in various applications.</Abstract>
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