TY - GEN
T1 - Set Semantic Similarity for Image Prosthetic Knowledge Exchange
AU - Franzoni, Valentina
AU - Li, Yuanxi
AU - Milani, Alfredo
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/6/28
Y1 - 2019/6/28
N2 - Concept information can be expressed by text, images or general objects which semantic meaning is clear to a human in a specific cultural context. For a computer, when available, text with its semantics (e.g., metadata, comments, captions) can convey more precise meaning than images or general objects with low-level features (e.g., color distribution, shapes, sound peaks) to extract the concept underlying the object. Among semantic measures, web-based proximity measures e.g., confidence, PMING, NGD, Jaccard, Dice, are particularly useful for concept evaluation, exploiting statistical data provided by search engines on terms and expressions provided in texts associated with the object. Where Artificial Intelligence can be a support for impaired individuals, e.g., having disabilities related to vision and hearing, understanding the concept underlying an object can be critical for an intelligent artificial assistant. In this work we propose to use the set semantic distance, already used in literature for semantic similarity measurement of web objects, as a tool for artificial assistants to support knowledge extraction; in other words, as prosthetic knowledge.
AB - Concept information can be expressed by text, images or general objects which semantic meaning is clear to a human in a specific cultural context. For a computer, when available, text with its semantics (e.g., metadata, comments, captions) can convey more precise meaning than images or general objects with low-level features (e.g., color distribution, shapes, sound peaks) to extract the concept underlying the object. Among semantic measures, web-based proximity measures e.g., confidence, PMING, NGD, Jaccard, Dice, are particularly useful for concept evaluation, exploiting statistical data provided by search engines on terms and expressions provided in texts associated with the object. Where Artificial Intelligence can be a support for impaired individuals, e.g., having disabilities related to vision and hearing, understanding the concept underlying an object can be critical for an intelligent artificial assistant. In this work we propose to use the set semantic distance, already used in literature for semantic similarity measurement of web objects, as a tool for artificial assistants to support knowledge extraction; in other words, as prosthetic knowledge.
KW - Artificial Intelligence
KW - Assistants
KW - Group distance
KW - Information retrieval
KW - Semantic proximity
UR - https://www.scopus.com/pages/publications/85068607022
U2 - 10.1007/978-3-030-24311-1_37
DO - 10.1007/978-3-030-24311-1_37
M3 - Conference proceeding
AN - SCOPUS:85068607022
SN - 9783030243104
T3 - Lecture Notes in Computer Science
SP - 513
EP - 525
BT - Computational Science and Its Applications – ICCSA 2019
A2 - Misra, Sanjay
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Stankova, Elena
A2 - Korkhov, Vladimir
A2 - Torre, Carmelo
A2 - Tarantino, Eufemia
A2 - Rocha, Ana Maria A.C.
A2 - Taniar, David
A2 - Apduhan, Bernady O.
PB - Springer Cham
T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019
Y2 - 1 July 2019 through 4 July 2019
ER -
