TY - GEN
T1 - Heuristics for semantic path search in Wikipedia
AU - Franzoni, Valentina
AU - Mencacci, Marco
AU - Mengoni, Paolo
AU - Milani, Alfredo
PY - 2014
Y1 - 2014
N2 - In this paper an approach based on Heuristic Semantic Walk (HSW) is presented, where semantic proximity measures among concepts are used as heuristics in order to guide the concept chain search in the collaborative network of Wikipedia, encoding problem-specific knowledge in a problem-independent way. Collaborative information and multimedia repositories over the Web represent a domain of increasing relevance, since users cooperatively add to the objects tags, label, comments and hyperlinks, which reflect their semantic relationships, with or without an underlying structure. As in the case of the so called Big Data, methods for path finding in collaborative web repositories require solving major issues such as large dimensions, high connectivity degree and dynamical evolution of online networks, which make the classical approach ineffective. Experiments held on a range of different semantic measures show that HSW lead to better results than state of the art search methods, and points out the relevant features of suitable proximity measures for the Wikipedia concept network. The extracted semantic paths have many relevant applications such as query expansion, synthesis of explanatory arguments, and simulation of user navigation.
AB - In this paper an approach based on Heuristic Semantic Walk (HSW) is presented, where semantic proximity measures among concepts are used as heuristics in order to guide the concept chain search in the collaborative network of Wikipedia, encoding problem-specific knowledge in a problem-independent way. Collaborative information and multimedia repositories over the Web represent a domain of increasing relevance, since users cooperatively add to the objects tags, label, comments and hyperlinks, which reflect their semantic relationships, with or without an underlying structure. As in the case of the so called Big Data, methods for path finding in collaborative web repositories require solving major issues such as large dimensions, high connectivity degree and dynamical evolution of online networks, which make the classical approach ineffective. Experiments held on a range of different semantic measures show that HSW lead to better results than state of the art search methods, and points out the relevant features of suitable proximity measures for the Wikipedia concept network. The extracted semantic paths have many relevant applications such as query expansion, synthesis of explanatory arguments, and simulation of user navigation.
KW - collaborative networks
KW - heuristics search
KW - information retrieval
KW - random walk
KW - semantic networks
KW - semantic similarity measures
UR - http://www.scopus.com/inward/record.url?scp=84904900353&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-09153-2_25
DO - 10.1007/978-3-319-09153-2_25
M3 - Conference proceeding
AN - SCOPUS:84904900353
SN - 9783319091525
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 327
EP - 340
BT - Computational Science and Its Applications, ICCSA 2014
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Rocha, Ana Maria A. C.
A2 - Torre, Carmelo
A2 - Rocha, Jorge Gustavo
A2 - Falcão, Maria Irene
A2 - Taniar, David
A2 - Apduhan, Bernady O.
A2 - Gervasi, Osvaldo
PB - Springer Verlag
T2 - 14th International Conference on Computational Science and Its Applications, ICCSA 2014
Y2 - 30 June 2014 through 3 July 2014
ER -