u
2013
Toolset for Entity and Semantic Associations – Final Release: Deliverable 8.4 of project EuDML
LEE, Mark; Petr SOJKA; Radim ŘEHŮŘEK; Radim HATLAPATKA; Maroš KUCBEL et. al.
Basic information
Original name
Toolset for Entity and Semantic Associations – Final Release: Deliverable 8.4 of project EuDML
Authors
LEE, Mark (826 United Kingdom of Great Britain and Northern Ireland); Petr SOJKA (203 Czech Republic, guarantor, belonging to the institution); Radim ŘEHŮŘEK (203 Czech Republic, belonging to the institution); Radim HATLAPATKA (203 Czech Republic, belonging to the institution); Maroš KUCBEL (203 Czech Republic, belonging to the institution); Thierry BOUCHE (250 France); Claude GOUTORBE (250 France); Romeo ANGHELACHE (250 France) and Krzysztof WOJCIECHOWSKI (616 Poland)
Edition
1.0 as of 8th February 2013. 13 pp. Deliverable D8.4, 2013
Publisher
EU CIP-ICT-PSP project 250503 EuDML: The European Digital Mathematics Library
Other information
Type of outcome
Special-purpose publication
Field of Study
Informatics
Country of publisher
United Kingdom of Great Britain and Northern Ireland
Confidentiality degree
is not subject to a state or trade secret
RIV identification code
RIV/00216224:14330/13:00068102
Organization
Fakulta informatiky – Repository – Repository
Keywords in English
The European Digital Mathematics Library; EuDML;
Links
LG13010, research and development project. 250503, interní kód Repo.
V originále
In this document we describe the final release of the toolset for entity and semantic associations, integrating two versions (language dependent and language independent) of Unsupervised Document Similarity implemented by MU (using the gensim tool) and Citation Indexing, Resolution and Matching (UJF/CMD). We give a brief description of tools, the rationale behind decisions made, and provide elementary evaluation. Tools are integrated in the main project result, EuDML website, and they deliver the needed functionality for exploratory searching and browsing the collected documents. EuDML users and content providers thus benefit from millions of algorithmically generated similarity and citation links, developed using state of the art machine learning and matching methods.
Displayed: 30/6/2025 20:03