MetaExp
| Autor: | Adrian Ziegler, Laurenz Seidel, Davide Mottin, Nikola Müller, Michael Hunger, Michael Vaichenker, Pius Ladenburger, Julius Rückin, Fatemeh Aghaei, Sebastian Bischoff, Freya Behrens, Emmanuel Müller, Fabian Stolp, Martin Preusse |
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| Rok vydání: | 2018 |
| Předmět: |
Information retrieval
Computer science Interface (Java) 0102 computer and information sciences 02 engineering and technology Similarity measure 01 natural sciences 010201 computation theory & mathematics 020204 information systems Similarity (psychology) Node (computer science) Core (graph theory) 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Domain knowledge Biological network |
| Zdroj: | WWW (Companion Volume) |
| DOI: | 10.1145/3184558.3186978 |
| Popis: | We present MetaExp, a system that assists the user during the exploration of large knowledge graphs, given two sets of initial nodes. At its core, MetaExp presents a small set of meta-paths to the user, which are sequences of relationships among node types. Such meta-paths do not overwhelm the user with complex structures, yet they preserve semantically-rich relationships in a graph. MetaExp engages the user in an interactive procedure, which involves simple meta-paths evaluations to infer a user-specific similarity measure. This similarity measure incorporates the domain knowledge and the preferences of the user, overcoming the fundamental limitations of previous methods based on local node neighborhoods or statically determined similarity scores. Our system provides a user-friendly interface for searching initial nodes and guides the user towards progressive refinements of the meta-paths. The system is demonstrated on three datasets, Freebase, a movie database, and a biological network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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