Predicting Invariant Nodes in Large Scale Semantic Knowledge Graphs
| Autor: | Martín Ariel Domínguez, Pablo Ariel Duboué, Damián Barsotti |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Change over time
Theoretical computer science business.industry Computer science Natural language generation 020207 software engineering 02 engineering and technology Data structure Graph Software 020204 information systems 0202 electrical engineering electronic engineering information engineering Semantic memory Cache Invariant (mathematics) business |
| Zdroj: | Information Management and Big Data ISBN: 9783319905952 SIMBig (Revised Selected Papers) |
| DOI: | 10.1007/978-3-319-90596-9_4 |
| Popis: | Understanding and predicting how large scale knowledge graphs change over time has direct implications in software and hardware associated with their maintenance and storage. An important subproblem is predicting invariant nodes, that is, nodes within the graph will not have any edges deleted or changed (add-only nodes) or will not have any edges added or changed (del-only nodes). Predicting add-only nodes correctly has practical importance, as such nodes can then be cached or represented using a more efficient data structure. This paper presents a logistic regression approach using attribute-values as features that achieves 90%+ precision on DBpedia yearly changes trained using Apache Spark. The paper concludes by outlining how we plan to use these models for evaluating Natural Language Generation algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|