Probase: a probabilistic taxonomy for text understanding

By Wentao Wu, Hongsong Li, Haixun Wang, Kenny Q. Zhu. SIGMOD Conference, 2012

890 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/760103b363b1557372e048c4c31b5f01162bfcfa

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In Artificial Intelligence

Citations in the corpus

Domain-specific Knowledge Graphs: A survey (2020, background): “KnowledgeVault [23], NELL [28]); and (iv) techniques that build taxonomies (hierarchy) attained based on information from the Web [29].”
Co-Attentive Multi-Task Learning for Explainable Recommendation (2019, methodology): “We derive the concepts by utilizing Microsoft Concept Graph2 [Wu et al., 2012; Wang et al., 2015], a widely used knowledge graph with about 18 million concepts or instances.”
Multi-Modal Knowledge Graph Construction and Application: A Survey (2022, neutral): “Probase [10]) and geographic knowledge (e.”
Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, methodology): “Wang et al. [41] propose amulti-phase algorithm that using the universal probabilistic taxonomy called Probase [42] is capable of understanding the entitles, attributes and values in many tables on the Web.”
Open-Universe Weighted Model Counting (2017, background): “The advent of large-scale probabilistic knowledge bases, such as Google’s Knowledge Vault (Dong et al. 2014) and Microsoft’s Probase (Wu et al. 2012), often containing billions of tuples and structured data extracted from the Web and other text corpora has generated enormous interest in relational p”
Locally Adaptive Translation for Knowledge Graph Embedding (2015, background): “In the past decade, the great success of constructing these large-scale knowledge graphs have advanced many realistic applications, such as, link prediction (Liu et al. 2014) and document understanding (Wu et al. 2012).”
Explainable Recommendation through Attentive Multi-View Learning (2019, methodology): “To build Υ , we leverage the Microsoft Concept Graph 1 (Wu et al. 2012; Wang et al. 2015), which is a widely used knowledge graph with over 5 million concepts and 85 million “IsA” relations (e.g., cat IsA animal ).”
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research (2020, methodology): “We believe that entity linking-based approaches, coupled with semantic graphs like Probase (Wu et al., 2012), should be able to perform reasonably while overcoming scalability issues.”
Trinity: a distributed graph engine on a memory cloud (2013, background): “Trinity supports both online graph query processing and offline graph analytics, and it has been used for real life applications, including knowledgebases [33], knowledge graphs [36], and social networks.”