Knowing What to Believe (when you already know something)

By Jeff Pasternack, Dan Roth. International Conference on Computational Linguistics, 2010

309 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/579e1e9217cfed6d563cedf8f8fdcd1604fc0917

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Theodoros Rekatsinas
2. Ihab F. Ilyas
3. Xu Chu
4. Yaliang Li
5. Christopher R'e
6. Qi Li
7. Jing Gao
8. Xin Luna Dong

Citations in the corpus

• Evaluating Event Credibility on Twitter (2012, methodology): “This work was followed by some more fact finder algorithms: Sums, Average.Log, Investment, Pooled In-vestment by Pasternack et al. [14].”
• Truth Finding on the Deep Web: Is the Problem Solved? (2012, methodology): “Recently, many data fusion techniques have been proposed to resolve conflicts and find the truth [2, 3, 6, 7, 8, 10, 13, 14, 16, 17, 18, 19, 20].”
• Big data integration (2013, background): “To address such veracity related challenges, techniques have been proposed to find the single truth from conflicting values [49], [32], [48], [39], [38], [18], [9], [47] and to find multiple truths [50].”
• KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, background): “• RelationalTables has three tables: Person has personal information joined on the attribute country from two sources: a biographic table extracted from wikipedia [32], and a country table obtained from a wikipedia page(3) resulting in 316K tuples.”
• A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration (2012, methodology): “Investment, PooledInvestment [10] [11].”
• On truth discovery in social sensing: A maximum likelihood estimation approach (2012, methodology): “Log, Investment, and Pooled Investment [22].”
• Knowledge Graphs (2020, unknown): “• Sums [397] adapts the HITS algorithm [291] by defining sources as hubs (with 0 authority score) and facts as authorities (with 0 hub score).”
• A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (2014, methodology): “Two datasets(1) were crawled by the authors in [23].”
• SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, background): “…collaborators from a medical school—who are currently engaged in extracting information from scientific articles to populate a structured data repository—we find that using data fusion can be challenging for users despite the numerous approaches proposed in the literature [9, 14, 21, 22, 28, 38].”
• A Survey on Truth Discovery (2015, methodology): “In [32], prior knowledge is translated into propositional constraints that are integrated into each round of truth discovery process.”
• Fusing data with correlations (2014, background): “Among these approaches, [6, 14, 19, 20, 21, 23, 24] jointly infer truth and source quality, but they assume the conflicting-triple, closedworld semantics.”