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1. Measuring the Importance of User-Generated Content to Search Engines

   Vincent, Nicholas; Johnson, Isaac; Sheehan, Patrick; Hecht, Brent (January 2019, Proceedings of the 2019 International AAAI Conference on Web and Social Media)

   Search engines are some of the most popular and profitable intelligent technologies in existence. Recent research, however, has suggested that search engines may be surprisingly dependent on user-created content like Wikipedia articles to address user information needs. In this paper, we perform a rigorous audit of the extent to which Google leverages Wikipedia and other user-generated content to respond to queries. Analyzing results for six types of important queries (e.g. most popular, trending, expensive advertising), we observe that Wikipedia appears in over 80% of results pages for some query types and is by far the most prevalent individual content source across all query types. More generally, our results provide empirical information to inform a nascent but rapidly-growing debate surrounding a highly consequential question: Do users provide enough value to intelligent technologies that they should receive more of the economic benefits from intelligent technologies? 

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2. Structural Similarity Search for Formulas using Leaf-Root Paths in Operator Subtrees

   https://doi.org/0.1007/978-3-030-15712-8\_8  

   Zhong, Wei; Zanibbi, Richard (January 2019, Proceedings of the European Conference on Information Retrieval (ECIR))

   We present a new search method for mathematical formulas based on Operator Trees (OPTs) representing the application of operators to operands. Our method provides (1) a simple indexing scheme using OPT leaf-root paths, (2) practical matching of the K largest common subexpressions, and (3) scoring matched OPT subtrees by counting nodes corresponding to visible symbols, weighting operators lower than operands. Using the largest common subexpression (K=1), we outperform existing formula search engines for non-wildcard queries on the NTCIR-12 Wikipedia Formula Browsing Task. Stronger results are obtained when using additional subexpressions for scoring. Without parallelization or pruning, our system has practical execution times with low variance when compared to other state-of-the-art formula search engines. 

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3. Understanding and Mitigating Bias in Online Health Search

   https://doi.org/10.1145/3404835.3462930  

   Hashavit, Anat; Wang, Hongning; Lin, Raz; Stern, Tamar; Kraus, Sarit (July 2021, Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '21))

   null (Ed.)

   Search engines are perceived as a reliable source for general information needs. However, finding the answer to medical questions using search engines can be challenging for an ordinary user. Content can be biased and results may present different opinions. In addition, interpreting medically related content can be difficult for users with no medical background. All of these can lead users to incorrect conclusions regarding health related questions. In this work we address this problem from two perspectives. First, to gain insight on users' ability to correctly answer medical questions using search engines, we conduct a comprehensive user study. We show that for questions regarding medical treatment effectiveness, participants struggle to find the correct answer and are prone to overestimating treatment effectiveness. We analyze participants' demographic traits according to age and education level and show that this problem persists in all demographic groups. We then propose a semi-automatic machine learning approach to find the correct answer to queries on medical treatment effectiveness as it is viewed by the medical community. The model relies on the opinions presented in medical papers related to the queries, as well as features representing their impact. We show that, compared to human behaviour, our method is less prone to bias. We compare various configurations of our inference model and a baseline method that determines treatment effectiveness based solely on the opinion of medical papers. The results bolster our confidence that our approach can pave the way to developing automatic bias-free tools that can help mediate complex health related content to users. 

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4. Leveraging Schema Labels to Enhance Dataset Search

   https://doi.org/10.1007/978-3-030-45439-5_18  

   Chen, Zhiyu; Jia, Haiyan; Heflin, Jeff; Davison, Brian D. (January 2020, 42nd European Conference on Information Retrieval, LNCS)

   A search engine's ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text. We propose a novel schema label generation model which generates possible schema labels based on dataset table content. We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels. To evaluate our method on real-world datasets, we create a new benchmark specifically for the dataset retrieval task. Experiments show that our approach can effectively improve the precision and NDCG scores of the dataset retrieval task compared with baseline methods. We also test on a collection of Wikipedia tables to show that the features generated from schema labels can improve the unsupervised and supervised web table retrieval task as well. 

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5. A Web Service for Author Name Disambiguation in Scholarly Databases

   Kunho Kim, Athar Sefid (July 2018, Web/online services)

   Author Name Disambiguation (AND) is the task of clustering unique author names from publication records in scholarly or related databases. Although AND has been extensively studied and has served as an important preprocessing step for several tasks (e.g. calculating bibliometrics and scientometrics for authors), there are few publicly available tools for disambiguation in large-scale scholarly databases. Furthermore, most of the disambiguated data is embedded within the search engines of the scholarly databases, and existing application programming interfaces (APIs) have limited features and are often unavailable for users for various reasons. This makes it difficult for researchers and developers to use the data for various applications (e.g. author search) or research. Here, we design a novel, web-based, RESTful API for searching disambiguated authors, using the PubMed database as a sample application. We offer two type of queries, attribute-based queries and record-based queries which serve different purposes. Attribute-based queries retrieve authors with the attributes available in the database. We study different search engines to find the most appropriate one for processing attribute-based queries. Record-based queries retrieve authors that are most likely to have written a query publication provided by a user. To accelerate record-based queries, we develop a novel algorithm that has a fast record-to-cluster match. We show that our algorithm can accelerate the query by a factor of 4.01 compared to a baseline naive approach. 

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