Verification and Validation of Semantic Annotations

In this paper, we propose a framework to perform verification and validation of semantically annotated data. The annotations, extracted from websites, are verified against the schema.org vocabulary and Domain Specifications to ensure the syntactic correctness and completeness of the annotations. The Domain Specifications allow checking the compliance of annotations against corresponding domain-specific constraints. The validation mechanism will detect errors and inconsistencies between the content of the analyzed schema.org annotations and the content of the web pages where the annotations were found.Comment: Accepted for the A.P. Ershov Informatics Conference 2019(the PSI Conference Series, 12th edition) proceedin

Top 10 Law School Home Pages of 2011

For the third consecutive year, the website home pages for all ABA-accredited law schools are evaluated and ranked based on objective criteria. For 2011, law school home pages advanced in some areas. For instance, there are now thirteen sites using the HTML5 doctype, up from a single site in 2010. In addition, seventeen schools achieved a perfect score for three tests focused on website accessibility, up from eight in 2010. Nonetheless, there’s enough diversity in coding practices and content to help separate the great from the good. For this year’s survey, twenty-four elements of each home page are assessed across three broad categories: Design Patterns & Metadata; Accessibility & Validation; and Marketing & Communications. Most elements require no special design skills, sophisticated technology or significant expenses. For interpreting these results, the author does not try to decide if any whole is greater or less than the sum of its parts

White Hat Search Engine Optimization (SEO): Structured Web Data for Libraries

“White hat” search engine optimization refers to the practice of publishing web pages that are useful to humans, while enabling search engines and web applications to better understand the structure and content of your website. This article teaches you to add structured data to your website so that search engines can more easily connect patrons to your library locations, hours, and contact information. A web page for a branch of the Greater Sudbury Public Library retrieved in January 2015 is used as the basis for examples that progressively enhance the page with structured data. Finally, some of the advantages structured data enables beyond search engine optimization are explored

Complete LibTech 2013 Print Program

PDF of the complete print program from the 2013 Library Technology Conferenc

Schema2QA: High-Quality and Low-Cost Q&A Agents for the Structured Web

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few annotations for each field. The key concept is to cover the space of possible compound queries on the database with a large number of in-domain questions synthesized with the help of a corpus of generic query templates. The synthesized data and a small paraphrase set are used to train a novel neural network based on the BERT pretrained model. We use Schema2QA to generate Q&A systems for five Schema.org domains, restaurants, people, movies, books and music, and obtain an overall accuracy between 64% and 75% on crowdsourced questions for these domains. Once annotations and paraphrases are obtained for a Schema.org schema, no additional manual effort is needed to create a Q&A agent for any website that uses the same schema. Furthermore, we demonstrate that learning can be transferred from the restaurant to the hotel domain, obtaining a 64% accuracy on crowdsourced questions with no manual effort. Schema2QA achieves an accuracy of 60% on popular restaurant questions that can be answered using Schema.org. Its performance is comparable to Google Assistant, 7% lower than Siri, and 15% higher than Alexa. It outperforms all these assistants by at least 18% on more complex, long-tail questions