This study present a semi-automated data-driven approach to developing a semantic network that aligns well with the top-level information structure in clinical research eligibility criteria text and demonstrates the feasibility of using the resulting semantic role labels to generate semistructured eligibility criteria with nearly perfect interrater reliability.
Nowadays, huge volumes of data are organized or exported in a tree-structured form. Querying capabilities are provided through queries that are based on branching path expression. Even for a single knowledge domain structural differences raise difficulties for querying data sources in a uniform way. In this paper, we present a method for semantically querying tree-structured data sources using partially specified tree patterns. Based on dimensions which are sets of semantically related nodes in tree structures, we define dimension graphs. Dimension graphs can be automatically extracted from trees and abstract their structural information. They are semantically rich constructs that support the formulation of queries and their efficient evaluation. We design a tree-pattern query language to query multiple treestructured data sources. A central feature of this language is that the structure can be specified fully, partially, or not at all in the queries. Therefore, it can be used to query multiple trees with structural differences. We study the derivation of structural expressions in queries by introducing a set of inference rules for structural expressions. We define two types of query unsatisfiability and we provide necessary and sufficient conditions for checking each of them. Our approach is validated through experimental evaluation.
Answering queries using views is a well-established technique in databases. In this context, two outstanding problems can be formulated. The first one consists in deciding whether a query can be answered exclusively using one or multiple materialized views. Given the many alternative ways to compute the query from the materialized views, the second problem consists in finding the best way to compute the query from the materialized views. In the realm of XML, there is a restricted number of contributions in the direction of these problems due to the many limitations associated with the use of materialized views in traditional XML query evaluation models. In this paper, we adopt a recent evaluation model, called inverted lists model, and holistic algorithms which together have been established as the prominent technique for evaluating queries on large persistent XML data, and we address the previous two problems. This new context revises these problems since it requires new conditions for view usability and new techniques for computing queries from materialized views. We suggest an original approach for materializing views which stores for every view node only the list of XML nodes necessary for computing the answer of the view. We specify necessary and sufficient conditions for answering a treepattern query using one or multiple materialized views in terms of homomorphisms from the views to the query. In order to efficiently answer queries using materialized views, we design a stack-based algorithm which compactly encodes in polynomial time and space all the homomorphisms from a view to a query. We further propose space and time optimizations by using bitmaps to encode view materializations and by employing bitwise operations to minimize the evaluation cost of the queries. Finally, we conducted an extensive experimentation which demonstrates that our approach yields impressive query hit rates in the view pool, achieves significant time and space savings and shows smooth scalability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.