Enabling Structured Queries over Unstructured Documents

Abstract

With the information explosion on the internet, finding precise answers efficiently is a prevalent requirement by many users. Today, search engines answer keyword queries with a ranked list of documents. Users might not be always willing to read the top ranked documents in order to satisfy their information need. It would save lots of time and efforts if the the answer to a query can be provided directly, instead of a link to a document which might contain the answer. To realize this functionality, users must be able to define their information needs precisely, e.g., by using structured queries, and, on the other hand, the system must be able to extract information from unstructured text documents to answer these queries.To this end, we introduce a system which supports structured queries over unstructured text documents, aiming at finding structured answers to the users' information need. Our goal is to extract answers from unstructured natural text, by applying various efficient techniques that allow fast query processing over text documents from the web or other heterogeneous sources.A key feature of our approach is that it does not require any upfront integration efforts such as the definition of a common data model or ontology.

Transformation rules for decomposing heterogeneous data into triples

“….,(p ij , (l ijr ; d ijr ), v ijr )} The proposed TRSs for unstructured data are simple and straight forward. Unlike Information Extraction (IE) tool, we translate the unstructured data into the collection of triples without extracting the structure from the data (Grishman, 1997;Doan et al, 2009a;Doan et al, 2009b;Al-Mathami, 1998), because the existing IE tools have the following disadvantages (Kastrati et al, 2011): first, such approaches are costly due to a very large collection of data have high preprocessing cost, second, automatic extraction of structure is a source of uncertainty (Sarma et al, 2009), and third, they consist of out-of-dated version of extracted data already stored in somewhere. Therefore, we have adopted an approach proposed by F. Kastrati et.…”

“…Therefore, an IE based approach is not suitable for data extraction. In this work, we have adopted a just-in-time query processing over a large collection of documents, which are result of a corpus selection procedure (Kastrati et al, 2011). This approach utilizes the functionality of search engine for selecting a relevant document based on the input keywords, and locating the appropriate data segments from a selected document.…”

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

“…A dataspace system copes with the problem of integrating a variety of data based on their structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answer to its users [16,17,30]. The existing works on query processing and query answering paid attention to return top-k answers to the users in the area of dataspace system [6,10,15,18,29,40,42,4,26,22,38,19,34,39,37,33,41]. The motivation behind this work is to focus on "how is information important?"…”

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

“…A dataspace system copes with the problem of integrating a variety of data based on their structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answer to its users [16,17,30]. The existing works on query processing and query answering paid attention to return top-k answers to the users in the area of dataspace system [6,10,15,18,29,40,42,4,26,22,38,19,34,39,37,33,41]. The motivation behind this work is to focus on "how is information important?"…”