Just-in-time information extraction using extraction views

El-Helw, Amr; Farid, Mina; Ilyas, Ihab F.

doi:10.1145/2213836.2213913

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…We consider the SQOUT system as an orthogonal optimization for a specific join scenario that might further speed up query processing for NLP tasks in a RDBMS. Authors of [23] propose another join optimization and join selection strategy, while authors of [13] discuss optimization strategies for main memory databases and Hadoop clusters. Finally, authors of GRAFT [8] propose For the task of domain dependent relation extraction, we abstract document and language adaptation as a linear process and domain adaptation as an interactive and iterative process.…”

Section: Declarative Relation Extractionmentioning

confidence: 99%

INDREX: In-database relation extraction

Kilias

Löser

Andritsos

2015

Information Systems

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Section: Declarative Relation Extractionmentioning

confidence: 99%

INDREX: In-database relation extraction

Kilias

Löser

Andritsos

2015

Information Systems

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“…We consider the SQOUT system as an orthogonal optimization for a specific join scenario that might further speed up query processing for NLP tasks in a RDBMS. Authors of [14] propose another join optimization and join selection strategy, while authors of [10] discuss optimization strategies for main memory data bases and Hadoop clusters. Finally, authors of GRAFT [7] propose another set of scoring-based optimization strategies in the presence of an index.…”

Section: Declarative Relation Extractionmentioning

confidence: 99%

Indrex

Kilias

Löser

Andritsos

2013

Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP

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Relation extraction transforms the textual representation of a relationship into the relational model of a data warehouse. Early systems, such as SystemT by IBM or the open source system GATE solve this task with handcrafted rule sets that the system executes document-by-document. Thereby the user must execute a highly interactive and iterative process of reading a document, of expressing rules, of testing these rules on the next document and of refining rules. Until now, these systems do neither leverage the full potential of built-in declarative query languages nor the indexing and query optimization techniques of a modern RDBMS that would enable a user interactive rule refinement across documents and on the entire corpus. We propose the INDREX system that enables a user for the first time to describe corpus-wide extraction tasks in a declarative language and permits the user to run interactive rule refinement queries. For enabling this powerful functionality we extend a standard PostgreSQL with a set of white-box user-definedfunctions that enable corpus-wide transformations from sentences into relationships . We store the text corpus and rules in the same RDBMS that already holds domain specific structured data. As a result, (1) the user can leverage this data to further adapt rules to the target domain, (2) the user does not need an additional system for rule extraction and (3) the INDREX system can leverage the full power of built-in indexing and query optimization techniques of the underlaying RDBMS. In a preliminary study we report on the feasibility of this disruptive approach and show multiple queries in INDREX on the Reuters Corpus, Volume 1.

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“…Recent studies, however, indicate that accurately annotating the entities of a concept in a large collection requires developing, deploying, and maintaining complex pieces of software, manual labor, and/or collecting training data, which may take a long time and substantial amount of computational and financial resources [Chiticariu et al 2010;Anderson et al 2013;Elhelw et al 2012;Doan et al 2009;Finin et al 2010]. Given a concept, developers have to design and write a program called an annotator or extractor that finds and annotates all instances of the concept in the collection.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, users have to wait a long time for the execution of extraction programs before they have a fully annotated collection. The long delays to execute extraction programs and to create and/or update fully annotated collections are well recognized as an issue in concept extraction [Shen et al 2008;Gulhane et al 2011;Elhelw et al 2012;Doan et al 2009]. They are particularly problematic in domains with urgent information needs [Jain et al 2008a;Shen et al 2008;Elhelw et al 2012].…”

Section: Introductionmentioning

confidence: 99%

“…The long delays to execute extraction programs and to create and/or update fully annotated collections are well recognized as an issue in concept extraction [Shen et al 2008;Gulhane et al 2011;Elhelw et al 2012;Doan et al 2009]. They are particularly problematic in domains with urgent information needs [Jain et al 2008a;Shen et al 2008;Elhelw et al 2012]. For example, an FBI agent who needs to query the evolving content of Web sites and social media pages to find and respond to new activities in human trafficking, a stock analyst who has to respond to changes in the stock market in a timely fashion, and an epidemiologist who must act quickly to control the spread of an infectious disease cannot afford to wait for all annotation programs to be (re-)executed [Shen et al 2008].…”

Section: Introductionmentioning

confidence: 99%

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Cost-Effective Conceptual Design for Information Extraction

Termehchy

Vakilian

Chodpathumwan

et al. 2015

ACM Trans. Database Syst.

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It is well established that extracting and annotating occurrences of entities in a collection of unstructured text documents with their concepts improves the effectiveness of answering queries over the collection. However, it is very resource intensive to create and maintain large annotated collections. Since the available resources of an enterprise are limited and/or its users may have urgent information needs, it may have to select only a subset of relevant concepts for extraction and annotation. We call this subset a conceptual design for the annotated collection. In this article, we introduce and formally define the problem of cost-effective conceptual design where, given a collection, a set of relevant concepts, and a fixed budget, one likes to find a conceptual design that most improves the effectiveness of answering queries over the collection. We provide efficient algorithms for special cases of the problem and prove it is generally NP-hard in the number of relevant concepts. We propose three efficient approximations to solve the problem: a greedy algorithm, an approximate popularity maximization (APM for short), and approximate annotation-benefit maximization (AAM for short). We show that, if there are no constraints regrading the overlap of concepts, APM is a fully polynomial time approximation scheme. We also prove that if the relevant concepts are mutually exclusive, the greedy algorithm delivers a constant approximation ratio if the concepts are equally costly, APM has a constant approximation ratio, and AAM is a fully polynomial-time approximation scheme. Our empirical results using a Wikipedia collection and a search engine query log validate the proposed formalization of the problem and show that APM and AAM efficiently compute conceptual designs. They also indicate that, in general, APM delivers the optimal conceptual designs if the relevant concepts are not mutually exclusive. Also, if the relevant concepts are mutually exclusive, the conceptual designs delivered by AAM improve the effectiveness of answering queries over the collection more than the solutions provided by APM.

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Just-in-time information extraction using extraction views

Cited by 5 publications

References 7 publications

INDREX: In-database relation extraction

INDREX: In-database relation extraction

Indrex

Cost-Effective Conceptual Design for Information Extraction

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