Ripple-down rules based open information extraction for the web documents

The World Wide Web contains a massive amount of information in unstructured natural language and obtaining valuable information from informally written Web documents is a major research challenge. One research focus is Open Information Extraction (OIE) aimed at developing relation-independent information extraction. Open Information Extraction systems seek to extract all potential relations from the text rather than extracting few pre-defined relations. Previous machine learning-based Open Information Extraction systems require large volumes of labelled training examples and have trouble handling NLP tools errors caused by Web s informality. These systems used self-supervised learning that generates a labelled training dataset automatically using NLP tools with some heuristic rules. As the number of NLP tool errors increase because of the Web s informality, the self-supervised learning-based labelling technique produces noisy label and critical extraction errors. This thesis presents Ripple-Down Rules based Open Information Extraction (RDROIE) an approach to Open Information Extraction that uses Ripple-Down Rules (RDR) incremental learning technique. The key advantages of this approach are that it does not require labelled training dataset and can handle the freer writing style that occurs in Web documents and can correct errors introduced by NLP tools. The RDROIE system, with minimal low-cost rule addition, outperformed previous OIE systems on informal Web documents

Extracting and re-using research data from chemistry e-theses: the SPECTRa-T project

Scientific e-theses are data-rich resources, but much of the information they contain is not readily accessible. For chemistry, the SPECTRa-T project has addressed this problem by developing data-mining techniques to extract experimental data, creating RDF (Resource Description Framework) triples for exposure to sophisticated Semantic Web searches. We used OSCAR3, an Open Source chemistry text-mining tool, to parse and extract data from theses in PDF, and from theses in Office Open XML document format. Theses in PDF suffered data corruption and a loss of formatting that prevented the identification of chemical objects. Theses in .docx yielded semantically rich SciXML that enabled the additional extraction of associated data. Chemical objects were placed in a data repository, and RDF triples deposited in a triplestore. Data-mining from chemistry e-theses is both desirable and feasible; but the use of PDF, the de facto format standard for deposit in most repositories, prevents the optimal extraction of data for semantic querying. In order to facilitate this, we recommend that universities also require deposition of chemistry e-theses in an XML document format. Further work is required to clarify the complex IPR issues and ensure that they do not become an unwarranted barrier to data extraction and re-use

A New Open Information Extraction System Using Sentence Difficulty Estimation

The World Wide Web has a considerable amount of information expressed using natural language. While unstructured text is often difficult for machines to understand, Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract assertions directly from massive and heterogeneous corpora. Allocation of low-cost computational resources is a main demand for Open Relation Extraction (ORE) systems. A large number of ORE methods have been proposed recently, covering a wide range of NLP tools, from ``shallow'' (e.g., part-of-speech tagging) to ``deep'' (e.g., semantic role labeling). There is a trade-off between NLP tools depth versus efficiency (computational cost) of ORE systems. This paper describes a novel approach called Sentence Difficulty Estimator for Open Information Extraction (SDE-OIE) for automatic estimation of relation extraction difficulty by developing some difficulty classifiers. These classifiers dedicate the input sentence to an appropriate OIE extractor in order to decrease the overall computational cost. Our evaluations show that an intelligent selection of a proper depth of ORE systems has a significant improvement on the effectiveness and scalability of SDE-OIE. It avoids wasting resources and achieves almost the same performance as its constituent deep extractor in a more reasonable time

KnowText: Auto-generated Knowledge Graphs for custom domain applications

While industrial Knowledge Graphs enable information extraction from massive data volumes creating the backbone of the Semantic Web, the specialised, custom designed knowledge graphs focused on enterprise specific information are an emerging trend. We present “KnowText”, an application that performs automatic generation of custom Knowledge Graphs from unstructured text and enables fast information extraction based on graph visualisation and free text query methods designed for non-specialist users. An OWL ontology automatically extracted from text is linked to the knowledge graph and used as a knowledge base. A basic ontological schema is provided including 16 Classes and Data type Properties. The extracted facts and the OWL ontology can be downloaded and further refined. KnowText is designed for applications in business (CRM, HR, banking). Custom KG can serve for locally managing existing data, often stored as “sensitive” information or proprietary accounts, which are not on open web access. KnowText deploys a custom KG from a collection of text documents and enable fast information extraction based on its graph based visualisation and text based query methods

PIE: an online prediction system for protein–protein interactions from text

Protein–protein interaction (PPI) extraction has been an important research topic in bio-text mining area, since the PPI information is critical for understanding biological processes. However, there are very few open systems available on the Web and most of the systems focus on keyword searching based on predefined PPIs. PIE (Protein Interaction information Extraction system) is a configurable Web service to extract PPIs from literature, including user-provided papers as well as PubMed articles. After providing abstracts or papers, the prediction results are displayed in an easily readable form with essential, yet compact features. The PIE interface supports more features such as PDF file extraction, PubMed search tool and network communication, which are useful for biologists and bio-system developers. The PIE system utilizes natural language processing techniques and machine learning methodologies to predict PPI sentences, which results in high precision performance for Web users. PIE is freely available at http://bi.snu.ac.kr/pie/

Arabic open information extraction system using dependency parsing

Arabic is a Semitic language and one of the most natural languages distinguished by the richness in morphological enunciation and derivation. This special and complex nature makes extracting information from the Arabic language difficult and always needs improvement. Open information extraction systems (OIE) have been emerged and used in different languages, especially in English. However, it has almost not been used for the Arabic language. Accordingly, this paper aims to introduce an OIE system that extracts the relation tuple from Arabic web text, exploiting Arabic dependency parsing and thinking carefully about all possible text relations. Based on clause types' propositions as extractable relations and constituents' grammatical functions, the identities of corresponding clause types are established. The proposed system named Arabic open information extraction(AOIE) can extract highly scalable Arabic text relations while being domain independent. Implementing the proposed system handles the problem using supervised strategies while the system relies on unsupervised extraction strategies. Also, the system has been implemented in several domains to avoid information extraction in a specific field. The results prove that the system achieves high efficiency in extracting clauses from large amounts of text

Distantly Supervised Web Relation Extraction for Knowledge Base Population

Extracting information from Web pages for populating large, cross-domain knowledge bases requires methods which are suitable across domains, do not require manual effort to adapt to new domains, are able to deal with noise, and integrate information extracted from different Web pages. Recent approaches have used existing knowledge bases to learn to extract information with promising results, one of those approaches being distant supervision. Distant supervision is an unsupervised method which uses background information from the Linking Open Data cloud to automatically label sentences with relations to create training data for relation classifiers. In this paper we propose the use of distant supervision for relation extraction from the Web. Although the method is promising, existing approaches are still not suitable for Web extraction as they suffer from three main issues: data sparsity, noise and lexical ambiguity. Our approach reduces the impact of data sparsity by making entity recognition tools more robust across domains and extracting relations across sentence boundaries using unsupervised co- reference resolution methods. We reduce the noise caused by lexical ambiguity by employing statistical methods to strategically select training data. To combine information extracted from multiple sources for populating knowledge bases we present and evaluate several information integration strategies and show that those benefit immensely from additional relation mentions extracted using co-reference resolution, increasing precision by 8%. We further show that strategically selecting training data can increase precision by a further 3%