Automatic knowledge extraction from documents

Automatic knowledge extraction of any Chatbot from conversation

Biomedical information is available to research and development scientists as unstructured text in the form of scientific manuscripts and reports published in the literature and elsewhere. Scientists focused on specific research programs are burdened with surveying vast numbers of publications and reports to acquire information relevant to their efforts. Employing technology as a research aid provides a mechanism to cope with information overload that characterizes the R&D environment. Text mining can extract knowledge from large corpora of biomedical text and make it available to support scientific research and knowledge collections [1, 2] and intelligent PDF reader tools able to search content and find related articles [3] are available; however, such reader tools are typically desktop applications limited to specific platforms and data sources so they cannot easily support broad based integrated scientific search needs for a dispersed R&D organization with a wide variety of content needs. Our team has developed a web-browser based document reader with a built-in exploration tool and automatic concept extraction from biomedical text content. This provides R&D scientists with a simple tool to aid finding, reading, and exploring documents relevant to focused research objectives. The tool, Shangri-Docs, combines a document reader with automatic concept extraction and highlighting of relevant terms based on carefully selected ontologies combined with our custom corporate enterprise taxonomy. Shangri-Docs provides the ability to evaluate a wide variety of document formats (e.g. PDF, Word, PPT, text, etc.) and exploits the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and privately cataloged databases simultaneously. Shangri-Docs incorporates Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific pathology, disease, drug, and biological terms mentioned in the text. cTAKES was originally designed specifically to extract information from clinical medical records. We have extended cTAKES automatic knowledge extraction process to include the R&D biomedical research domain by improving the ontology guided information extraction process. Shangri-Docs could be adapted to other science fields and further customized across our R&D scientific community via our open source, cloud-based, data management system. [1] Funk, et.al., BMC Bioinformatics 2014, 15:59 doi:10.1186/1471-2105-15-59 [2] Kang et al., BMC Bioinformatics 2014, 15:64 doi:10.1186/1471-2105-15-64 [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org Citation Format: Chris Mattmann, Lauren Intagliata, Selina Chu, Garth McGrath, Giuseppe Totaro, Daniel Civello, David Ballard, Jeffrey Long, Nipurn Doshi, Shivika Thapar, Michael Livstone, Paul Ramirez, Maureen Cronin. Shangri-Docs: a browser based tool for document exploration and automatic knowledge extraction from unstructured biomedical text. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5283.

In this paper, a new approach based on Differential Evolution for the automatic classification of items in medical databases is proposed. Based on it, a tool called DERExis presented, which automatically extracts explicit knowledge from the database under the form of IF -- THEN rules. DERExis thought as a useful support to decision making whenever explanations on why an item is assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The tool has been compared over seven medical databases against a set of fifteen classification tools widely used in literature. The results have proven the effectiveness of the proposed approach, since DEREx turns out to be among the very best tools in terms of highest classification accuracy, so it is preferable because it automatically extracts knowledge and provides users with it under an easily comprehensible form.

Automatic extraction of knowledge from large corpus of texts is an essential step toward linguistic knowledge acquisition in the medical domain. The current situation shows a lack of computer-readable large medical lexicons, with a partial exception for the English language. Moreover, multilingual lexicons with versatility for multiple languages applications are far from reach as long as only manual extraction is considered. Computer-assisted linguistic knowledge acquisition is a must.

An Expert System for the Analysis and Interpretation of Evoked Potentials Based on Fuzzy Classification: Application to Brainstem Auditory Evoked Potentials

Ensemble methods are state of the art for many NLP tasks. Recent work by Banko and Brill (2001) suggests that this would not necessarily be true if very large training corpora were available. However, their results are limited by the simplicity of their evaluation task and individual classifiers.Our work explores ensemble efficacy for the more complex task of automatic thesaurus extraction on up to 300 million words. We examine our conflicting results in terms of the constraints on, and complexity of, different contextual representations, which contribute to the sparseness-and noise-induced bias behaviour of NLP systems on very large corpora.

Interactive improvement of decision trees through flaw analysis and interpretation

Question Answering (QA) systems aim at supplying precise answers to questions, posed by users in a natural language form. They are used in a wide range of application areas, from bio-medicine to tourism. Their underlying knowledge source can be structured data (e.g. RDF graphs and SQL databases), unstructured data in the form of plain text (e.g. textual excerpts from Wikipedia), or combinations of the above. In this paper we survey the recent work that has been done in the area of stateless QA systems with emphasis on methods that have been applied in RDF and Linked Data, documents, and mixtures of these. We identify the main challenges, we categorize the existing approaches according to various aspects, we review 21 recent systems, and 23 evaluation and training datasets that are most commonly used in the literature categorized according to the type of the domain, the underlying knowledge source, the provided tasks, and the associated evaluation metrics.

In our paper, we address the problem of recognition of irrelevant phrases in terminology lists obtained with an automatic term extraction tool. We focus on identification of multi-word phrases that are general terms or discourse expressions. We defined several methods based on comparison of domain corpora and a method based on contexts of phrases identified in a large corpus of general language. The methods were tested on Polish data. We used six domain corpora and one general corpus. Two test sets were prepared to evaluate the methods. The first one consisted of many presumably irrelevant phrases, as we selected phrases which occurred in at least three domain corpora. The second set mainly consisted of domain terms, as it was composed of the top-ranked phrases automatically extracted from the analyzed domain corpora. The results show that the task is quite hard as the inter-annotator agreement is low. Several tested methods achieved similar overall results, although the phrase ordering varied between methods. The most successful method, with a precision of about 0.75 on half of the tested list, was the context based method using a modified contextual diversity coefficient. Although the methods were tested on Polish, they seems to be language independent.

Autonomous on-line knowledge discovery and acquisition play an important role in cognitive informatics, cognitive computing, knowledge engineering, and computational intelligence. On the basis of the latest advances in cognitive informatics and denotational mathematics, this paper develops a web knowledge discovery engine for web document restructuring and comprehension, which decodes on-line knowledge represented in informal documents into cognitive knowledge represented by concept algebra and concept networks. A visualized concept network explorer and a semantic analyzer are implemented to capture and refine queries based on concept algebra. A graphical interface is built using concept and semantic models to refine users’ queries. To enable autonomous information restructuring by machines, a two-level knowledge base that mimics human lexical/syntactical and semantic cognition is introduced. The information restructuring model provides a foundation for automatic concept indexing and knowledge extraction from web documents. The web knowledge discovery engine extends machine learning capability from imperative and adaptive information processing to autonomous and cognitive knowledge processing with unstructured documents in natural languages.

The issue of improving quality, costs and delays indicators in design and manufacturing is more relevant than ever in the industry. After lean manufacturing, well known in production process, the lean engineering approach is being implemented today in the field of design, taking the name of lean product development. The management of knowledge and know-how (existing, new or to be acquired) is the heart of lean engineering. In our suggested methodology this is implemented through a new generation of tools called Knowledge Configuration Management (KCM) and Knowledge Extraction Assistant (KEA). KCM tools are lean engineering components that provide analytical approach to knowledge management and knowledge-based engineering. These tools require a highly integrated approach that involves, for example, predefined geometrical parametric 3D models, such as CAD templates. But this approach cannot be deployed in all engineering sites. We propose to complete this KCM approach introducing a semantic network approach, coupling with Feature Identity Card (FIC). FIC contains a set of metadata and information existing in the Product Data Management (PDM), connected with information extracted from 3D CAD (Computer Aided Design) models. It allows contextualizing information and ensures semantic connections, in order to manipulate the right parameters with mathematical algorithms. Those algorithms will search candidate relationships between design parameters extracted from CAD models. Our suggested approach aims at extracting knowledge in cases where design never came out of Knowledge Based Engineering (KBE) applications. In those situations, it seems important to complete classical knowledge management approach, and to find out the implicit knowledge embedded in 3D CAD models. This is achieved through a global approach, focusing on the product’s 3D definitions. We suggest introducing the latter approach by a suite of digital KEA tools (interfaced with KCM tools). Extracting knowledge from projects information stored in the Product Data Management does this. More precisely, the methodology is based on a commercial 3D similarity search tools for CAD models and on mathematical algorithms that search relationships between extracted design parameters. The goal is to submit new rules to the process and design experts. Implementing this methodology, a deeper knowledge of the product and its associated process can be acquired. This ensures a more productive and efficient design process.

The explosive increase in Internet usage has attracted technologies for automatically mining the user-generated contents (UGC) from Web documents. These UGC-rich resources have raised new opportunities and challenges to carry out the opinion extraction and mining tasks for opinion summaries. The technology of opinion extraction allows users to retrieve and analyze people’s opinions scattered over Web documents. Opinion mining is a process which is concerned with the opinions generated by the consumers about the product. Opinion Mining aims at understanding, extraction and classification of opinions scattered in unstructured text of online resources. The search engines performs well when one wants to know about any product before purchase, but the filtering and analysis of search results often complex and time-consuming. This generated the need of intelligent technologies which could process these unstructured online text documents through automatic classification, concept recognition, text summarization, etc. These tools are based on traditional natural language techniques, statistical analysis, and machine learning techniques. Automatic knowledge extraction over large text collections like Internet has been a challenging task due to many constraints such as needs of large annotated training data, requirement of extensive manual processing of data, and huge amount of domain-specific terms. Ambient Intelligence (AmI) in wed-enabled technologies supports and promotes the intelligent e-commerce services to enable the provision of personalized, self-configurable, and intuitive applications for facilitating UGC knowledge for buying confidence. In this chapter, we will discuss various approaches of Opinion Mining which combines Ambient Intelligence, Natural Language Processing and Machine Learning methods based on textual and grammatical clues.

Explicit schematic information in knowledge representation and acquisition

Automatic extraction of semantic information, if successful, offers to languages with little or poor resources, the prospects of creating ontological resources inexpensively, thus providing support for common-sense reasoning applications in those languages. In this paper we explore the automatic extraction of synonymy information from large corpora using two complementary techniques: a generic broad-coverage parser for generation of bits of semantic information, and their synthesis into sets of synonyms using automatic sense-disambiguation. To validate the quality of the synonymy information thus extracted, we experiment with English, where appropriate semantic resources are already available. We cull synonymy information from a large corpus and compare it against synonymy information available in several standard sources. We present the results of our methodology, both quantitatively and qualitatively, that indicate good quality synonymy information may be extracted automatically from large corpora using the proposed methodology.

This work first provides a literature review of product development and knowledge management including product development process, design history and domain knowledge. It then presents a method for the knowledge-based multi-view process modeling including process implementation, process monitoring, and knowledge management. The integrated framework and hierarchical model of the design history is built. The relationship among process history, design intent and domain knowledge is analyzed, and the method for acquisition and management of process history, design intent and domain knowledge is presented. The functional modules of knowledge-based PDPM system are described, and the architecture of integrated knowledge management (IKM) system based on PDPM is set up and developed with a B/C/S structure. The system is used successfully during the life cycle of a new type of railway rolling stock development in a Chinese enterprise.