Automatic extraction of function–behaviour–state information from patents

Transforming unstructured or semi-structured information into structured knowledge is one of the big challenges of today’s knowledge society. While this abstract goal is still unreached and probably unreachable, intelligent information extraction techniques are considered key ingredients on the way to generating and representing knowledge for a wide variety of applications. This is especially true for the current efforts to turn the World Wide Web being the world’s largest collection of information into the world’s largest knowledge base. This introduction gives a broad overview about the major topics and current trends in information extraction.

Recent advances in information extraction have led to huge knowledge bases (KBs), which capture knowledge in a machine-readable format. Inductive logic programming (ILP) can be used to mine logical rules from these KBs, such as If two persons are married, then they (usually) live in the same city. While ILP is a mature field, mining logical rules from KBs is difficult, because KBs make an open-world assumption. This means that absent information cannot be taken as counterexamples. Our approach AMIE (Galarraga et al. in WWW, 2013) has shown how rules can be mined effectively from KBs even in the absence of counterexamples. In this paper, we show how this approach can be optimized to mine even larger KBs with more than 12M statements. Extensive experiments show how our new approach, AMIE$$+$$+, extends to areas of mining that were previously beyond reach.

Neural network language models, such as BERT, can be used for information extraction from medical texts with unstructured free text. These models can be pre-trained on a large corpus to learn the language and characteristics of the relevant domain and then fine-tuned with labeled data for a specific task. We propose a pipeline using human-in-the-loop labeling to create annotated data for Estonian healthcare information extraction. This method is particularly useful for low-resource languages and is more accessible to those in the medical field than rule-based methods like regular expressions.

Recognizing and extracting meaningful information from Web unstructured documents, taking into account their semantics, is an important problem of information and knowledge management. This paper describes H\(\imath\)LeX, a system implementing a novel logic-based approach to information extraction from unstructured documents. The approach adopted in the H\(\imath\)LeX system is founded on a new two-dimensional representation of documents, and heavily exploits DLP + - an extension of disjunctive logic programming for ontology representation and reasoning, which has been recently implemented on top of the DLV reasoning environment. Unlike previous systems, which are mainly syntactic, H\(\imath\)LeX combines both semantic and syntactic knowledge for a powerful information extraction. Ontologies, representing the semantics of information to be extracted, are encoded in DLP + , while the extraction patterns are expressed using regular expressions and an ad hoc two-dimensional grammar. The execution of DLP + reasoning modules, encoding the grammar expressions, yields the actual extraction of information from the input document. H\(\imath\)LeX allows the semantic information extraction from both HTML pages and flat text documents by using synthetic and very expressive extraction patterns.

Machine-readable semantic knowledge in the form of taxonomies (i.e., a collection of is-a edges) has proved to be beneficial in an array of NLP tasks including inference, textual entailment, question answering and information extraction. Such widespread utility of taxonomies has led to multiple large-scale manual efforts towards taxonomy induction such as WordNet and Cyc. However, manual construction of taxonomies is time-intensive, and usually, requires substantial annotation efforts by domain experts. Furthermore, the resulting taxonomies suffer from low coverage and are unavailable for specific domains or languages. Therefore, in recent years, there has been a growing body of work, which aims to induce taxonomies automatically, either from semi-structured knowledge resources or unstructured text. In this thesis, we focus on the task of automated taxonomy induction under a variety of different settings. We first focus on taxonomy induction from the largest semi-structured knowledge resource, i.e., Wikipedia. More specifically, we introduce a set of novel heuristics aimed towards inducing a large-scale taxonomy from the English Wikipedia categories network. We also propose a novel comprehensive path-based evaluation framework for taxonomies. Taxonomy induced using our approach significantly outperforms the state of the art across both edge-based as well as path-based evaluation metrics. Moreover, our experiments also demonstrate that high accuracy in edge-based evaluation metrics does not always translate to high-accuracy in path-based evaluation metrics. Subsequently, we propose a novel approach, which leverages the interlanguage links of Wikipedia to induce taxonomies in other Wikipedia languages. Compared to the state of the art, our approach is simpler, more principled, and results in taxonomies that are significantly more accurate across both edge-based and path-based evaluation metrics. In the second part of this thesis, we focus on taxonomy induction from unstructured text. We propose a novel approach towards taxonomy induction from an input vocabulary of seed terms. Unlike previous approaches, which extract singular hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract long-range hypernym subsequences. Taxonomy induction from the extracted subsequences is cast as an instance of the minimum-cost flow optimization problem on a carefully-designed flow network. Through experiments, we demonstrate that our approach outperforms the state of the art across four languages. We also show that our approach is robust to the presence of noise in the input vocabulary. Our approach facilitates the relaxation of many simplifying assumptions, which were employed by previous taxonomy induction approaches. As a result, our work serves to automate the process of taxonomy induction from unstructured text in the true sense. Finally, we introduce a task of discovering and generalizing lexicalized templates from the titles of Wikipedia entities. The experimental results on this task demonstrate that taxonomies, which perform better on our proposed path-based metrics, result in a more accurate set of generalizations for a given set of entities. In summary, this thesis proposes new approaches towards automated taxonomy induction. It improves upon the state of the art in a variety of different settings. It also relaxes many simplifying assumptions that limited the applicability of prior approaches.

Design patterns support extraction of design information for better program understanding, reusability and reengineering. With the advent of contemporary applications, the extraction of design information has become quite complex and challenging. These applications are multilingual in nature i.e. their design information is spread across various language components that are interlinked with each other. At present, no approach is available that is capable to extract design information of multilingual applications by using design patterns. This paper lays foundation for the analysis of multilingual source code for the detection of J2EE Patterns. J2EE Patterns provide design solutions for effective enterprise applications. A novel approach is presented for the detection of J2EE Patterns from multilingual source code of J2EE applications. For this purpose, customizable and reusable feature types are presented as definitions of J2EE Patterns catalogue. A prototype implementation is evaluated on a corpus that contains the repository of multilingual source code of J2EE Patterns. Additionally, the tool is tested on open source applications. The accuracy of the tool is validated by successfully recognizing J2EE Patterns from the multilingual source code. The results demonstrate the significance of customizable definitions of J2EE Pattern’s catalogue and capability of prototype.

In more and more application areas large collections of digitized multimedia information are gathered and have to be maintained (e.g. in tourism, medicine, etc). Therefore, there is an increasing demand for tools and techniques supporting the management and usage of digital multimedia data. Furthermore, new large collections of data are available through it every day. In this paper we are presenting Tourist Face, a system aimed at integrating text analyzing techniques into the paradigm of multimedia information, specifically tourist multimedia information.Particularly relevant components to its the development are Freebase, a large collaborative base of knowledge, and General Architecture for Text Engineering (GATE), a system for text processing. The platform architecture has been built thinking in terms of scalability, with the following objectives: to allow the integration of different natural language processing techniques, to expand the sources from which information extraction can be performed and to ease integration of new user interfaces.KeywordsInformation ExtractionMultimedia DataText ProcessingScalable ArchitectureFreebaseGATE

Free software and open source databases have become a serious alternative to commercial databases. This article states common points and differences between the open source and free software movements and briefly describes the most relevant licenses and database projects.

Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks.Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files.The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states.

The authors have examined the process of tax accounting digitalization in Ukraine. Current trends in the implementation of digital technologies in the field of tax accounting and administration have been analyzed. The key issues that hinder the effective development of digital solutions have been identified. The study highlights the role of information systems, electronic document management, and cloud services in enhancing the transparency, accuracy, and efficiency of tax accounting. The main challenges of tax accounting digitalization have been outlined. Among them are significant initial investments required for the implementation of digital technologies, ongoing costs for maintaining digital systems, weak integration of information systems, outdated technical infrastructure, risks of confidential data leakage, low digital literacy of accounting professionals, and the need to update the regulatory framework. These factors complicate the digital transformation process.Several solutions to address these challenges have been proposed, including the use of free basic software and educational resources, the implementation of phased data migration with parallel record-keeping, and the introduction of system certification for compliance with security standards along with continuous security auditing. Positive developments have also been observed. The electronic services of the State Tax Service are expanding, electronic reporting is improving, and new platforms for automated interaction between businesses and tax authorities are emerging. Ukraine is gradually adopting international experience in digitalization, which creates opportunities for establishing a unified tax ecosystem with a high level of automation and control. Future research should focus on assessing the effectiveness of tax accounting digitalization and analyzing the impact of automation on the quality of tax administration. It is also necessary to study the social, economic, and ethical aspects of artificial intelligence application in the tax domain. Promising directions include the harmonization of legislation, the development of cybersecurity measures, and the improvement of personnel training and requalification to meet the demands of the digital economy.Overall, the digitalization of tax accounting under current conditions serves as a strategic factor in enhancing economic competitiveness, strengthening tax discipline, and ensuring the sustainable development of the national economy.

In this work, we demonstrate structured search capabilities of the GYANI indexing infrastructure. GYANI allows linguists, journalists, and scholars in humanities to search large semantically annotated document collections in a structured manner by supporting queries with regular expressions between word sequences and annotations. In addition to this, we provide support for attaching semantics to words via annotations in the form of part-of-speech, named entities, temporal expressions, and numerical quantities. We demonstrate that by enabling such structured search capabilities we can quickly gather annotated text regions for various knowledge-centric tasks such as information extraction and question answering.

This paper proposes an information extraction model that identifies text patterns representing relations between two entities. It is proposed that, given a set of entity pairs representing a specific relation, it is possible to find text patterns representing such relation within sentences from documents containing those entites. After those text patterns are identified, it is possible to attempt the extraction of a complementary entity, considering the first entity of the relation and the related text patterns are provided. The pattern selection relies on regular expressions, frequency and identification of less relevant words. Modern search engines APIs and HTML parsers are used to retrieve and parse web pages in real time, eliminating the need of a pre-established corpus. The retrieval of document counts within a timeframe is also used to aid in the selection of the entities extracted.

Information extraction is concerned with applying natural language processing to automatically extract the essential details from text documents. A great disadvantage of current approaches is their intrinsic dependence to the application domain and the target language. Several machine learning techniques have been applied in order to facilitate the portability of the information extraction systems. This paper describes a general method for building an information extraction system using regular expressions along with supervised learning algorithms. In this method, the extraction decisions are lead by a set of classifiers instead of sophisticated linguistic analyses. The paper also shows a system called TOPO that allows to extract the information related with natural disasters from newspaper articles in Spanish language. Experimental results of this system indicate that the proposed method can be a practical solution for building information extraction systems reaching an F-measure as high as 72%.KeywordsNatural Language ProcessingRegular ExpressionInformation ExtractionNews ReportMachine Learn ApproachThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As engineering systems grow more intricate and technological progress accelerates, traditional sources of design knowledge, such as historical data and expert intuition, struggle to keep pace with the complexity and the speed of knowledge generation. To address this challenge, additional sources of knowledge are necessary, particularly for designing unprecedented engineering systems lacking any design heritage. One promising approach involves analyzing optimized designs to extract valuable insights, enabling designers to break away from incremental improvements over existing designs. This article explores the extraction of design information from optimized designs in power flow systems using various classification machine learning methods, empowering designers to make informed decisions in future design endeavors. This design information can also serve as a foundation for synthesizing engineering system configurations that are more complex than those previously encountered. This approach offers several advantages over traditional methods, including its applicability in the absence of design heritage and its ability to provide normative guidance for system design. This article focuses on power flow systems that can be modeled as graphs with a tree structure, with the case study being multisplit fluid-based thermal management systems. The article presents four case studies demonstrating the effectiveness of using information from optimized designs to enhance the design of complex thermal management systems, in both human-directed and automated design processes. The results show that information extraction significantly improves the design process, with less than 1 percent error in approximating the true optimal configuration. This approach eliminates the need for solving complex control problems, leading to reduced computation costs.

The construction, development and maintenance of an economically, environmentally and socially sustainable campus involves the integration of measuring tools and technical information that invites and encourages the community to know the actual state to generate positive actions for reducing the negative impacts over the local environment. At the Universidad Nacional de Colombia - Campus Bogotá, a public area with daily traffic of more than 25000 people, the Environmental Management Bureau has committed with the monitoring of the noise pollution and air quality, as support to the campaigns aiming to reduce the pollutant emissions associated to the student’s activities and campus operation. The target of this study is based in the implementation of mobile air quality and sonometry monitoring equipment, the mapping of the actual air quality and noise pollution inside the university campus as a novel methodology for a sub-area inside a megacity. This results and mapping are proposed as planning tool for the institution administrative sections. A mobile Kunak® Air & OPC air monitoring station with the capability to measure particulate matter PM10, PM25, Ozone (O3), Sulfur Oxide (SO2), Carbon Monoxide (CO) and Nitrogen Oxide (NO2) as well as Temperature, Relative Humidity and Latitude and Longitude coordinates for the data georeferenciation; and a sonometer Cirrus® 162B Class 2 were used to perform the measurements. The measurements took place in conditions of academic activity and without it, with the aim of identify the impacts generated by the campus operation. Using the free code geographical information software QGIS® 2.18, the maps of each variable measured were developed, and the impacts generated by the operation of the campus were identified qualitative and quantitively. For the measured variables, an increase of around 21% for the LAeq noise level and around 80% to 90% for air pollution were detected during the operation period.