Ontology-based Information Extraction Research Articles

Enhancing Moroccan legal cases analysis through ontology-based information extraction

The efficient organization of diverse disorder cases within a unified memory necessitates an adaptable representation. This study introduces an ontology-based approach for extracting facts from Moroccan legal cases. Leveraging ontological frameworks, a comprehensive case architecture is established, enabling advanced information extraction. Utilizing rules, patterns, and knowledge modeling harmonizes cases and identifies pervasive legal concepts. Statistical techniques unveil latent entities within complex legal textual discourse. Empirical validation demonstrates proficiency, extracting up to 25 regular entities. The rule-based mechanism achieves an F1-score of 99.5%, highlighting precision, while the statistical extractor achieves an 88.3% F1-score, revealing concealed entities. This work presents an innovative ontology-based paradigm for legal information extraction, contributing to advanced knowledge management in the legal domain.

SUSIE: Pharmaceutical CMC ontology-based information extraction for drug development using machine learning

Automatically extracting information from unstructured text in pharmaceutical documents is important for drug discovery and development. This information can be integrated with structured datasets to ultimately accelerate pharmaceutical product development. To this end, we report an end-to-end information extraction framework based on a custom-built pharmaceutical drug development ontology, a weak supervision framework, contextualization algorithms, and a fine-tuned BioBERT model (adaptation of BERT or Bidirectional Encoder Representations from Transformers for biomedical text). The proposed framework, SUSIE (Schema-based Unsupervised Semantic Information Extraction), was trained on ICH (International Conference on Harmonization) documents to identify important entities and relations from unstructured text and auto-generate knowledge graphs representing crucial information in a structured format. On the entity identification task, the framework achieves a test accuracy and F1-score of 96% and 88%, respectively, on out-of-sample documents. A major contribution of this work is to build an automated, unsupervised information extraction framework around a domain-specific, custom-built pharmaceutical drug development ontology without the need for manual curation of training datasets for specific tasks. The efficacy of the approach was tested on out-of-sample documents including an internal Eli Lilly technical document.

Neural Semantic Parsing of Building Regulations for Compliance Checking

Computerising building regulations to allow reasoning is one of the main challenges in automated compliance checking in the built environment. While there has been a long history of translating regulations manually, in recent years, natural language processing (NLP) has been used to support or automate this task. While rule- and ontology-based information extraction and transformation approaches have achieved accurate translations for narrow domains and specific regulation types, machine learning (ML) promises increased scalability and adaptability to new regulation styles. Since ML usually requires many annotated examples as training data, we take advantage of the long history of building code computerisation and use a corpus of manually translated regulations to train a transformer-based encoder-decoder model. Given a relatively small corpus, the model learns to predict the logical structure and extracts entities and relations reasonably well. While the translation quality is not adequate to fully automate the process, the model shows the potential to serve as an auto-completion system and to identify manually translated regulations that need to be reviewed.

An Ontology-Based Information Extraction System for Residential Land-Use Suitability Analysis

We propose an Ontology-Based Information Extraction (OBIE) system to automate the extraction of the criteria and values applied in Land-Use Suitability Analysis (LUSA) from bylaw and regulation documents related to the geographic area of interest. The results obtained by our proposed LUSA OBIE system (land-use suitability criteria and their values) are presented as an ontology populated with instances of the extracted criteria and property values. This latter output ontology is incorporated into a Multi-Criteria Decision-Making (MCDM) model applied for constructing suitability maps for different kinds of land uses. The resulting maps may be the final desired product or can be incorporated into the cellular automata urban modeling and simulation for predicting future urban growth. A case study has been conducted where the output from LUSA OBIE is applied to help produce a suitability map for the City of Regina, Saskatchewan, to assist in the identification of suitable areas for residential development. A set of Saskatchewan bylaw and regulation documents were downloaded and input to the LUSA OBIE system. We accessed the extracted information using both the populated LUSA ontology and the set of annotated documents. In this regard, the LUSA OBIE system was effective in producing a final suitability map.

Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits.

Improved characterisation of clinical text through ontology-based vocabulary expansion

BackgroundBiomedical ontologies contain a wealth of metadata that constitutes a fundamental infrastructural resource for text mining. For several reasons, redundancies exist in the ontology ecosystem, which lead to the same entities being described by several concepts in the same or similar contexts across several ontologies. While these concepts describe the same entities, they contain different sets of complementary metadata. Linking these definitions to make use of their combined metadata could lead to improved performance in ontology-based information retrieval, extraction, and analysis tasks.ResultsWe develop and present an algorithm that expands the set of labels associated with an ontology class using a combination of strict lexical matching and cross-ontology reasoner-enabled equivalency queries. Across all disease terms in the Disease Ontology, the approach found 51,362 additional labels, more than tripling the number defined by the ontology itself. Manual validation by a clinical expert on a random sampling of expanded synonyms over the Human Phenotype Ontology yielded a precision of 0.912. Furthermore, we found that annotating patient visits in MIMIC-III with an extended set of Disease Ontology labels led to semantic similarity score derived from those labels being a significantly better predictor of matching first diagnosis, with a mean average precision of 0.88 for the unexpanded set of annotations, and 0.913 for the expanded set.ConclusionsInter-ontology synonym expansion can lead to a vast increase in the scale of vocabulary available for text mining applications. While the accuracy of the extended vocabulary is not perfect, it nevertheless led to a significantly improved ontology-based characterisation of patients from text in one setting. Furthermore, where run-on error is not acceptable, the technique can be used to provide candidate synonyms which can be checked by a domain expert.

An Ontology-Based Information Extraction System for Organic Farming

In the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protégé editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWL API). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.

Financial Knowledge Graph Based Financial Report Query System

Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design. Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

A Survey of Ontology-based Information Extraction for Social Media Content Analysis

The amount of information generated in the Web has grown enormously over the years. This information is significant to individuals, businesses and organizations. If analyzed, understood and utilized, it will provide a valuable insight to its stakeholders. However, many of these information are semi-structured or unstructured which makes it difficult to draw in-depth understanding of the implications behind those information. This is where Ontology-based Information Extraction (OBIE) and social media content analysis come into play. OBIE has now become a popular way to extract information coming from machine-readable sources. This paper presents a survey of OBIE, Ontology languages and tools and the process to build an ontology model and framework. The author made a comparison of two ontology building frameworks and identified which framework is complete.

Ontology-based NLP information extraction to enrich nanomaterial environmental exposure database

In recent years, nanotechnologies have led to undeniable progress in any domains, such as electronics, materials and medicine. Despite the benefits of such a technology, a careful assessment of the potential risks for Human and Environmental health have to be studied. Assessing exposure and hazard to nanomaterials is a major challenge in the field of environmental sciences. This task requires to gather a large amount of meaningful experimental data usually generated by laboratory experiments. A first database of environmental exposure to nanomaterials (EXPOSED database) has been developed to gather data generated during mesocosm experiments. The challenge is now to enrich this database with more data from scientific articles in related fields. Herein, we present an ontology-based Natural Language Processing (NLP) approach to automatically extract and transfer data from text sources to database. This approach combines the use of NLP techniques and a domain ontology to automatically extract environmental exposure and hazards information. This approach was tested to enrich the EXPOSED database and indicators of quality highlight that this approach is effective and promising.

Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype

To derive meaningful insights from voluminous healthcare data, it is essential to convert it into machine understandable knowledge. Currently, machine understandable domain specific healthcare knowledge curation framework does not exist for complex neurological diseases such as subarachnoid hemorrhage stroke. We envisage futuristic clinical decision support systems and tools backed with such knowledge will aide in complex neurological disease prognosis, diagnosis, and treatment. Existing knowledge graphs (KGs) only contain concepts and relationships between them and offer this knowledge to information extraction and knowledge management applications. However, the proposed domain-specific automated KG curation framework enables extraction of concepts, relationships, individual and cohort graphs, and predictive knowledge. By employing ontology-based information extraction, ensemble learning and word embedding based on skip-gram techniques on structured and unstructured data from electronic health records of 1025 patients with an intracranial aneurysm, this paper proposes a novel fully automated framework to curate knowledge graph, consisting of concepts, different hierarchical and non-hierarchical relationships, and predictive rules for prediction of subarachnoid hemorrhage. The evaluation shows that proposed framework achieves 78% precision and 71% recall respectively, for concept extraction from clinical text. Taxonomic relationships evaluation had precision and recall of 68%, and 95%, respectively. Evaluation of knowledge to predict unruptured status using validation dataset shows accuracy, precision, recall, of 73%, 76%, and 90% respectively.

An ontology-based information extraction and summarization of multiple news articles

Ontology based information extraction and summarization process in news content retrieved the news based on the user query. The user query should be of any context about the news content. So that, users need not be aware of the information that they search. This type of ontology based multi-document summarization system mainly focused on abstract document found in the news content. So, we proposed a work to combine information extraction, Abstraction based summarization and natural language generation to generate efficient multi-document summarization from multiple news articles. In this paper, we proposed an IE-supported summarization system that automatically extracts keyword for text summarization in Tamil e-newspaper datasets. The system first requires that the user need to select the particular domain. A set of news articles related to the domain is searched by search engine. One or more scenario templates (likes extraction domains such as Science, and technology, sports and natural disasters) are used to activate the system. The user optionally provides filters and preferences on the scenario template slots, specifying what information he/she wants to be reported in the summary. Then the system invokes the Summarizer to generate a natural language summary of the extracted information subject to the user’s constraints.

Ontology-based clinical information extraction from physician's free-text notes.

Documenting clinical notes in electronic health records might affect physician's workflow. In this paper, an Ontology-based clinical information extraction system, OB-CIE, has been developed. OB-CIE system provides a method for extracting clinical concepts from physician's free-text notes and converts the unstructured clinical notes to structured information to be accessed in electronic health records. OB-CIE system can help physicians to document visit notes without changing their workflow. For recognizing named entities of clinical concepts, ontology concepts have been used to construct a dictionary of semantic categories, then, exact dictionary matching method has been used to match noun phrases to their semantic categories. A rule-based approach has been used to classify clinical sentences to their predefined categories. The system evaluation results have achieved an F-measure of 94.90% and 97.80% for concepts classification and sentences classification, respectively. The results have showed that OB-CIE system performed well on extracting clinical concepts compared with data mining techniques. The system can be used in another field by adapting its ontology and extraction rule set.

ENRICHMENT AND POPULATION OF A GEOSPATIAL ONTOLOGY FOR SEMANTIC INFORMATION EXTRACTION

Abstract. The massive amount of user-generated content available today presents a new challenge for the geospatial domain and a great opportunity to delve into linguistic, semantic, and cognitive aspects of geographic information. Ontology-based information extraction is a new, prominent field in which a domain ontology guides the extraction process and the identification of pre-defined concepts, properties, and instances from natural language texts. The paper describes an approach for enriching and populating a geospatial ontology using both a top-down and a bottom-up approach in order to enable semantic information extraction. The top-down approach is applied in order to incorporate knowledge from existing ontologies. The bottom-up approach is applied in order to enrich and populate the geospatial ontology with semantic information (concepts, relations, and instances) extracted from domain-specific web content.

SDF: psychological Stress Detection Framework from Microblogs using Pre-defined rules and Ontologies

Spreading of Unwanted microblogs from Social Networking Sites (SNS) is pervasive in social media that leads to unaccountable disturbances such as Mental disorders, Wastage of precious time, Break-up of relationships, Stressness giving birth to psychological health problems and manymore. To overcome these problems, the immense necessity is to ignore those unwanted microblogs in SNS, which is uncontrollable by humans due to addiction towards social media. Even the literate people fall prey to psychological stress from SNS. This seriousness of stress related issues is very rarely attended by researchers, to tackle such vicious microblogs. The prediction strategy is proposed named as Stress Detection Framework (SDF) to analyze the stress in microblog. SDF is developed using Ontology based Information Extraction technique using Probabilistic Model (GSHL & TreeAlignment Algorithm ), set of pre-defined knowledge based logical rules that constitutes of low-level attributes (simple textual, linguistic words) and visual features (emoticons & Images) and social Interaction (Likes and Dislikes) to detect and predict stress in microblog messages.SDF is compared with TeniStrength that has shown an increase of 94.2% of stress detection rate. The experimental results obtained will aid to take precise decision for blocking/eradicating/ segregating stress related microblogs from Social media (especially SNS).

Towards Knowledge Handling in Ontology-Based Information Extraction Systems

Ontologies proved to be efficient and powerful tools for gathering and sharing knowledge, providing explicit specifications of conceptualizations. However, the proper ontology construction processes and their updating of various data sources, require a huge effort and well-adjusted mechanisms to their extraction, positioning and sharing. In this context, the application of a proper Ontology-Based Information Extraction (OBIE) system may help in solving these problems. The paper is a successful attempt to purvey state-of-the-art of selected OBIE systems, followed by the process of taxonomy construction and aftermath knowledge systematization of particular OBIE approaches.

An Ontology Driven Knowledge Block Summarization Approach for Chinese Judgment Document Classification

Efficient document classification techniques are crucial to current legal applications, such as case-based reasoning, legal citations, and so on. However, Chinese judgment documents are large and highly complex, so the traditional machine leaning-based classification models are often inefficient to Chinese document classification due to the fact that they fail to incorporate the overall structure and extra domain specific knowledge. In this paper, we propose an ontology-driven knowledge block summarization approach to computing document similarity for Chinese judgment document classification. First, the extra semantic knowledge for Chinese judgment documents is adopted from the perspectives of the top-level ontology and domain-specific ontologies, where how to merge the different kinds of ontologies together in an extensible manner is further represented. Second, the core semantic knowledge residing in Chinese judgment documents can be summarized into knowledge blocks by ontology-based information extraction. Third, we use Word Mover’s Distance (WMD) is to calculate the similarity between different knowledge blocks instead of their original Chinese judgment documents. At last, the KNN-based experiments for Chinese judgment document classification were made to illustrate that our approach is more effective in achieving higher classification accuracy and has faster computation speed compared to the original WMD approach.

Ontology-based information extraction for juridical events with case studies in Brazilian legal realm

The number of available legal documents has presented an enormous growth in recent years, and the digital processing of such materials is prompting the necessity of systems that support the automatic relevant information extraction. This work presents a system for ontology-based information extraction from natural language texts, able to identify a set of legal events. The system is based on an innovative methodology based on domain ontology of legal events and a set of linguistic rules, integrated through inference mechanism, resulting in a flexible approach and scalable approach. A case study with the use of documents from the Superior Court in Brazil is related, with satisfactory results in precision and recall.

OMIR: Ontology-Based Multimedia Information Retrieval System for Web Usage Mining

ABSTRACTInformation extraction relevant to the user queries is the challenging task in the ontology environment due to data varieties such as image, video, and text. The utilization of appropriate semantic entities enables the content-based search on annotated text. Recently, the automatic extraction of textual content in the audio-visual content is an advanced research area in a multimedia (MM) environment. The annotation of the video includes several tags and comments. This paper proposes the Collaborative Tagging (CT) model based on the Block Acquiring Page Segmentation (BAPS) method to retrieve the tag-based information. The information extraction in this model includes the Ontology-Based Information Extraction (OBIE) based on the single ontology utilization. The semantic annotation phase in the proposed work inserts the metadata with limited machine-readable terms. The insertion process is split into two major processes such as database uploading to server and extraction of images/web pages based on the results of semantic phase. Novel weight-based novel clustering algorithms are introduced to extract knowledge from MM contents. The ranking based on the weight value in the semantic annotation phase supports the image/web page retrieval process effectively. The comparative analysis of the proposed BAPS-CT with the existing information retrieval (IR) models regarding the average precision rate, time cost, and storage space rate assures the effectiveness of BAPS-CT in OMIR.

Strategy ontology construction and learning: insights from smart city strategies

Smart cities are designed to overcome problems that traditional cities face like the exponential increase of the population and related needs. Smart cities design is a relatively new research field that appeared with the concept of smart city. Unfortunately, it is a laborious and error prone task which suggests designers' expertise and adequate skills. To overcome this issue, we attempt to acquire expertise from existing strategies and projects about smart cities. For this end, we develop strategy ontology. The strategy ontology allows not only to formalise and conceptualise the strategy related concepts but also to analyse existing projects and strategies. We attempt thereafter to enrich it via automatic learning techniques. We use therefore automatic annotation of smart cities related documents. In addition to automatic annotation, we also use the ontology-based information extraction (OBIE) for ontology population and enrichment with new concepts and instances. The followed process is composed of five tasks: corpus construction, ontology population, ontology enrichment, ontology's consistency verification and ontology learning evaluation. The resulting ontology will allow sharing the gathered knowledge between people participating in the activities of smart cities design and thus learning from smart cities previous projects and strategies in order to create new ones.