Automatic ontology-based knowledge extraction from web documents

Journal of Intelligent Information Systems, 2016

There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than documentcentric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.

2020

Extracting required information from the huge academic repository in an efficient way is a tedious job as the repository grows day by day. Getting user satisfaction in searching is the primary concern always. Academic search engines are mostly used by research scholars, scientists, faculty, and students for their academic purposes. The use of ontology in information extraction helps an academic search engine to perform its operation effectively. As the research activities are going on in an extensive pace in all domains, new specialized areas are evolved. This paper proposes a novel framework that incorporates a dynamically evolving ontology that focuses on expanding the existing Computer Science Ontology based on the new findings in existing areas. The Word2Vec model helps to identify new keywords for the expansion of the ontology. The keywords given by the Word2Vec should satisfy a confidence score above a threshold value and only these keywords will be used for updating the Compu...

Proceedings of the 24th International Conference on World Wide Web, 2015

Today, we can find a vast amount of textual information stored in proprietary data stores. The experience of searching information in these systems could be improved in a remarkable manner if we combine these private data stores with the information supplied by the Internet, merging both data sources to get new knowledge. In this paper, we propose an architecture with the goal of automatically obtaining knowledge about entities (e.g., persons, places, organizations, etc.) from a set of natural text documents, building smart data from raw data. We have tested the system in the context of the news archive of a real Media Group.

Lecture Notes in Computer Science

With Google's Federated Learning & Facebook's introduction of client-side NLP into their chat service, the era of client-side Machine Learning is upon us. While interesting ML approaches beyond the realm of toy examples were hitherto confined to large data-centers and powerful GPU's, exponential trends in technology and the introduction of billions of smartphones enable sophisticated processing swarms of even hand-held devices. Such approaches hold several promises: 1. Without the need for powerful server infrastructures, even small companies could be scalable to millions of users easily and cost-efficiently; 2. Since data only used in the learning process never need to leave the client, personal information can be used free of privacy and data security concerns; 3. Since privacy is preserved automatically, the full range of personal information on the client device can be utilized for learning; and 4. without round-trips to the server, results like recommendations can be made available to users much faster, resulting in enhanced user experience. In this paper we propose an architecture for federated learning from personalized, graph based recommendations computed on client devices, collectively creating & enhancing a global knowledge graph. In this network, individual users will 'train' their local recommender engines, while a server-based voting mechanism aggregates the developing client-side models, preventing over-fitting on highly subjective data from tarnishing the global model.

Language Resources and Evaluation, 2007

The complexity of Korean numeral classifiers demands semantic as well as computational approaches that employ natural language processing (NLP) techniques. The classifier is a universal linguistic device, having the two functions of quantifying and classifying nouns in noun phrase constructions. Many linguistic studies have focused on the fact that numeral classifiers afford decisive clues to categorizing nouns. However, few studies have dealt with the semantic categorization of classifiers and their semantic relations to the nouns they quantify and categorize in building ontologies. In this article, we propose the semantic recategorization of the Korean numeral classifiers in the context of classifier ontology based on large corpora and KorLex Noun 1.5 (Korean wordnet; Korean Lexical Semantic Network), considering its high applicability in the NLP domain. In particular, the classifier can be effectively used to predict the semantic characteristics of nouns and to process them appropriately in NLP. The major challenge is to make such semantic classification and the attendant NLP techniques efficient. Accordingly, a Korean numeral classifier ontology (KorLexClas 1.0), including semantic hierarchies and relations to nouns, was constructed.

Proceedings of the 2015 ACM Symposium on Document Engineering, 2015

Concept hierarchies have been useful tools for presenting and organizing knowledge. With the rapid growth of online knowledge resources, automatic concept hierarchy extraction is increasingly attractive. Here, we focus on concept extraction from textbooks based on the knowledge in Wikipedia. Given a book, we extract important concepts in each book chapter using Wikipedia as a resource and from this construct a concept hierarchy for that book. We define local and global features that capture both the local relatedness and global coherence embedded in that textbook. In order to evaluate the proposed features and extracted concept hierarchies, we manually construct concept hierarchies for three well used textbooks by labeling important concepts for each book chapter. Experiments show that our proposed local and global features achieve better performance than using only keyphrases to construct the concept hierarchies. Moreover, we observe that incorporating global features can improve the concept ranking precision and reaffirms the global coherence in the book.

SpringerPlus, 2016

Ontology is one of the key components in semantic webs. It contains the core knowledge for an effective search. However, building ontology requires the carefully-collected knowledge which is very domain-sensitive. In this work, we present the practice of ontology construction for a case study of health tourism in Thailand. The whole process follows the METHONTOLOGY approach, which consists of phases: information gathering, corpus study, ontology engineering, evaluation, publishing, and the application construction. Different sources of data such as structure web documents like HTML and other documents are acquired in the information gathering process. The tourism corpora from various tourism texts and standards are explored. The ontology is evaluated in two aspects: automatic reasoning using Pellet, and RacerPro, and the questionnaires, used to evaluate by experts of the domains: tourism domain experts and ontology experts. The ontology usability is demonstrated via the semantic web...

Models, Methods, and Applications

Credit risk assessment has been one of the most appealing topics in banking and finance studies, attracting both scholars' and practitioners' attention for some time. Following the success of the Grameen Bank, works on credit risk, in particular for Small Medium Enterprises (SMEs), have become essential. The distinctive character of SMEs requires a method that takes into account quantitative and qualitative information for loan granting decision purposes. In this chapter, we first provide a survey of existing credit risk assessment methods, which shows a current gap in the existing research in regards to taking qualitative information into account during the data mining process. To address this shortcoming, we propose a framework that utilizes an XML-based template to capture both qualitative and quantitative information in this domain. By representing this information in a domain-oriented way, the potential knowledge that can be discovered for evidence-based decision support will be maximized. An XML document can be effectively represented as a rooted ordered labelled tree and a number of tree mining methods exist that enable the efficient discovery of associations among tree-structured data objects, taking both the content and structure into account. The guidelines for correct and effective application of such methods are provided in order to gain detailed insight into the information governing the decision making process. We have obtained a number of textual reports from the banks regarding the information

Wireless Communications and Mobile Computing

The World Wide Web comprises of huge web databases where the data are searched using web query interface. Generally, the World Wide Web maintains a set of databases to store several data records. The distinct data records are extracted by the web query interface as per the user requests. The information maintained in the web database is hidden and retrieves deep web content even in dynamic script pages. In recent days, a web page offers a huge amount of structured data and is in need of various web-related latest applications. The challenge lies in extracting complicated structured data from deep web pages. Deep web contents are generally accessed by the web queries, but extracting the structured data from the web database is a complex problem. Moreover, making use of such retrieved information in combined structures needs significant efforts. No further techniques are established to address the complexity in data extraction of deep web data from various web pages. Despite the fact ...

Smart Data and Computational Intelligence, 2019

IT Service Management (ITSM) is an information system management approach that represents the information system in the form of a set of capabilities that bring value to customers in the form of services. ITIL (Information Technology Infrastructure Library) framework has positioned itself as a generic solution to tackle a broad range of ITSM issues and try to guide IT managers in their endeavors. Research on the subject has been mostly restricted to the implementation of the incident, problem and change management processes. Such approaches, however, have failed to address the deployment of the service level management (SLM) process. Yet, the SLM process has been criticized as the most important process in ITIL framework and that is highly dependent on the other processes. In this paper, an ontological metamodel of ITIL SLM process explicating its core concepts is presented; the goal is to provide a machine-readable document for modeling SLM domain knowledge according to ITIL V3 Framework. The proposed metamodel could be used for: (i) a deeper evaluation for comparing it to other ITSM models, and (ii) the integration of different frameworks and standards of IT governance such as Cobit and ISO 27001.

Journal of Information Science, 2018

Ontologies provide a means to store knowledge in a machine-readable format. Ontology population is the task of updating an ontology with new facts from an input knowledge resource. These facts are represented in a structured format and integrated thereafter into the existing knowledge in the ontology. Textual resources are the dominant online knowledge resources that contain a large number of facts expressed either explicitly or implicitly. Hence, the automatic processing of the extensive knowledge available in these resources has recently gained increasing interest. This study discusses the major components of ontology population process and the different design aspects to be considered when building ontology population systems. In addition, this research explains the different approaches and techniques adopted to carry out the task of ontology population. The possible choices of the design aspects and the related issues are identified and analysed using a set of representative ont...

Математическое моделирование, 2020

Представлены модели текстовых коллекций, формируемые на базе матричной модели текстов на естественных языках. В качестве методов формирования моделей коллекций рассмотрены способы вычислительного выявления тематической структуры текстовых коллекций. Предложено использование моделей для поиска тематически сходных текстовых коллекций и тематической категоризации текстов на основании моделей текстов и текстовых коллекций. Проанализированы отличия предложенных моделей текстовых коллекций от распространенных подходов к их анализу и моделированию.

ACM Computing Surveys, 2021

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Further...

Lecture Notes in Computer Science, 2005

This paper describes the design and user evaluation of an intelligent user interface intended to mediate between users and an Adaptive Information Extraction (AIE) system. The design goal was to support a synergistic and cooperative work between user and AIE. Past laboratory experiments had shown the approach was efficient and effective. A set of focus groups were run to assess its ease of use. Logs, user satisfaction questionnaires, and interviews were exploited to investigate the interaction experience. We found that users' attitudes are mainly hierarchical with the user wishing to control and check the system's initiatives. However when confidence in the system capabilities rises, a more cooperative interaction is adopted.