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Endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects, and moods of humans in their context is an important challenge, which requires sophisticated and novel approaches of emotion recognition. Most studies explore data-driven pattern recognition techniques that are generally highly dependent on learning data and insufficiently effective for emotion contextual recognition. In this article, a hybrid model-based emotion contextual recognition approach for cognitive assistance services in ubiquitous environments is proposed. This model is based on: 1) a hybrid-level fusion exploiting a multilayer perceptron (MLP) neural-network model and the possibilistic logic and 2) an expressive emotional knowledge representation and reasoning model to recognize nondirectly observable emotions; this model exploits jointly the emotion upper ontology (EmUO) and the n-ary ontology of events HTemp supported by the NKRL language. For validation purposes of the proposed approach, experiments were carried out using a YouTube dataset, and in a real-world scenario dedicated to the cognitive assistance of visitors in a smart devices showroom. Results demonstrated that the proposed multimodal emotion recognition model outperforms all baseline models. The real-world scenario corroborates the effectiveness of the proposed approach in terms of emotion contextual recognition and management and in the creation of emotion-based assistance services.

yamato sharply distinguishes itself from other existing upper ontologies in the following respects. (1) Most importantly, yamato is designed with both engineering and philosophical minds. (2) yamato is based on a sophisticated theory of roles, given that the world is full of roles. (3) yamato has a tenable theory of functions which helps to deal with artifacts effectively. (4) Information is a ‘content-bearing’ entity and it differs significantly from the entities that philosophers have traditionally discussed. Taking into account the modern society in which a flood of information occurs, yamato has a sophisticated theory of informational objects (representations). (5) Quality and quantity are carefully organized for the sake of greater interoperability of real-world data. (6) The philosophical contribution of yamato includes a theory of objects, processes, and events. Those features are illustrated with several case studies. These features lead to the intensive application of yamato in some domains such as biomedicine and learning engineering.

A domain-independent conceptual model that aims to be highly reusable across specific domain applications is provided by upper-level ontologies which usually describe abstract concepts. In this paper, the authors proposed Sup_Ont, a fundamental upper ontology. In this ontology, the structure of the universe shows the concept of reality that is defined to have an existence which is known as truth. The devised super ontology and hence the domain ontologies can be reused across applications because of the generalized representation scheme used that is an EHCPR. An extended hierarchical censored production rules (EHCPRs) system is a knowledge representation system for reasoning with real-life problems and a step towards a generalized representation system. An EHCPR is a unit of knowledge resulting in a knowledge base that shows modularity and hierarchy. Extended hierarchical censored production rules (EHCPRs) have been used to represent the knowledge in intelligent systems.

The importance of standardized operation of medical equipment is self-evident. An effective method is to construct the medical equipment domain ontology as the background knowledge base, and make judgments on the legality of the user’s operation sequence based on the ontology, and guide the user to legally operate the equipment. After analysis, medical device data mainly comes from internal sensors. If we want to build an ontology background knowledge base, we can combine data with user operations, map the ontology to events, and build a directed graph model through events to form the final ontology. In this paper, we take the atomized particle preparation device in the field of medical equipment as an example, and selects and constructs the ontology by analyzing the internal structure and use process of the device.The application-level ontology selects the SSN ontology in the sensor field, and the upper ontology selects the general event ontology SEM. Combining the above two ontology and the characteristics of the medical device field, the ontology SSN_SEM is constructed, and the actual data of the device is semantically annotated through the SSN_SEM ontology, which verifies the validity of the ontology.

Building an ontology is a difficult and time- consuming task. In order to make this task easier and faster, some automatic methods can be employed. This paper examines the feasibility of using rules and concepts discovered during the classification tree building process in the C4.5 algorithm, in a completely automated way, for the purposes of building an ontology from data. By building the ontology directly from continuous data, concepts and relations can be discovered without specific knowledge about the domain. This paper also examines how this method reproduces the classification capabilities of the classification three within an ontology using concepts and class expression axioms.

The flexible finite element analysis (FEA) modeling process is addressed within the framework of scripting programming language such as ANSYS Parametric Design Language(APDL). Resorting to recently proposed upper ontology and specific ontology, the FEA modeling processes are expressed as the entities and relations among entities in an ontology tree. An algorithm to obtain a script fragment grammar (SFG) that describe the combination rules of script fragments by instantiating FEA ontology tree is provided. In addition, a method of automatically generating FEA parametric scripts based on SFG derivations is proposed. Then, the whole procedure is applied to develop an automatically generating FEA script software for an air separator product family. The proposed method can effectively reduce repetitive operations in the parametric FEA modeling process, which thereby improves the overall efficiency of the FEA modeling process.

In recent years, there has been a growing interest in modeling value in the context of Enterprise Architecture, which has been driven by a need to align the vision and strategic goals of an enterprise with its business architecture. Nevertheless, the current literature shows that the concept of value is conceptually complex and still causes a lot of confusion. For example, we can find in the literature the concept of value being taken as equivalent to notions as disparate as goals, events, objects and capabilities. As a result, there is still a lack of proper support for modeling all aspects of value as well as its relations to these aforementioned notions. To address this issue, we propose in this paper a pattern language for value modeling in ArchiMate, which is based on the Common Ontology of Value and Risk, a well-founded reference ontology developed following the principles of the Unified Foundation Ontology. This enables us to delineate a clear ontological foundation, which addresses the ambiguous use of the value concept. The design of the Value Pattern Language will be guided by the Design Science Research Methodology. More specifically, a first iteration of the build-and-evaluate loop is presented, which includes the development of the pattern language and its demonstration by means of a case study of a low-cost airline.

The relationship between cultural heritage, digital technologies and visual models involves an increasingly wide area of research, oriented towards the renewal of archives and museums for the preservation and promotion of culture. Recent research activities are the result of the progressive strengthening of digital technologies and the needs of a new generation of “digital” users, which requires museums to update their means of communication using Semantic Web languages and technologies shaped by a social conceptualization of a graph-based representation of information.The growth of several digitized heritage collections increases the necessity of proper methodologies to develop a structured system able to access to these collections and the large amount of data, metadata and paradata related to the digitized objects in a structured and organized way, defining a set of collection information models (CIM), that considers not only the digitizing process but also the data collection process, layered by an Upper Ontology level structure, based on CIDOC-CRM.

© 2013 IEEE. The Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE) aims at developing and applying persuasive technologies and system science for social innovation that can help humanity to move toward sustainable, wise, adaptive living. The PERSWADE collaborative knowledge base needs to be designed with the intent to bring together, enrich and logically relate heterogeneous content, such as datasets, scientific literature and any kind of multimedia and social content, to support a participatory approach and help to translate science into action. PERSWADE-CORE, the foundation ontology described in this paper, plays a critical role in this by providing the backbone semantic infrastructure to enable collaboration through efficient data and knowledge integration, sharing and reuse. It also serves the purpose of clarifying and explaining the goals, functions and operations of the Centre. Because of its purpose, PERSWADE-CORE has been designed to be easy-to-use and easy-to-adapt by allowing generic, as well as more specific, relationships among concepts. The PERSWADE approach prioritizes interoperability and relies on the Semantic Web infrastructure. Furthermore, its design is intrinsically aimed at collaborative environments in which ontologies are expected to evolve as a response to users' activity.

Ontologies are increasingly used as schema for knowledge graphs in many application areas. As such, there are a variety of different approaches for their development. In this paper, we describe and evaluate UAO (for Upper Ontology Alignment Tool), which is an extension to CoModIDE, a graphical Protege plugin for modular ontology modeling. UAO enables ontology engineers to combine modular ontology modeling with a more traditional ontology modeling approach based on upper ontologies. We posit – and our evaluation supports this claim – that the tool does indeed makes it easier to combine both approaches. Thus, UAO enables a best-of-both-worlds approach. The evaluation consists of a user study, and the results show that performing typical manual alignment modeling tasks is relatively easier with UAO than doing it with Protege alone, in terms of the time required to complete the task and improving the correctness of the output. Additionally, our test subjects provided significantly higher ratings on the System Utilization Scale for UOA.

The Allotrope Foundation (AF) is a group of pharmaceutical, device vendor, and software companies that develops and releases technologies [the Allotrope Data Format (ADF), the Allotrope Foundation Ontology (AFO), and the Allotrope Data Models (ADM)] to simplify the exchange of electronic data. We present here the first comprehensive history of the AF, its structure, a list of members and partners, and an introduction to the technologies. Finally, we provide current insights into the adoption and development of the technologies by summarizing the Fall 2020 Allotrope Connect virtual conference. This overview provides an easy access to the AF and highlights opportunities for collaboration.

With the current trends and developments in the information technology domain, there is a high enthusiasm for using semantic Web technologies and decision analysis mechanisms to solve numerous recurring issues in societies. There are plenty of existing knowledge models available on the Internet, developed for solving various problems. But, the reusability aspects of those are almost very low, due to main barriers, such as complexities associated with schema understanding, technical barriers associated with querying and comprehension of semantic representations. This will hinder the reusability of existing knowledge models and also knowledge dissemination associated with new and existing knowledge models. These consequences are obstructing the opportunities of experiencing the advancements of semantic technologies to both technical and non-technical audiences. This research is focusing on proposing an architectural structure leading towards a framework, to resolve most of the above-listed technical barriers and open doors to wider audiences in experiencing the benefits of the semantic Web. The proposed architectural structure is a combination of an instructional upper ontology and multiples of decision support systems integrated to the endpoints of the upper ontology. Crime domain is selected for the proposal of the high-level architectural design, leading towards a framework, as crime escalation has been a crucial concern which needs timely attention to under control the further spread.

Fuzzy ontology is a generalization of crisp ontology for modeling uncertain information and has been applied in recent years for supporting different activities of semantic web. However, there are great collections of crisp ontologies developed so far in various domains which are not appropriate for decision making in fuzzy environment. Accordingly, this paper aims at presenting an approach to automatically convert a crisp ontology to fuzzy ontology in the context of social networks. Furthermore, this paper demonstrates that the combination of a learning process of crisp ontology with proposed approach, decreases computational complexity of fuzzy ontology learning due to breaking the task to two optimal steps. Accordingly, the approach allows for an advantageous application of various crisp clustering techniques in fuzzy ontology context.

We present the first release of OntoMath\({}^{Edu}\), a new educational mathematical ontology. The ontology is intended to be used as a Linked Open Data hub for mathematical education, a linguistic resource for intelligent mathematical language processing and an end-user reference educational database. The ontology is organized in three layers: a foundational ontology layer, a domain ontology layer and a linguistic layer. The domain ontology layer contains language-independent concepts, covering secondary school mathematics curriculum. The linguistic layer provides linguistic grounding for these concepts, and the foundation ontology layer provides them with meta-ontological an-notations. The concepts are organized in two main hierarchies: the hierarchy of objects and the hierarchy of reified relationships. For our knowledge, OntoMath\({}^{Edu}\) is the first Linked Open Data mathematical ontology, that respects ontological distinctions provided by a foundational ontology; represents mathematical relationships as first-oder entities; and provides strong linguistic grounding for the represented mathematical concepts.

Purpose This study aims to explore a way of representing historical collections by examining the features of an event in historical documents and building an event-based ontology model. Design/methodology/approach To align with a domain-specific and upper ontology, the Basic Formal Ontology (BFO) model is adopted. Based on BFO, an event-based ontology for historical description (EOHD) is designed. To define events, event-related vocabularies are taken from the Library of Congress’ event types (2012). The three types of history and six kinds of changes are defined. Findings The EOHD model demonstrates how to apply the event ontology to biographical sketches of a creator history to link event types. Research limitations/implications The EOHD model has great potential to be further expanded to specific events and entities through different types of history in a full set of historical documents. Originality/value The EOHD provides a framework for modeling and semantically reforming the relationships of historical documents, which can make historical collections more explicitly connected in Web environments.

The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems. Keywords: Information systems, Ontology, Domain knowledge, Database, Information retrieval, Knowledge management

Current urban and district energy management systems lack a common semantic referential for e�ectively interrelating intelligent sensing, data models and energy models with visualization, analysis and decision support tools. This paper describes the structure, as well as the rationale that led to this structure, of an ontology that captures the real-world concepts of a district energy system, such as a district heating and cooling system. This ontology (called eedistrict ontology) is intended to support knowledge provision that can play the role of an intermediate layer between high-level energy management software applications and local monitoring and control software components. In order to achieve that goal, the authors propose to encapsulate queries to the ontology in a scalable web service, which will facilitate the development of interfaces for third-party applications. Considering the size of the ee-district ontology once populated with data from a speci�c district case study, this could prove to be a repetitive and time-consuming task for the software developer. This paper therefore assesses the feasibility of ontology-driven automation of web service development that is to be a core element in the deployment of heterogeneous district-wide energy management software.

Purpose The purpose of this study is to explore an ontology of designer-user interaction with a knowledge management foundation. To address this research gap, the authors ask the following research question: what types of knowledge on designer-user interactions are associated with design function and approach in creating effective design outcomes in a collaborative design process? Design/methodology/approach Based on ontology of a knowledge management foundation and 99 design projects, the authors conceptualized the ontology of designer-user interaction, which considers design role, function, approach and outcome as a knowledge of designer-user interaction in the design process. Findings Based on this analysis, the authors theorize an ontology of designer-user interactions with five dimensions: participant, role, function, design approach and design outcome. Also, this study presents a case study of how this ontology could be applied into the actual projects. Originality/value In this study, the authors explore an ontology of designer-user interaction with a knowledge management foundation, because previous interdisciplinary design studies have not formalized the types of designer-user interaction. To address this research gap, the authors ask the following research question: What types of knowledge on designer-user interactions are associated with design function and approach in creating effective design outcomes in a collaborative design process?

With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ont...

PaaS is a Cloud computing service that provides a computing platform to develop, run, and manage applications without the complexity of infrastructure maintenance. SMEs are reluctant to enter the growing PaaS market due to the possibility of being locked in to a certain platform, mostly provided by the market's giants. The PaaSport Marketplace aims to avoid the provider lock-in problem by allowing Platform provider SMEs to roll out semantically interoperable PaaS offerings and Software SMEs to deploy or migrate their applications on the best-matching offering, through a thin, non-intrusive Cloud broker. In this paper, we present the PaaSport semantic model, namely an OWL ontology, extension of the DUL ontology. The ontology is used for semantically representing (a) PaaS offering capabilities and (b) requirements of applications to be deployed. The ontology has been designed to optimally support a semantic matchmaking and ranking algorithm that recommends the best-matching PaaS offering to the application developer. The DUL ontology offers seamless extensibility, since both PaaS Characteristics and parameters are defined as classes; therefore, extending the ontology with new characteristics and parameters requires the addition of new specialized subclasses of the already existing classes, which is less complicated than adding ontology properties. The PaaSport ontology is evaluated through verification tools, competency questions, human experts, application tasks and query performance tests.

Ontology is the kernel technology of semantic web, which plays a prominent role for achieving inter-operability across heterogeneous systems and applications by formally describing the semantics of data that characterize a particular application domain. However, different ontology engineers might have potentially opposing world views which could yield the different descriptions on the same ontology entity, raising so called ontology heterogeneous problem. Ontology matching, which aims at identifying the correspondences between the entities of heterogeneous ontologies, is recognized as an effective technology to solve the ontology heterogeneous problem. Due to the complexity of ontology matching process, ontology alignments generated by the automatic ontology matchers should be validated by the users to ensure their qualities, and the technology that makes multiple users collaborate with each other to help the automatic tool create high quality matchings in a reasonable amount of time is called collaborative ontology matching. Such a collaborative ontology matching poses a new challenge of how to reduce users’ workload, but at the same time, increase their involvement’s value. To address this challenge, in this paper, we propose a Compact Interactive Memetic Algorithm (CIMA) based collaborative ontology matching technology, which can reduce users’ workload by adaptively determining the time of getting users involved, presenting the most problematic correspondences for users and helping users to automatic validate multiple conflict mappings, and increase user involvement’s value by propagating the collaborative validation and decreasing the negative effect brought by the error user validations. The experimental results show that our proposal is able to efficiently exploit the collaborative validation to improve its non-interactive version, and the runtime and alignment quality of our approach both outperform state-of-the-art interactive ontology matching systems under different user error rate cases.

Aristotle was the founder not only logics but also of ontology which he describes in Metaphysics and Categories as a theory of general properties of all entities and categorical aspects they should be analyzed. Meanwhile it is commonly accepted that we inherited from him not one but two different logics: early dialectical logoi of Topics and later formal syllogistic of Prior Analytics. The last considers logics the same way as the modern symbolic logic do. According to J. Bochenski the symbolic logic is “a theory of general objects” (by apt turn in phrase, a "physics of the object in general”) hence logics, as it is interpreted now, has the same subject as ontology. But does Aristotle himself counts that ontology (as it is accepted to speak now) is just a kind of “prolegomenon” to logic? In the paper some aspects of this issue are studied at length.

The correlated exploitation of disparate and heterogeneous data sources is important to the efficacy of many analytics tasks. Currently in application domains of major interest, such as in the maritime and aviation domains, available technology provides real time surveillance data from moving entities, which together with archival static data, can be processed in an integrated way to detect complex events and support decision making. The variety of data in disparate sources, the heterogeneity of data formats, as well as the volume of data, make data retrieval, integration, and especially reasoning with these data, challenging tasks. This paper presents an ontology-based distributed framework that addresses conjunctively these challenges: Data retrieval, integration and reasoning with data from heterogeneous static or regularly updated data sources. The proposed OBDAIR framework provides the means to support building scalable data-driven domain-specific applications that support decision-making and problem-solving. This is achieved by processing large volumes of heterogeneous data close to the sources, supporting knowledge generation in a distributed/decentralized but still unified manner. OBDAIR integrates modular ontology representation frameworks and ontology-based data access frameworks: This article presents an instantiation of OBDAIR using the modular ontology representation framework E − SHIQ , and the Ontop ontology-based access system. This OBDAIR instance has been evaluated at recognising important complex events in the maritime domain using real-world data. Experiments show the potential of OBDAIR to detect complex events in large geographic areas with computational efficiency.

PurposeThe purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval.Design/methodology/approachA multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology).FindingsThe results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500.Research limitations/implicationsThis study only focused on BBC Sport News collection in the year 2009.Practical implicationsThe paper includes implications for the development of ontology-based retrieval on image collection.Originality valueThis study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning.

A domain-specific ontology for IT benchmarking has been developed to bridge the gap between a systematic characterization of IT services and their data-based valuation. Since information is generally collected during a benchmark exercise using questionnaires on a broad range of topics, such as employee costs, software licensing costs, and quantities of hardware, it is commonly stored as natural language text; thus, this information is stored in an intrinsically unstructured form. Although these data form the basis for identifying potentials for IT cost reductions, neither a uniform description of any measured parameters nor the relationship between such parameters exists. Hence, this work proposes an ontology for the domain of IT benchmarking, available at https://w3id.org/bmontology. The design of this ontology is based on requirements mainly elicited from a domain analysis, which considers analyzing documents and interviews with representatives from Small- and Medium-Sized Enterprises and Information and Communications Technology companies over the last eight years. The development of the ontology and its main concepts is described in detail (i.e., the conceptualization of benchmarking events, questionnaires, IT services, indicators and their values) together with its alignment with the DOLCE-UltraLite foundational ontology.

This paper aims to develop an ontology (AFPS-Onto) which formalizes the knowledge of active fall protection system (AFPS) design, with attempt to facilitate knowledge sharing and reuse. METHONTOLOGY was adopted as a method to build the AFPS-Onto. The AFPS-Onto consists of nine core concepts: hazard, actor, task, ifc building element, construction method, constraint, safety resource, hazard control measure, and residual risk. The concepts, relations, attributes, and axioms were coded using Protege. The ontology was evaluated through automated consistency checking, criteria-based and task-based evaluation. The AFPS-Onto fills the knowledge gap by providing a formal and shared vocabulary for the domain of AFPS design. This can promote knowledge reuse and sharing among professional engineers. In addition, the ontology can be used to develop knowledge-based systems to help design effective AFPS. Future effort can be made to develop ontologies of other control measures against fall from heights and combine them into a fall from heights ontology (FFH-Onto).

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.

Purpose The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically. Design/methodology/approach TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology. Findings The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods. Originality/value The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.