Ontologies have been known for their semantic representation of knowledge. ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic ontology generation from unstructured text corpus. Unfortunately, systems that aim to generate ontologies from unstructured text corpus are domain-specific and require manual intervention. In addition, they suffer from uncertainty in creating concept linkages and difficulty in finding axioms for the same concept. Knowledge Graphs (KGs) has emerged as a powerful model for the dynamic representation of knowledge. However, KGs have many quality limitations and need extensive refinement. This research aims to develop a novel domain-independent automatic ontology generation framework that converts unstructured text corpus into domain consistent ontological form. The framework generates KGs from unstructured text corpus as well as refine and correct them to be consistent with domain ontologies. The power of the proposed automatically generated ontology is that it integrates the dynamic features of KGs and the quality features of ontologies.
This paper presents the OntoRich framework, a support tool for semi-automatic ontology enrichment and evaluation. The WordNet is used to extract candidates for dynamic ontology enrichment from RSS streams. With the integration of OpenNLP the system gains access to syntactic analysis of the RSS news. The enriched ontologies are evaluated against several qualitative metrics.
Change management for evolving collaborative business process development is crucial when the business logic, transections and workflow change due to changes in business strategies or organizational and technical environment. During the change implementation, business processes are analyzed and improved ensuring that they capture the proposed change and they do not contain any undesired functionalities or change side-effects. This paper presents Business Process Change Management approach for the efficient and effective implementation of change in the business process. The key technology behin…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Knowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowledge in a fully automated way. However fully-automated knowledge-base evolution on the Semantic Web is a major challenges, although there are many ontology evolution techniques available. Therefore learning ontology automatically can contribute to the semantic web society significantly. In this paper, we propose full-automated ontology learning algorithm to generate refined knowledge base from incomplete knowledge ba…
Ontology Learning has been the subject of intensive study for the past decade. Researchers in this field have been motivated by the possibility of automatically building a knowledge base on top of text documents so as to support reasoning based knowledge extraction. While most works in this field have been primarily statistical (known as light-weight Ontology Learning) not much attempt has been made in axiomatic Ontology Learning (called heavy-weight Ontology Learning) from Natural Language text documents. Heavy-weight Ontology Learning supports more precise formal logic-based reasoning when c…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）；University College London（伦敦大学学院）
Reference ontologies provide a shared vocabulary and knowledge resource for their domain. Manual construction enables them to maintain a high quality, allowing them to be widely accepted across their community. However, the manual development process does not scale for large domains. We present a new methodology for automatic ontology extension and apply it to the ChEBI ontology, a prominent reference ontology for life sciences chemistry. We trained a Transformer-based deep learning model on the leaf node structures from the ChEBI ontology and the classes to which they belong. The model is the…
Orange Labs（奥兰奇实验室）；University of Versailles（凡尔赛大学）
The construction of a reference ontology for a large domain still remains an hard human task. The process is sometimes assisted by software tools that facilitate the information extraction from a textual corpus. Despite of the great use of XML Schema files on the internet and especially in the B2B domain, tools that offer a complete semantic analysis of XML schemas are really rare. In this paper we introduce Janus, a tool for automatically building a reference knowledge base starting from XML Schema files. Janus also provides different useful views to simplify B2B application integration.
Motivated by the increased need for formalized representations of the domain of Data Mining, the success of using Formal Concept Analysis (FCA) and Ontology in several Computer Science fields, we present in this paper a new approach for automatic generation of Fuzzy Ontology of Data Mining (FODM), through the fusion of conceptual clustering, fuzzy logic, and FCA. In our approach, we propose to generate ontology taking in consideration another degree of granularity into the process of generation. Indeed, we suggest to define an ontology between classes resulting from a preliminary classificatio…
Schulich School of Business, York University（约克大学舒立克商学院）；York University（约克大学）
An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods--e.g. tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods--has often not been possible with today's items that are produced and transported in complex, inter-organizational, often internationally-spanning supply chains. Recent adoption of Internet of Things and Blockchain technologies give promise at better supply chain provenance. We are particularly interested in the blockchain as many favoured use cases of blockchain…
Peking University（北京大学）；IER Business Development Center（IER业务发展中心）
Ontology can be used for the interpretation of natural language. To construct an anti-infective drug ontology, one needs to design and deploy a methodological step to carry out the entity discovery and linking. Medical synonym resources have been an important part of medical natural language processing (NLP). However, there are problems such as low precision and low recall rate. In this study, an NLP approach is adopted to generate candidate entities. Open ontology is analyzed to extract semantic relations. Six-word vector features and word-level features are selected to perform the entity lin…
Centro de Tecnología Biomédica（生物医学技术中心）；Universidad Politécnica de Madrid（马德里理工大学）
There is a large number of online documents data sources available nowadays. The lack of structure and the differences between formats are the main difficulties to automatically extract information from them, which also has a negative impact on its use and reuse. In the biomedical domain, the DISNET platform emerged to provide researchers with a resource to obtain information in the scope of human disease networks by means of large-scale heterogeneous sources. Specifically in this domain, it is critical to offer not only the information extracted from different sources, but also the evidence t…
The interaction between business models is used in consumer centric manner instead of using a producer centric approach for customizing the business process in cloud environment. The knowledge based human semantic web is used for customizing the business process It introduces the Human Semantic Web as a conceptual interface, providing human-understandable semantics on top of the ordinary Semantic Web, which provides machine-readable semantics based on RDF in this mismatching is a major problem. To overcome this following technique automatic customization detection is an automated process of de…
INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE（法国国家信息与自动化研究所）
We present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance-level cross-fertilize with alignments at the schema-level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with two of the world'…
The delineation of logical definitions for each class in an ontology and the consistent application of these definitions to the assignment of instances to classes are important criteria for ontology evaluation. If ontologies are specified with property-based restrictions on class membership, then such consistency can be checked automatically. If no such logical restrictions are applied, as is the case with many biological ontologies, there are currently no automated methods for measuring the semantic consistency of instance assignment on an ontology-wide scale, nor for inferring the patterns o…
Bosch Center for AI（博世人工智能中心）；University of Oslo（奥斯陆大学）；Nanjing University（南京大学）
Knowledge graphs (KG) are used in a wide range of applications. The automation of KG generation is very desired due to the data volume and variety in industries. One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology. However, the automatic generation of such ontology is demanding and existing solutions are often not satisfactory. An important challenge is a trade-off between two principles of ontology engineering: knowledge-orientation and data-orientation. The former on…
Vaal University of Technology（瓦尔理工大学）；North-West University（西北大学）
Electronic government (e-government) has been one of the most active areas of ontology development during the past six years. In e-government, ontologies are being used to describe and specify e-government services (e-services) because they enable easy composition, matching, mapping and merging of various e-government services. More importantly, they also facilitate the semantic integration and interoperability of e-government services. However, it is still unclear in the current literature how an existing ontology building methodology can be applied to develop semantic ontology models in a go…
Curtin University of Technology（科廷科技大学）；University of Western Australia（西澳大学）
The need for domain ontologies in mission critical applications such as risk management and hazard identification is becoming more and more pressing. Most research on ontology learning conducted in the academia remains unrealistic for real-world applications. One of the main problems is the dependence on non-incremental, rare knowledge and textual resources, and manually-crafted patterns and rules. This paper reports work in progress aiming to address such undesirable dependencies during ontology construction. Initial experiments using a working prototype of the system revealed promising poten…
The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing machines and processes. To fully utilize this potential of dynamic manufacturing through automatic production planning, formal skill descriptions of the machines are essential. However, generating those skill descriptions in a manual fashion is labor-intensive and requires extensive domain-knowledge. In this contribution an ontology-based semi-automatic skill description system that utilizes production logs and industr…
Towards Ontology Construction with Language Models
Leipzig University（莱比锡大学）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；School of Embedded Composite Artificial Intelligence (SECAI)（嵌入式复合人工智能学院（SECAI））；TU Dortmund University（多特蒙德工业大学）
Institute of Cognitive Sciences and Technologies, Italian National Research Council (ISTC-CNR)（意大利国家研究委员会认知科学与技术研究所）；University of Bologna（博洛尼亚大学）
We present the Frame-based ontology Design Outlet (FrODO), a novel method and tool for drafting ontologies from competency questions automatically. Competency questions are expressed as natural language and are a common solution for representing requirements in a number of agile ontology engineering methodologies, such as the eXtreme Design (XD) or SAMOD. FrODO builds on top of FRED. In fact, it leverages the frame semantics for drawing domain-relevant boundaries around the RDF produced by FRED from a competency question, thus drafting domain ontologies. We carried out a user-based study for a…
Ontology matching is the process of automatically determining the semantic equivalences between the concepts of two ontologies. Most ontology matching algorithms are based on two types of strategies: terminology-based strategies, which align concepts based on their names or descriptions, and structure-based strategies, which exploit concept hierarchies to find the alignment. In many domains, there is additional information about the relationships of concepts represented in various ways, such as Bayesian networks, decision trees, and association rules. We propose to use the similarities between…
Modeling an ontology is a hard and time-consuming task. Although methodologies are useful for ontologists to create good ontologies, they do not help with the task of evaluating the quality of the ontology to be reused. For these reasons, it is imperative to evaluate the quality of the ontology after constructing it or before reusing it. Few studies usually present only a set of criteria and questions, but no guidelines to evaluate the ontology. The effort to evaluate an ontology is very high as there is a huge dependence on the evaluator's expertise to understand the criteria and questions in…
Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）
In recent years, Semantic Web technologies have been increasingly adopted by researchers, industry and public institutions to describe and link data on the Web, create web annotations and consume large knowledge graphs like Wikidata and DBPedia. However, there is still a knowledge gap between ontology engineers, who design, populate and create knowledge graphs; and web developers, who need to understand, access and query these knowledge graphs but are not familiar with ontologies, RDF or SPARQL. In this paper we describe the Ontology-Based APIs framework (OBA), our approach to automatically cr…
Montclair State University（蒙特克莱尔州立大学）；Covenant University（圣约大学）
The ability of a system to meet its requirements is a strong determinant of success. Thus effective requirements specification is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IM…
In this work we propose an ontology to support automated negotiation in multiagent systems. The ontology can be connected with some domain-specific ontologies to facilitate the negotiation in different domains, such as Intelligent Transportation Systems (ITS), e-commerce, etc. The specific negotiation rules for each type of negotiation strategy can also be defined as part of the ontology, reducing the amount of knowledge hardcoded in the agents and ensuring the interoperability. The expressiveness of the ontology was proved in a multiagent architecture for the automatic traffic light setting a…
University of Manchester（曼彻斯特大学）；Institut National de la Santé et de la Recherche Médicale（法国国家健康与医学研究院）
We present Populous, a tool for gathering content with which to populate an ontology. Domain experts need to add content, that is often repetitive in its form, but without having to tackle the underlying ontological representation. Populous presents users with a table based form in which columns are constrained to take values from particular ontologies; the user can select a concept from an ontology via its meaningful label to give a value for a given entity attribute. Populated tables are mapped to patterns that can then be used to automatically generate the ontology's content. Populous's con…
Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）
Ontologies are increasingly used for machine reasoning over the last few years. They can provide explanations of concepts or be used for concept classification if there exists a mapping from the desired labels to the relevant ontology. Another advantage of using ontologies is that they do not need a learning process, meaning that we do not need the train data or time before using them. This paper presents a practical use of an ontology for a classification problem from the financial domain. It first transforms a given ontology to a graph and proceeds with generalization with the aim to find co…
Université de La Réunion（留尼汪大学）；Griffith University（格里菲斯大学）
This article first lists reasons why - in the long term or when creating a new knowledge base (KB) for general knowledge sharing purposes - collaboratively building a well-organized KB does/can provide more possibilities, with on the whole no more costs, than the mainstream approach where knowledge creation and re-use involves searching, merging and creating (semi-)independent (relatively small) ontologies or semi-formal documents. The article lists elements required to achieve this and describes the main one: a KB editing protocol that keeps the KB free of automatically/manually detected inco…
We propose an automatic approach to analyze the consistency and satisfiability of Unified Modeling Language UML models containing multiple class, object and statechart diagrams using logic reasoners for the Web Ontology Language OWL 2. We describe how to translate UML models in OWL 2 and we present a tool chain implementing this translation that can be used with any standard compliant UML modeling tool. The proposed approach is limited in scope, but is fully automatic and does not require any expertise about OWL 2 and its reasoners from the designer.
Huazhong Agricultural University（华中农业大学）；Hubei HongShan Laboratory（湖北洪山实验室）；University of South Australia（南澳大学）；Wuhan University（武汉大学）；Macro Agricultural Research Institute（宏观农业研究院）
A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we pro…
Peking University（北京大学）；Yale University（耶鲁大学）；Tencent（腾讯）；University of Washington（华盛顿大学）
Gene Ontology (GO) is the primary gene function knowledge base that enables computational tasks in biomedicine. The basic element of GO is a term, which includes a set of genes with the same function. Existing research efforts of GO mainly focus on predicting gene term associations. Other tasks, such as generating descriptions of new terms, are rarely pursued. In this paper, we propose a novel task: GO term description generation. This task aims to automatically generate a sentence that describes the function of a GO term belonging to one of the three categories, i.e., molecular function, biol…
AceWiki is a prototype that shows how a semantic wiki using controlled natural language - Attempto Controlled English (ACE) in our case - can make ontology management easy for everybody. Sentences in ACE can automatically be translated into first-order logic, OWL, or SWRL. AceWiki integrates the OWL reasoner Pellet and ensures that the ontology is always consistent. Previous results have shown that people with no background in logic are able to add formal knowledge to AceWiki without being instructed or trained in advance.
University of Mannheim（曼海姆大学）；The University of Sheffield（谢菲尔德大学）；Vienna University of Economics and Business（维也纳经济与商业大学）；L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Complexity Science Hub Vienna（维也纳复杂性科学中心）
Open Government Data (OGD) is being published by various public administration organizations around the globe. Within the metadata of OGD data catalogs, the publishing organizations (1) are not uniquely and unambiguously identifiable and, even worse, (2) change over time, by public administration units being merged or restructured. In order to enable fine-grained analyses or searches on Open Government Data on the level of publishing organizations, linking those from OGD portals to publicly available knowledge graphs (KGs) such as Wikidata and DBpedia seems like an obvious solution. Still, as …
Ontology Refinement with Embeddings and LLMs: A Case Study on CASTON
University of Manchester（曼彻斯特大学）；Jönköping University（延雪平大学）；National University of Singapore（新加坡国立大学）；COMPtech i Skillingaryd AB（COMPtech i Skillingaryd AB公司）
Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. The best currently known interactive debugging systems rely upon some meta information in terms of fault probabilities, which can speed up the debugging procedure in the good case, but can also have negative impact on the performance in the bad case. The problem is that assessment of the meta information is only possible a-posteriori. Consequently, as long as the actual fault is unknown, th…
King's College London（伦敦国王学院）；University of Sussex（苏塞克斯大学）；The University of Manchester（曼彻斯特大学）
Ontology development is a non-trivial task requiring expertise in the chosen ontological language. We propose a method for making the content of ontologies more transparent by presenting, through the use of natural language generation, naturalistic descriptions of ontology classes as textual paragraphs. The method has been implemented in a proof-of- concept system, OntoVerbal, that automatically generates paragraph-sized textual descriptions of ontological classes expressed in OWL. OntoVerbal has been applied to ontologies that can be loaded into Protégé and been evaluated with SNOMED CT, show…
In the context of the Covid-19 pandemic, many were quick to spread deceptive information. I investigate here how reasoning in Description Logics (DLs) can detect inconsistencies between trusted medical sources and not trusted ones. The not-trusted information comes in natural language (e.g. "Covid-19 affects only the elderly"). To automatically convert into DLs, I used the FRED converter. Reasoning in Description Logics is then performed with the Racer tool.
Enterprise knowledge is a key asset in the competing and fast-changing corporate landscape. The ability to learn, store and distribute implicit and explicit knowledge can be the difference between success and failure. While enterprise knowledge management is a well-defined research domain, current implementations lack orientation towards small and medium enterprise. We propose a semantic search engine for relevant documents in an enterprise, based on automatic generated domain ontologies. In this paper we focus on the component for ontology learning and population.
Semantics based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty level of these system generated questions is helpful to effectively utilize them in various educational and professional applications. The existing approaches for finding the difficulty level of factual questions are very simple and are limited to a few basic principles. We propose a new methodology for this problem by considering an educational theory called Item Response Theory (IRT). In the IRT, knowledge proficiency of…
Uncertainty in Automated Ontology Matching: Lessons Learned from an Empirical Experimentation
University of Tunis El Manar（突尼斯迦太基大学）；University of Technology Sydney（悉尼科技大学）；Tallinn University of Technology（塔林理工大学）；University of Southern Denmark（南丹麦大学）
This paper presents a tool, TyDI, and methods experimented in the building of a termino-ontology, i.e. a lexicalized ontology aimed at fine-grained indexation for semantic search applications. TyDI provides facilities for knowledge engineers and domain experts to efficiently collaborate to validate, organize and conceptualize corpus extracted terms. A use case on biotechnology patent search demonstrates TyDI's potential.
Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction
Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-Universität Bochum（波鸿鲁尔大学先进材料模拟跨学科中心）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；BAM Federal Institute for Materials Research and Testing（BAM联邦材料研究与测试研究所）
Jožef Stefan International Postgraduate School（约热夫·斯特凡国际研究生院）
Semantic parsing methods are used for capturing and representing semantic meaning of text. Meaning representation capturing all the concepts in the text may not always be available or may not be sufficiently complete. Ontologies provide a structured and reasoning-capable way to model the content of a collection of texts. In this work, we present a novel approach to joint learning of ontology and semantic parser from text. The method is based on semi-automatic induction of a context-free grammar from semantically annotated text. The grammar parses the text into semantic trees. Both, the grammar…
University of Calabria（卡拉布里亚大学）；AGH University of Science and Technology（AGH科技大学）
Homogeneous unstructured data (HUD) are collections of unstructured documents that share common properties, such as similar layout, common file format, or common domain of values. Building on such properties, it would be desirable to automatically process HUD to access the main information through a semantic layer -- typically an ontology -- called semantic view. Hence, we propose an ontology-based approach for extracting semantically rich information from HUD, by integrating and extending recent technologies and results from the fields of classical information extraction, table recognition, o…
Competency Questions (CQs) are used in many ontology engineering methodologies to collect requirements and track the completeness and correctness of an ontology being constructed. Although they are frequently suggested by ontology engineering methodologies, the publicly available datasets of CQs and their formalizations in ontology query languages are very scarce. Since first efforts to automate processes utilizing CQs are being made, it is of high importance to provide large and diverse datasets to fuel these solutions. In this paper, we present BigCQ, the biggest dataset of CQ templates with…
