A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

Abstract: Vast amounts of medical information reside within text documents, so that the automatic retrieval of such information would certainly be beneficial for clinical activities. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semi-automatic methods to build ontologies. Most techniques for learning domain ontologies from free text have important limitations. Thus, they can extract concepts so that only taxonomies are generally produced although there are other types of semantic relations relevant in knowledge modelling. This paper presents a language-independent approach for extracting knowledge from medical natural language documents. The knowledge is represented by means of ontologies that can have multiple semantic relationships among concepts.     

Relating ontologies with a fuzzy information model

More people than ever before have access to information with the World Wide Web; information volume and number of users both continue to expand. Traditional search methods based on keywords are not effective, resulting in large lists of documents, many of which unrelated to users’ needs. One way to improve information retrieval is to associate meaning to users’ queries by using ontologies, knowledge bases that encode a set of concepts about one domain and their relationships. Encoding a knowledge base using one single ontology is usual, but a document collection can deal with different domains, each organized into an ontology. This work presents a novel way to represent and organize knowledge, from distinct domains, using multiple ontologies that can be related. The model allows the ontologies, as well as the relationships between concepts from distinct ontologies, to be represented independently. Additionally, fuzzy set theory techniques are employed to deal with knowledge subjectivity and uncertainty. This approach to organize knowledge and an associated query expansion method are integrated into a fuzzy model for information retrieval based on multi-related ontologies. The performance of a search engine using this model is compared with another fuzzy-based approach for information retrieval, and with the Apache Lucene search engine. Experimental results show that this model improves precision and recall measures.     

Towards a cognitive foundation for knowledge representation

Abstract. Knowledge engineering, knowledge management and conceptual modelling are concerned with representing knowledge of business and organizational domains. These research areas use ontologies for knowledge representation. Ontologies are understood either in the philosophical sense as firm metaphysical commitments or in the looser sense of dictionaries or taxonomies.
This paper critically examines the understanding and use of ontologies and knowledge representation languages in information systems (IS) research and application. As ontologies are intended to be conceptualizations of a perceived reality, they should reflect the empirically observed reality. This motivates proposing psychology of language as a reference discipline for knowledge engineering and knowledge management. Natural language is argued to reflect the cognitive concepts we use to think about and perceive the world around us. These cognitive concepts are the relevant terms with which to structure and represent knowledge about the world.
Psychology of language can provide empirical justification for a particular set of concepts to represent knowledge. This paper draws on psycho-linguistic research to develop a proposal for a system of cognitive structures. This is argued to provide the relevant concepts on which to found knowledge representation schemata for knowledge engineering, knowledge management and conceptual modelling.     

Coupling semantic and statistical techniques for dynamically enriching web ontologies

With the development of the Semantic Web technology, the use of ontologies to store and retrieve information covering several domains has increased. However, very few ontologies are able to cope with the ever-growing need of frequently updated semantic information or specific user requirements in specialized domains. As a result, a critical issue is related to the unavailability of relational information between concepts, also coined missing background knowledge. One solution to address this issue relies on the manual enrichment of ontologies by domain experts which is however a time consuming and costly process, hence the need for dynamic ontology enrichment. In this paper we present an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic ontologies from the World Wide Web. Using the massive amount of information encoded in texts on the Web as a corpus, missing background knowledge can therefore be discovered through a combination of semantic relatedness measures and pattern acquisition techniques and subsequently exploited. The benefits of our approach are: (i) proposing the dynamic enrichment of large-scale generic ontologies with missing background knowledge, and thus, enabling the reuse of such knowledge, (ii) dealing with the issue of costly ontological manual enrichment by domain experts. Experimental results in a precision-based evaluation setting demonstrate the effectiveness of the proposed techniques.     

Developing a modular hydrogeology ontology by extending the SWEET upper-level ontologies

Upper-level ontologies comprise general concepts and properties which need to be extended to include more diverse and specific domain vocabularies. We present the extension of NASA's Semantic Web for Earth and Environmental Terminology (SWEET) ontologies to include part of the hydrogeology domain. We describe a methodology that can be followed by other allied domain experts who intend to adopt the SWEET ontologies in their own discipline. We have maintained the modular design of the SWEET ontologies for maximum extensibility and reusability of our ontology in other fields, to ensure inter-disciplinary knowledge reuse, management, and discovery.The extension of the SWEET ontologies involved identification of the general SWEET concepts (classes) to serve as the super-class of the domain concepts. This was followed by establishing the special inter-relationships between domain concepts (e.g., equivalence for vadose zone and unsaturated zone), and identifying the dependent concepts such as physical properties and units, and their relationship to external concepts. Ontology editing tools such as SWOOP and Protégé were used to analyze and visualize the structure of the existing OWL files. Domain concepts were introduced either as standalone new classes or as subclasses of existing SWEET ontologies. This involved changing the relationships (properties) and/or adding new relationships based on domain theories. In places, in the Owl files, the entire structure of the existing concepts needed to be changed to represent the domain concept more meaningfully. Throughout this process, the orthogonal structure of SWEET ontologies was maintained and the consistency of the concepts was tested using the Racer reasoner. Individuals were added to the new concepts to test the modified ontologies. Our work shows that SWEET ontologies can successfully be extended and reused in any field without losing their modular or reference structure, or disrupting their URI links.     

运用属性探索构建完备描述逻辑本体

分析描述逻辑本体构建的重要性和研究现状。针对描述逻辑本体构建中本体的完备性问题,研究属性探索算法在描述逻辑本体构建中的应用,分析目前运用属性探索算法构建本体时假设专家必须具备全部领域知识的不足,研究在领域专家不具备全部领域知识情况下的完备描述逻辑本体构建。在描述背景下给出描述逻辑本体完备性的定义,设置描述背景下的不完备背景,构造一种在不完备背景下领域专家不能判断属性集合间的蕴含关系的描述逻辑本体构建算法。该算法可与领域专家交互获取蕴含知识从而构建本体知识库,并且证明利用该方法构建的本体是完备本体。     

Toward analyzing impact of disjoint axioms for merging heterogeneous ontologies

In recent years, the question on Automatic Ontology Merging (AOM) become challenging to address for the researchers. Our research and development for the Disjoint Knowledge Perservation based Automatic Ontology Merging (DKP-AOM) is a milestone in the same direction. This paper provides a more specific discussion about disjoint knowledge axioms in DKP-AOM and makes an assessment of our merge algorithm that looks-up within disjoint partitions of concept hierarchies of ontologies. The significant use of disjoint knowledge is corroborated by testing conference and vertebrate ontologies. The results reveal that disjoint knowledge axioms help identifying initial inaccurate mappings and remove ambiguity when the concept with same symbolic identifier has a different meaning in different local ontologies in the process of ontology merging. Disjoint axioms separate the knowledge in distinct chunks and enable concept matching within the boundaries of sub-hierarchies of the entire ontology concept hierarchy. While finding matches between concepts of ontologies, disjoint partitions with the disjoint knowledge about concepts in source ontologies minimize the search space and reduce the runtime complexity of ontology merging. We also discuss encouraging results obtained by our DKP-AOM system within the OAEI 2015 campaign.     

Towards Ontology Generation from Tables

At the heart of today's information-explosion problems are issues involving semantics, mutual understanding, concept matching, and interoperability. Ontologies and the Semantic Web are offered as a potential solution, but creating ontologies for real-world knowledge is nontrivial. If we could automate the process, we could significantly improve our chances of making the Semantic Web a reality. While understanding natural language is difficult, tables and other structured information make it easier to interpret new items and relations. In this paper we introduce an approach to generating ontologies based on table analysis. We thus call our approach TANGO (Table ANalysis for Generating Ontologies). Based on conceptual modeling extraction techniques, TANGO attempts to (i) understand a table's structure and conceptual content; (ii) discover the constraints that hold between concepts extracted from the table; (iii) match the recognized concepts with ones from a more general specification of related concepts; and (iv) merge the resulting structure with other similar knowledge representations. TANGO is thus a formalized method of processing the format and content of tables that can serve to incrementally build a relevant reusable conceptual ontology.     

A novel approach to generate MCQs from domain ontology: Considering DL semantics and open-world assumption

Ontologies are structures, used for knowledge representation, which model domain knowledge in the form of concepts, roles, instances and their relationships. This knowledge can be exploited by an assessment system in the form of multiple choice questions (MCQs). The existing approaches, which use ontologies expressed in the Web Ontology Language (OWL) for MCQ generation, are limited to simple concept related questions — “What is C?” or “Which of the following is an example of C?” (where $C$ is a concept symbol) — or analogy type questions involving roles. There are no efforts in the literature which make use of the terminological axioms in the ontology such as existential, universal and cardinality restrictions on concepts and roles for MCQ generation. Also, there are no systematic methods for generating incorrect answers (distractors) from ontologies. Distractor generation process has to be given much importance, since the generated distractors determine the quality and hardness of an MCQ. We propose two new MCQ generation approaches, which generate MCQs that are very useful and realistic in conducting assessment tests, and the corresponding distractor generating techniques. Our distractor generation techniques, unlike other methods, consider the open-world assumption, so that the generated MCQs will always be valid (falsity of distractors is ensured). Furthermore, we present a measure to determine the difficulty level (a value between 0 and 1) of the generated MCQs. The proposed system is implemented, and experiments on specific ontologies have shown the effectiveness of the approaches. We also did an empirical study by generating question items from a real-world ontology and validated our results with the help of domain experts.     

NKI中的本体、框架和逻辑理论

NKI(国家知识基础设施)是一个大规模知识库,它用框架来表示本体中的概念,用Hom逻辑程序作为自动推理.给出NKI中的本体、框架和逻辑理论的形式表示以及形式表示之间的转换,并证明如果将本体、框架和逻辑理论看作是3个范畴,则这些转换是这3个范畴之间的函子.这个结果保证了在NKI中,基于Horn逻辑程序的推理关于用本体和框架表示的知识库是正确的.     

Ontologies application in organizational learning: A literature review

Although ontologies and organizational learning are issues that have been discussed for many years, there is not an approach on literature that gives an overview about how both issues have been applied together. This literature review has the objective of exploring how ontologies are being applied in the organizational learning process recently; as a consequence, only studies from the year of 2005 onwards have been searched. The identification process produced 353 papers from 11 different databases. After applying the exclusion criteria, the set was reduced to 11 papers, which clearly fitted to the criteria defined for accomplishment of the systematic review, which were then analyzed and classified. The papers have been classified according to the structure and level of the ontologies. Furthermore, the Information Technology (IT) used in conjunction with ontology was identified, as well as the way ontologies and IT can act as a means of facilitating the organizational learning process. It was observed that although ontologies are rather important, a very few number of researches have applied ontologies in the organizational learning processes. In a general way, ontologies and IT encourage the sharing of knowledge and formalization.     

Ontologies are not the Panacea in Data Integration: A Flexible Coordinator to Mediate Context Construction

Shared ontologies describe concepts and relationships to resolve semantic conflicts amongst users accessing multiple autonomous and heterogeneous information sources. We contend that while ontologies are useful in semantic reconciliation, they do not guarantee correct classification of semantic conflicts, nor do they provide the capability to handle evolving semantics or a mechanism to support a dynamic reconciliation process. Their limitations are illustrated through a conceptual analysis of several prominent examples used in heterogeneous database systems and in natural language processing. We view semantic reconciliation as a nonmonotonic query-dependent process that requires flexible interpretation of query context, and as a mechanism to coordinate knowledge elicitation while constructing the query context. We propose a system that is based on these characteristics, namely the SCOPES (Semantic Coordinator Over Parallel Exploration Spaces) system. SCOPES takes advantage of ontologies to constrain exploration of a remote database during the incremental discovery and refinement of the context within which a query can be answered. It uses an Assumption-based Truth Maintenance System (ATMS) to manage the multiple plausible contexts which coexist while the semantic reconciliation process is unfolding, and the Dempster-Shafer (DS) theory of belief to model the likelihood of these plausible contexts.     

Supporting the discovery and labeling of non-taxonomic relationships in ontology learning

Ontology learning (OL) from texts has been suggested as a technology that helps to reduce the bottleneck of knowledge acquisition in the construction of domain ontologies. In this learning process, the discovery, and possibly also labeling, of non-taxonomic relationships has been identified as one of the most difficult and often neglected problems. In this paper, we propose a technique that addresses this issue by analyzing a domain text corpus to extract verbs frequently applied for linking certain pairs of concepts. Integrated in an ontology building process, this technique aims to reduce the work-load of knowledge engineers and domain experts by suggesting candidate relationships that might become part of the ontology as well as prospective labels for them.     

A methodology for constructing of philosophy ontology based on philosophical texts

An ontology is a crucial factor for the success of the Semantic Web and other knowledge-based systems in terms of share and reuse of domain knowledge. However, there are a few concrete ontologies within actual knowledge domains including learning domains. In this paper, we develop an ontology which is an explicit formal specification of concepts and semantic relations among them in philosophy. We call it a philosophy ontology. Our philosophy is a formal specification of philosophical knowledge including knowledge of contents of classical texts of philosophy. We propose a methodology, which consists of detailed guidelines and templates, for constructing text-based ontology. Our methodology consists of 3 major steps and 14 minor steps. To implement the philosophy ontology, we develop an ontology management system based on Topic Maps. Our system includes a semi-automatic translator for creating Topic Map documents from the output of conceptualization steps and other tools to construct, store, retrieve ontologies based on Topic Maps. Our methodology and tools can be applied to other learning domain ontologies, such as history, literature, arts, and music.     

A semantic metrics suite for evaluating modular ontologies

Ontologies, which are formal representations of knowledge within a domain, can be used for designing and sharing conceptual models of enterprises information for the purpose of enhancing understanding, communication and interoperability. For representing a body of knowledge, different ontologies may be designed. Recently, designing ontologies in a modular manner has emerged for achieving better reasoning performance, more efficient ontology management and change handling. One of the important challenges in the employment of ontologies and modular ontologies in modeling information within enterprises is the evaluation of the suitability of an ontology for a domain and the performance of inference operations over it. In this paper, we present a set of semantic metrics for evaluating ontologies and modular ontologies. These metrics measure cohesion and coupling of ontologies, which are two important notions in the process of assessing ontologies for enterprise modeling. The proposed metrics are based on semantic-based definitions of relativeness, and dependencies between local symbols, and also between local and external symbols of ontologies. Based on these semantic definitions, not only the explicitly asserted knowledge in ontologies but also the implied knowledge, which is derived through inference, is considered for the sake of ontology assessment. We present several empirical case studies for investigating the correlation between the proposed metrics and reasoning performance, which is an important issue in applicability of employing ontologies in real-world information systems.     

A hybrid evolutionary approach for solving the ontology alignment problem

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so‐called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high‐quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc.     

Learning about evaluation and assessment: teacher's use of folksonomies and ontologies in an online narrative environment

Building ontology in the domain of human sciences can be a difficult process because of the different meanings given to the same key concepts in these disciplines: in fact, shared meaning is an important element in knowledge construction between members of a community. In this paper, we propose a participatory social environment called ‘EduOntoWiki’ where academic experts in the field of educational sciences develop lightweight ontologies that members of multiple communities of practice (teachers, trainers, etc.) can modify and integrate using a folksonomic and storytelling approach. In this way, real‐life narrative contexts become precious ‘tagged’ alternative representations that can contribute to the ontology construction process. Experience of the environment gave possible indications regarding its use in teachers' training courses and as a useful tool to discuss evaluation and assessment issues in teachers' professional practice.     

Extracting focused knowledge from the semantic web

Ontologies are increasingly being recognized as a critical component in making networked knowledge accessible. Software architectures which can assemble knowledge from networked sources coherently according to the requirements of a particular task or perspective will be at a premium in the next generation of web services. We argue that the ability to generate task-relevant ontologies efficiently and relate them to web resources will be essential for creating a machine-inferencable “semantic web”. The Internet-based multi-agent problem solving (IMPS) architecture described here is designed to facilitate the retrieval, restructuring, integration and formalization of task-relevant ontological knowledge from the web. There are rich structured and semi-structured sources of knowledge available on the web that present implicit or explicit ontologies of domains. Knowledge-level models of tasks have an important role to play in extracting and structuring useful focused problem-solving knowledge from these web sources. IMPS uses a multi-agent architecture to combine these models with a selection of web knowledge extraction heuristics to provide clean syntactic integration of ontological knowledge from diverse sources and support a range of ontology merging operations at the semantic level. Whilst our specific aim is to enable on-line knowledge acquisition from web sources to support knowledge-based problem solving by a community of software agents encapsulating problem-sloving inferences, the techniques described here can be applied to more general task-based integration of knowledge from diverse web sources, and the provision of services such as the critical comparison, fusion, maintenance and update of both formal informal ontologies.