A knowledge encapsulation approach to ontology modularization

The development of monolithic ontologies for complex domains may face various challenges in reasoning and implementation. The notion of modularity can be employed for developing more efficient ontologies, especially in distributed environments. In this paper, we introduce a framework for developing ontologies in a modular manner. We describe the interface-based modular ontology formalism, (IBF), which theoretically supports the framework. The main feature of the framework is its support for knowledge encapsulation, i.e., it allows ontologies to define their main content using well-defined interfaces, such that their knowledge bases can only be accessed by other ontologies through these interfaces. An important implication of the proposed framework is that ontology modules can be developed completely independent of each other’s signature and languages. Such modules are free to only utilize the required knowledge segments of the others. We also investigate the issues of inconsistency in the proposed modular ontology framework. We provide solutions for isolating inconsistent ontology modules from the other parts of a modular ontology and also resolve inconsistencies which may be arisen by integrating consistent knowledge bases.     

Building linked ontologies with high precision using subclass mapping discovery

The creation of links between schemas of published datasets is a key part of the Linked Open Data (LOD) paradigm. The ability to discover these links “on the go” requires that ontology matching techniques achieve good precision and recall within acceptable execution times. In this paper, we add similarity-based and mediator-based ontology matching methods to the Agreementmaker ontology matching system, which aim to efficiently discover high precision subclass mappings between LOD ontologies. Similarity-based matching methods discover subclass mappings by extrapolating them from a set of high quality equivalence mappings and from the interpretation of compound concept names. Mediator-based matching methods discover subclass mappings by comparing polysemic lexical annotations of ontology concepts and by considering external web ontologies. Experiments show that when compared with a leading LOD approach, Agreementmaker achieves considerably higher precision and F-measure, at the cost of a slight decrease in recall.     

Induction of robust classifiers for web ontologies through kernel machines

The paper focuses on the task of approximate classification of semantically annotated individual resources in ontological knowledge bases. The method is based on classification models built through kernel methods, a well-known class of effective statistical learning algorithms. Kernel functions encode a notion of similarity among elements of some input space. The definition of a family of parametric language-independent kernel functions for individuals occurring in an ontology allows the application of these statistical learning methods on Semantic Web knowledge bases. The classification models induced by kernel methods offer an alternative way to classify individuals with respect to the typical exact and approximate deductive reasoning procedures. The proposed statistical setting enables further inductive approaches to a variety of other tasks that can better cope with the inherent incompleteness of the knowledge bases in the Semantic Web and with their potential incoherence due to their distributed nature. The effectiveness of the proposed method is empirically proved through experiments on the task of approximate classification with real ontologies collected from standard repositories.     

领域Ontology的自动丰富——基于ADL地名表的实例研究

本文以一个地理特征词表（Feature Type Thesaurus，F1T）为研究实例，提出了一种对领域Ontology进行自动丰富的方法。FTT描述了200多种地理特征类型，依照等级结构组织，用于标引和组织美国亚历山大数字图书馆地名表（ADL Gazetteer）中的6百万个地名。为了对F1可进行自动丰富，（1）首先从地名中抽取和发现有检索价值的、表示地理特征类型的通用词；（2）根据它们和标引主题词间的同现关系，在相同词族词汇的聚类过程中，确定与之相对应的主题词，进而将提取出的通用词定位到F1T的等级结构中。充分利用已经存在的大量标引语料，实现通用词的定位分析是核心内容，并且实验结果证明有效性达到82．7％。这项研究的实质是从Ontology标引的语料库中自动提取领域知识和标引知识，达到对Ontology的自动丰富。这一方法可以应用到类似的语料库和知识库上，实现新术语的发现、Ontology自丰富及其互操作。     

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.     

Axiom-based ontology matching

Abstract: Managing multiple ontologies is now a core question in most of the applications that require semantic interoperability. The semantic web is surely the most significant application of this report: the current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains. In this paper, we introduce a new approach of ontology matching named axiom-based ontology matching. As this approach is founded on the use of axioms, it is mainly dedicated to heavyweight ontologies, but it can also be applied to lightweight ontologies as a complementary approach to the current techniques based on the analysis of natural language expressions, instances and/or taxonomical structures of ontologies. This new matching paradigm is defined in the context of the conceptual graphs model, where the projection (i.e. the main operator for reasoning with conceptual graphs which corresponds to homomorphism of graphs) is used as a means to semantically match the concepts and the relations of two ontologies through the explicit representation of the axioms in terms of conceptual graphs. We also introduce an ontology of representation, called MetaOCGL, dedicated to the reasoning of heavyweight ontologies at the meta-level.     

语义集成:本体映射方法研究

本体是客观世界知识的表现形式,随着语义Web研究的深入,研究者们构建了越来越多的本体,如何实现本体之间的知识共享和重用,成为了语义Web发展的关键。文中对本体映射的方法进行了研究,系统阐述了本体及本体映射的定义、本体映射中的相似度计算和本体映射框架等。如何减少本体映射中的人工干预,实现本体的半自动化或自动化映射将是该领域的发展方向。     

Using JessTab to integrate Protege and Jess

Integration with external systems, such as problem solvers, is becoming increasingly important for ontology development and knowledge-modeling tools. The author's JessTab extension lets you write Jess programs that manage Protege ontologies and knowledge bases. Protege is a popular, modular ontology development and knowledge acquisition tool.     

A high-level electrical energy ontology with weighted attributes

One of the significant application areas of domain ontologies is known to be text analysis applications like information extraction and text classification systems, and semantic portals. In this paper, we present a high-level ontology for the electrical energy domain. This domain ontology has weighted attributes to cover the inherent fuzziness in the textual representations of its concepts. Additionally, we have included in the ontology the necessary attributes to align the ontology concepts to on-line collaborative knowledge bases like Wikipedia and linked open data sources like DBpedia, other attributes to facilitate its use in multilingual applications, and concepts to hold the named entities in the domain. The ultimate ontology is aligned with the previously proposed ontologies for the energy-related subdomains after extending the latter ones with weighted attributes. We make the ultimate form of the electrical energy ontology, as well as the extended versions of the domain ontologies for the subdomains, available for research purposes. Also included in the paper are sample text analysis applications which mainly exploit the weighted attributes within the ontology.     

Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.     

A Compact Co-Evolutionary Algorithm for sensor ontology meta-matching

With the proliferation of sensors, semantic web technologies are becoming closely related to sensor network. The linking of elements from semantic web technologies with sensor networks is called semantic sensor web whose main feature is the use of sensor ontologies. However, due to the subjectivity of different sensor ontology designer, different sensor ontologies may define the same entities with different names or in different ways, raising so-called sensor ontology heterogeneity problem. There are many application scenarios where solving the problem of semantic heterogeneity may have a big impact, and it is urgent to provide techniques to enable the processing, interpretation and sharing of data from sensor web whose information is organized into different ontological schemes. Although sensor ontology heterogeneity problem can be effectively solved by Evolutionary Algorithm (EA)-based ontology meta-matching technologies, the drawbacks of traditional EA, such as premature convergence and long runtime, seriously hamper them from being applied in the practical dynamic applications. To solve this problem, we propose a novel Compact Co-Evolutionary Algorithm (CCEA) to improve the ontology alignment’s quality and reduce the runtime consumption. In particular, CCEA works with one better probability vector (PV) \(PV_{better}\) and one worse PV \(PV_{worse}\), where \(PV_{better}\) mainly focuses on the exploitation which dedicates to increase the speed of the convergence and \(PV_{worse}\) pays more attention to the exploration which aims at preventing the premature convergence. In the experiment, we use Ontology Alignment Evaluation Initiative (OAEI) test cases and two pairs of real sensor ontologies to test the performance of our approach. The experimental results show that CCEA-based ontology matching approach is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and compared with the state-of-the-art sensor ontology matching systems, CCEA-based ontology matching approach can significantly improve the ontology alignment’s quality.     

An algebraic axiomatization of the Ewald’s intuitionistic tense logic

Ewald (J Symbolic Logic 51(1):166–179, 1986) considered tense operators \(G\) , \(H\) , \(F\) and \(P\) on intuitionistic propositional calculus and constructed an intuitionistic tense logic system called IKt. The aim of this paper is to give an algebraic axiomatization of the IKt system. We will also show that the algebraic axiomatization given by Chajda (Cent Eur J Math 9(5):1185–1191, 2011) of the tense operators \(P\) and \(F\) in intuitionistic logic is not in accordance with the Halmos definition of existential quantifiers. In this paper, we will study the IKt variety of IKt-algebras. First, we will introduce some examples and we will prove some properties. Next, we will prove that the IKt system has IKt-algebras as algebraic counterpart. We will also describe a discrete duality for IKt-algebras bearing in mind the results indicated by Or?owska and Rewitzky (Fundam Inform 81(1–3):275–295, 2007) for Heyting algebras. We will also get a general construction of tense operators on a complete Heyting algebra, which is a power lattice via the so-called Heyting frame. Finally, we will introduce the notion of tense deductive system which allowed us both to determine the congruence lattice in an IKt-algebra and to characterize simple and subdirectly irreducible algebras of the IKt variety.     

Addressing the Ontology Acquisition Bottleneck Through Reverse Ontological Engineering

The use of ontologies in knowledge engineering arose as a solution to the difficulties associated with acquiring knowledge, commonly referred to as the knowledge acquisition bottleneck. The knowledge-level model represented in an ontology provides a much more structured and principled approach compared with earlier transfer-of-symbolic-knowledge approaches but brings with it a new problem, which can be termed the ontology-acquisition (and maintenance) bottleneck. Each ontological approach offers a different structure, different terms and different meanings for those terms. The unifying theme across approaches is the considerable effort associated with developing, validating and connecting ontologies. We propose an approach to engineering ontologies by retrospectively and automatically discovering them from existing data and knowledge sources in the organization. The method offered assists in the identification of similar and different terms and includes strategies for developing a shared ontology. The approach uses a human-centered, concept-based knowledge processing technique, known as formal concept analysis, to generate an ontology from examples. To assist classification of examples and to identify the salient features of the example, we use a rapid and incremental knowledge acquisition and representation technique, known as ripple-down rules. The method can be used as an alternative or complement to other approaches.     

Thesaurus or Logical Ontology,Which One Do We Need for Text Mining?

Ontologies are recognised as important tools, not only for effective and efficient information sharing, but also for information extraction and text mining. In the biomedical domain, the need for a common ontology for information sharing has long been recognised, and several ontologies are now widely used. However, there is confusion among researchers concerning the type of ontology that is needed for text mining , and how it can be used for effective knowledge management, sharing, and integration in biomedicine. We argue that there are several different ways to define an ontology and that, while the logical view is popular for some applications, it may be neither possible nor necessary for text mining. We propose a text-centered approach for knowledge sharing, as an alternative to formal ontologies. We argue that a thesaurus (i.e. an organised collection of terms enriched with relations) is more useful for text mining applications than formal ontologies.