Where to publish and find ontologies? A survey of ontology libraries

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries affects their features, content, and potential exploitation in applications. From reviewing 11 ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address.     

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.     

Ontology based text indexing and querying for the semantic web

This publication shows how the gap between the HTML based internet and the RDF based vision of the semantic web might be bridged, by linking words in texts to concepts of ontologies. Most current search engines use indexes that are built at the syntactical level and return hits based on simple string comparisons. However, the indexes do not contain synonyms, cannot differentiate between homonyms (‘mouse’ as a pointing vs. ‘mouse’ as an animal) and users receive different search results when they use different conjugation forms of the same word. In this publication, we present a system that uses ontologies and Natural Language Processing techniques to index texts, and thus supports word sense disambiguation and the retrieval of texts that contain equivalent words, by indexing them to concepts of ontologies.

For this purpose, we developed fully automated methods for mapping equivalent concepts of imported RDF ontologies (for this prototype WordNet, SUMO and OpenCyc). These methods will thus allow the seamless integration of domain specific ontologies for concept based information retrieval in different domains.

To demonstrate the practical workability of this approach, a set of web pages that contain synonyms and homonyms were indexed and can be queried via a search engine like query frontend. However, the ontology based indexing approach can also be used for other data mining applications such text clustering, relation mining and for searching free text fields in biological databases. The ontology alignment methods and some of the text mining principles described in this publication are now incorporated into the ONDEX system http://ondex.sourceforge.net/.     

Automatic construction and enrichment of informal ontologies: A survey

The conceptualization of knowledge required for an efficient processing of textual data is usually represented as ontologies. Depending on the knowledge domain and tasks, different types of ontologies are constructed: formal ontologies, which involve axioms and detailed relations between concepts; taxonomies, which are hierarchically organized concepts; and informal ontologies, such as Internet encyclopedias created and maintained by user communities. Manual construction of ontologies is a time-consuming and costly process requiring the participation of experts; therefore, in recent years, there have appeared many systems that automate this process in a greater or lesser degree. This paper provides an overview of methods for automatic construction and enrichment of ontologies, with the focus being placed on informal ontologies.     

Ontology versioning in an ontology management framework

Ontologies have become ubiquitous in information systems. They constitute the semantic Web's backbone, facilitate e-commerce, and serve such diverse application fields as bioinformatics and medicine. As ontology development becomes increasingly widespread and collaborative, developers are creating ontologies using different tools and different languages. These ontologies cover unrelated or overlapping domains at different levels of detail and granularity. A uniform framework, which we present here, helps users manage multiple ontologies by leveraging data and algorithms developed for one tool in another. For example, by using an algorithm we developed for structural evaluation of ontology versions, this framework lets developers compare different ontologies and map similarities and differences among them. Multiple-ontology management includes these tasks: maintain ontology libraries, import and reuse ontologies, translate ontologies from one formalism to another, support ontology versioning, specify transformation rules between different ontologies and version, merge ontologies, align and map between ontologies, extract an ontology's self-contained parts, support inference across multiple ontologies, support query across multiple ontologies.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

Searching for arguments: applying a knowledge-based ontology of the legal domain

Legal ontologies are conceptual models of specific parts of the legal domain. They provide stable foundations for domain representation, essential to building legal information systems. The ontology presented in this article provides an analysis of the concept of knowledge in the legal domain. It is shown how new functions for information technology applications in the legal domain can be developed on the basis of this analysis. In particular, a search function distinguishing between the content and structure of legal arguments is described in order to show how the analysis is put into practice.     

Survey of modular ontology techniques and their applications in the biomedical domain

In the past several years, various ontologies and terminologies such as the Gene Ontology have been developed to enable interoperability across multiple diverse medical information systems. They provide a standard way of representing terms and concepts thereby supporting easy transmission and interpretation of data for various applications. However, with their growing utilization, not only has the number of available ontologies increased considerably, but they are also becoming larger and more complex to manage. Toward this end, a growing body of work is emerging in the area of modular ontologies where the emphasis is on either extracting and managing "modules" of an ontology relevant to a particular application scenario (ontology decomposition) or developing them independently and integrating into a larger ontology (ontology composition). In this paper, we investigate state-of-the-art approaches in modular ontologies focusing on techniques that are based on rigorous logical formalisms as well as well-studied graph theories. We analyze and compare how such approaches can be leveraged in developing tools and applications in the biomedical domain. We conclude by highlighting some of the limitations of the modular ontology formalisms and put forward additional requirements to steer their future development.     

Investigating ontology development for engineering design support

Ontologies are now in widespread use as a means of formalizing domain knowledge in a way that makes it accessible, shareable and reusable. Nevertheless, to many, the nature and use of ontologies are unfamiliar. This paper takes a practical approach - through the use of example - to clarifying what ontologies are and how they might be useful in an important and representative phase of the engineering design process, that of design requirement development and capture.The paper consists of two parts. In the first part ontologies and their use are discussed, and a methodology for developing ontologies is explored. In the second part, three very different types of ontology are developed in accordance with the methodology. Each of the ontologies captures a different conceptual facet of the engineering design domain, described at a quite different level of abstraction than the others. The process of developing ontologies is illustrated in a practical way and the application of these ontologies for supporting the capture of the engineering design requirement is described as a means of demonstrating the general potential of ontologies.     

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.     

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.     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

Experimentation with local consensus ontologies with implications for automated service composition

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.     

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.     

OBSERVER: An Approach for Query Processing in Global Information Systems Based on Interoperation Across Pre-Existing Ontologies

There has been an explosion in the types, availability and volume of data accessible in an information system, thanks to the World Wide Web (the Web) and related inter-networking technologies. In this environment, there is a critical need to replace or complement earlier database integration approaches and current browsing and keyword-based techniques with concept-based approaches. Ontologies are increasingly becoming accepted as an important part of any concept or semantics based solution, and there is increasing realization that any viable solution will need to support multiple ontologies that may be independently developed and managed. In particular, we consider the use of concepts from pre-existing real world domain ontologies for describing the content of the underlying data repositories. The most challenging issue in this approach is that of vocabulary sharing, which involves dealing with the use of different terms or concepts to describe similar information. In this paper, we describe the architecture, design and implementation of the OBSERVER system. Brokering across the domain ontologies is enabled by representing and utilizing interontology relationships such as (but not limited to) synonyms, hyponyms and hypernyms across terms in different ontologies. User queries are rewritten by using these relationships to obtain translations across ontologies. Well established metrics like precision and recall based on the extensions underlying the concepts are used to estimate the loss of information, if any.     

A visual tool for ontology alignment to enable geospatial interoperability

In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query. Using our approach, querying can be easily extended to new data sources and, therefore, to new regions. In this paper, we describe the AgreementMaker, a tool that displays the ontologies, supports several mapping layers visually, presents automatically generated mappings, and finally produces the agreements.     

Learning non-taxonomic relationships from web documents for domain ontology construction

In recent years, much effort has been put in ontology learning. However, the knowledge acquisition process is typically focused in the taxonomic aspect. The discovery of non-taxonomic relationships is often neglected, even though it is a fundamental point in structuring domain knowledge. This paper presents an automatic and unsupervised methodology that addresses the non-taxonomic learning process for constructing domain ontologies. It is able to discover domain-related verbs, extract non-taxonomically related concepts and label relationships, using the Web as corpus. The paper also discusses how the obtained relationships can be automatically evaluated against WordNet and presents encouraging results for several domains.