Showing papers on "Suggested Upper Merged Ontology published in 2002"

Measuring Similarity between Ontologies

Abstract: Ontologies now play an important role for many knowledge-intensive applications for which they provide a source of precisely defined terms However, with their wide-spread usage there come problems concerning their proliferation Ontology engineers or users frequently have a core ontology that they use, eg, for browsing or querying data, but they need to extend it with, adapt it to, or compare it with the large set of other ontologies For the task of detecting and retrieving relevant ontologies, one needs means for measuring the similarity between ontologies We present a set of ontology similarity measures and a multiple-phase empirical evaluation

OntoEdit: Collaborative Ontology Development for the Semantic Web

Abstract: Ontologies now play an important role for enabling the semantic web. They provide a source of precisely defined terms e.g. for knowledge-intensive applications. The terms are used for concise communication across people and applications. Typically the development of ontologies involves collaborative efforts of multiple persons. OntoEdit is an ontology editor that integrates numerous aspects of ontology engineering. This paper focuses on collaborative development of ontologies with OntoEdit which is guided by a comprehensive methodology.

Data modelling versus ontology engineering

Abstract: Ontologies in current computer science parlance are computer based resources that represent agreed domain semantics. Unlike data models, the fundamental asset of ontologies is their relative independence of particular applications, i.e. an ontology consists of relatively generic knowledge that can be reused by different kinds of applications/tasks. The first part of this paper concerns some aspects that help to understand the differences and similarities between ontologies and data models. In the second part we present an ontology engineering framework that supports and favours the genericity of an ontology. We introduce the DOGMA ontology engineering approach that separates "atomic" conceptual relations from "predicative" domain rules. A DOGMA ontology consists of an ontology base that holds sets of intuitive context-specific conceptual relations and a layer of "relatively generic" ontological commitments that hold the domain rules. This constitutes what we shall call the double articulation of a DOGMA ontology 1.

The Suggested Upper Merged Ontology: A Large Ontology for the Semantic Web and its Applic ations

Abstract: In this paper we discuss the development and application of a large formal ontology to the semantic web The Suggested Upper Merged Ontology (SUMO) (Niles & Pease, 2001) (SUMO, 2002) is a “starter document” in the IEEE Standard Upper Ontology effort This upper ontology is extremely broad in scope and can serve as a semantic foundation for search, interoperation, and communication on the semantic web

Semantic Similarity Measures as Tools for Exploring the Gene Ontology

Abstract: Many bioinformatics resources hold data in the form of sequences. Often this sequence data is associated with a large amount of annotation. In many cases this data has been hard to model, and has been represented as scientific natural language, which is not readily computationally amenable. The development of the Gene Ontology provides us with a more accessible representation of some of this data. However it is not clear how this data can best be searched, or queried. Recently we have adapted information content based measures for use with the Gene Ontology (GO). In this paper we present detailed investigation of the properties of these measures, and examine various properties of GO, which may have implications for its future design.

Ontology Research and Development. Part 1-A Review of Ontology Generation.

Abstract: Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next wave of web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. The second complementary survey is dedicated to ontology mapping and ontology ‘evolving’. Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extrac...

Method and system for collaborative ontology modeling

Abstract: A distributed ontology system including a central computer comprising a global ontology directory, a plurality of ontology server computers, each including a repository of class and relation definitions, and a server for responding to queries relating to class and relation definitions in the repository, and a computer network connecting the central computer with the plurality of ontology server computers. A method is also described and claimed.

Ontology research and development. Part 2 - a review of ontology mapping and evolving

Abstract: This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far ...

Ontology construction for information selection

Abstract: Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while ensuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).

Integrated approach to Web ontology learning and engineering

Abstract: Developing the Semantic Web, seeking to improve the semantic awareness of computers connected via the Internet, requires a systematic, computer-oriented world representation. Researchers often refer to such a model as an ontology. Despite the work done on them in recent years, ontologies have yet to be widely applied and used. Research has devoted only limited attention to such practical issues as techniques and tools aimed at an ontology's actual construction and content. The authors have built a software environment, centered around the OntoLearn tool, which can build and assess a domain ontology for intelligent information integration within a virtual user community. OntoLearn has already been tested in two European projects, where it functioned as the basis for a semantic interoperability platform used by small- and medium-sized tourism enterprises. Further, developers can easily adapt OntoLearn to work with other general-purpose ontologies.

Evaluating Ontology-Mapping Tools: Requirements and Experience.

Abstract: The appearance of a large number of ontology tools may leave a user looking for an appropriate tool overwhelmed and uncertain on which tool to choose. Thus evaluation and comparison of these tools is important to help users determine which tool is best suited for their tasks. However, there is no “one size fits all” comparison framework for ontology tools: different classes of tools require very different comparison frameworks. For example, ontology-development tools can easily be compared to one another since they all serve the same task: define concepts, instances, and relations in a domain. Tools for ontology merging, mapping, and alignment however are so different from one another that direct comparison may not be possible. They differ in the type of input they require (e.g., instance data or no instance data), the type of output they produce (e.g., one merged ontology, pairs of related terms, articulation rules), modes of interaction and so on. This diversity makes comparing the performance of mapping tools to one another largely meaningless. We present criteria that partition the set of such tools in smaller groups allowing users to choose the set of tools that best fits their tasks. We discuss what resources we as a community need to develop in order to make performance comparisons within each group of merging and mapping tools useful and effective. These resources will most likely come as results of evaluation experiments of stand-alone tools. As an example of such an experiment, we discuss our experiences and results in evaluating PROMPT, an interactive ontology-merging tool. Our experiment produced some of the resources that we can use in more general evaluation. However, it has also shown that comparing the performance of different tools can be difficult since human experts do not agree on how ontologies should be merged, and we do not yet have a good enough metric for comparing ontologies. 1 Ontology-Mapping Tools Versus Ontology-Development Tools Consider two types of ontology tools: (1) tools for developing ontologies and (2) tools for mapping, aligning, or merging ontologies. By ontology-development tools (which we will call development tools in the paper) we mean ontology editors that allow users to define new concepts, relations, and instances. These tools usually have capabilities for importing and extending existing ontologies. Development tools may include graphical browsers, search capabilities, and constraint checking. Protégé-2000 [17], OntoEdit [19], OilEd [2], WebODE [1], and Ontolingua [7] are some examples of development tools. Tools for mapping, aligning, and merging ontologies (which we will call mapping tools) are the tools that help users find similarities and differences between source ontologies. Mapping tools either identify potential correspondences automatically or provide the environment for the users to find and define these correspondences, or both. Mapping tools are often extensions of development tools. Mapping tool and algorithm examples include PROMPT[16], ONION [13], Chimaera [11], FCA-Merge [18], GLUE [5], and OBSERVER [12]. Even though theories on how to evaluate either type of tools are not well articulated at this point, there are already several frameworks for evaluating ontologydevelopment tools. For example, Duineveld and colleagues [6] in their comparison experiment used different development tools to represent the same domain ontology. Members of the Ontology-environments SIG in the OntoWeb initiative designed an extensive set of criteria for evaluating ontology-development tools and applied these criteria to compare a number of projects. Some of the aspects that these frameworks compare include: – interoperability with other tools and the ability to import and export ontologies in different representation languages; – expressiveness of the knowledge model; – scalability and extensibility; – availability and capabilities of inference services; – usability of the tools. Let us turn to the second class of ontology tools: tools for mapping, aligning, or merging ontologies. It is tempting to reuse many of the criteria from evaluation of development tools. For example, expressiveness of the underlying language is important and so is scalability and extensibility. We need to know if a mapping tool can work with ontologies from different languages. However, if we look at the mapping tools more closely, we see that their comparison and evaluation must be very different from the comparison and evaluation of development tools. All the ontology-development tools have very similar inputs and the desired outputs: we have a domain, possibly a set of ontologies to reuse, and a set of requirements for the ontology, and we need to use a tool to produce an ontology of that domain satisfying the requirements. Unlike the ontology-development tools, the 1 http://delicias.dia.fi.upm.es/ontoweb/sig-tools/ ontology-mapping tools vary with respect to the precise task that they perform, the inputs on which they operate and the outputs that they produce. First, the tasks for which the mapping tools are designed, differ greatly. On the one hand, all the tools are designed to find similarities and differences between source ontologies in one way or another. In fact, researchers have suggested a uniform framework for describing and analyzing this information regardless of what the final task is [3, 10]. On the other hand, from the user’s point of view the tools differ greatly in what tasks this analysis of similarities and differences supports. For example, Chimaera and PROMPT allow users to merge source ontologies into a new ontology that includes concepts from both sources. The output of ONION is a set of articulation rules between two ontologies; these rules define what the similarities and differences are. The articulation rules can later be used for querying and other tasks. The task of GLUE, AnchorPROMPT [14] and FCA-Merge is to provide a set of pairs of related concepts with some certainty factor associated with each pair. Second, different mapping tools rely on different inputs: Some tools deal only with class hierarchies of the sources and are agnostic in their merging algorithms about slots or instances (e.g., Chimaera). Other tools use not only classes but also slots and value restrictions in their analysis (e.g., PROMPT). Other tools rely in their algorithms on the existence of instances in each of the source ontologies (e.g., GLUE). Yet another set of tools require not only that instances are present, but also that source ontologies share a set of instances (e.g., FCA-Merge). Some tools work independently and produce suggestions to the user at the end, allowing the user to analyze the suggestions (e.g., GLUE, FCAMerge). Some tools expect that the source ontologies follow a specific knowledgerepresentation paradigm (e.g., Description Logic for OBSERVER). Other tools rely heavily on interaction with the user and base their analysis not only on the source ontologies themselves but also on the merging or alignment steps that the user performs (e.g., PROMPT, Chimaera). Third, since the tasks that the mapping tools support differ greatly, the interaction between a user and a tool is very different from one tool to another. Some tools provide a graphical interface which allows users to compare the source ontologies visually, and accept or reject the results of the tool analysis (e.g., PROMPT, Chimaera, ONION), the goal of other tools is to run the algorithms which find correlations between the source ontologies and output the results to the user in a text file or on the terminal–the users must then use the results outside the tool itself. The goal of this paper is to start a discussion on a framework for evaluating ontology-mapping tools that would account for this great variety in underlying assumptions and requirements. We argue that many of the tools cannot be compared directly with one another because they are so different in the tasks that they support. We identify the criteria for determining the groups of tools that can be compared directly, define what resources we need to develop to make such comparison possible and discuss our experiences in evaluating our merging tool, PROMPT, as well as the results of this evaluation. 2 Requirements for Evaluating Mapping Tools Before we discuss the evaluation requirements for mapping tools, we must answer the following question which will certainly affect the requirements: what is the goal of such potential evaluation? It is tempting to say “find the best tool.” However, as we have just discussed, given the diversity in the tasks that the tools support, their modes of interaction, the input data they rely on, it is impossible to compare the tools to one another and to find one or even several measures to identify the “best” tool. Therefore, we suggest that the questions driving such evaluation must be user-oriented. A user may ask either what is the best tool for his task or whether a particular tool is good enough for his task. Depending on what the user’s source ontologies are, how much manual work he is willing to put in, how important the precision of the results is, one or another tool will be more appropriate. Therefore, the first set of evaluation criteria are pragmatic criteria. These criteria include but are not limited to the following: Input requirements What elements from the source ontologies does the tool use? Which of these elements does the tool require? This information may include: concept names, class hierarchy, slot definitions, facet values, slot values, instances. Does the tool require that source ontologies use a particular knowledge-representation paradigm? Level of user interaction Does the tool perform the comparison in a “batch mode,” presenting the results at the end, or is it an interactive tool where intermediate results are analyzed by the user, and the tool uses the feedback for further analysis? Type o

OntoEdit: Guiding Ontology Development by Methodology and Inferencing

Abstract: Ontologies now play an important role for many knowledge-intensive applications for which they provide a source of precisely defined terms. The terms are used for concise communication across people and applications. OntoEdit is an ontology editor that has been developed keeping five main objectives in mind: 1. Ease of use. 2. Methodology-guided development of ontologies. 3. Ontology development with help of inferencing. 4. Development of ontology axioms. 5. Extensibility through plug-in structure. This paper is about the first four of these items.

1. Ontology Learning Part One On Discovering Taxonomic Relations from the Web

Abstract: Ontologies may help to facilitate the finding and use of Web information. However, the engineering of an ontology may turn out to be expensive and time-consuming. Therefore, we exploit ontology learning techniques that automate ontology engineering to some extent. In this chapter, we focus on the learning of the taxonomic backbone of ontologies, presenting a survey on algorithms as well as on some new ideas that consider the structure of existing ontology parts. Eventually, we describe an evaluation of our proposal and give concrete results.

Formal Ontology Engineering in the DOGMA Approach

Abstract: This paper presents a specifically database-inspired approach (called DOGMA) for engineering formal ontologies, implemented as shared resources used to express agreed formal semantics for a real world domain. We address several related key issues, such as knowledge reusability and shareability, scalability of the ontology engineering process and methodology, efficient and effective ontology storage and management, and coexistence of heterogeneous rule systems that surround an ontology mediating between it and application agents. Ontologies should represent a domain's semantics independently from "language", while any process that creates elements of such an ontology must be entirely rooted in some (natural) language, and any use of it will necessarily be through a (in general an agent's computer) language.To achieve the claims stated, we explicitly decompose ontological resources into ontology bases in the form of simple binary facts called lexons and into socalled ontological commitments in the form of description rules and constraints. Ontology bases in a logic sense, become "representationless" mathematical objects which constitute the range of a classical interpretation mapping from a first order language, assumed to lexically represent the commitment or binding of an application or task to such an ontology base. Implementations of ontologies become database-like on-line resources in the model-theoretic sense. The resulting architecture allows to materialize the (crucial) notion of commitment as a separate layer of (software agent) services, mediating between the ontology base and those application instances that commit to the ontology. We claim it also leads to methodological approaches that naturally extend key aspects of database modeling theory and practice. We discuss examples of the prototype DOGMA implementation of the ontology base server and commitment server.

Extending a Lexical Ontology by a Combination of Distributional Semantics Signatures

Abstract: Ontologies are a tool for Knowledge Representation that is now widely used, but the effort employed to build an ontology is high. We describe here a procedure to automatically extend an ontology such as WordNet with domain-specific knowledge. The main advantage of our approach is that it is completely unsupervised, so it can be applied to different languages and domains. Our experiments, in which several domain-specific concepts from a book have been introduced, with no human supervision, into WordNet, have been successful.

Information-Flow-Based Ontology Mapping

Abstract: As ontologies become ever more important for semanticallyrich information exchange and a crucial element for supporting knowledge sharing in a large distributed environment, like the Web, the demand for sharing them increases accordingly. One way of achieving this ambitious goal is to provide mechanised ways for mapping and merging ontologies. This has been the focus of recent research in knowledge engineering. However, we observe a dearth of mapping methods that are based on a strong theoretical ground, are easy to replicate in different settings, and use semantically-rich mechanisms for performing ontology mapping. In this paper, we aim to fill in these gaps with a method we propose for Information-Flow-based ontology mapping. Our method draws on the proven theoretical ground of Information Flow and channel theory, and we provide a systematic and mechanised methodology for deploying it on a distributed environment to perform ontology mapping among a variety of different ontologies. We applied our method at a large-scale experiment of mapping five ontologies modelling Computer Science departments in five UK Universities. We elaborate on a theory for ontology mapping, analyse the mechanised steps of applying it, and assess its ontology mapping results.

Ontology Evolution: Not the Same as Schema Evolution In: Knowledge and Information Systems

Abstract: As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.

Fine-grained proper noun ontologies for question answering

Abstract: The WordNet lexical ontology, which is primarily composed of common nouns, has been widely used in retrieval tasks. Here, we explore the notion of a fine-grained proper noun ontology and argue for the utility of such an ontology in retrieval tasks. To support this claim, we build a fine-grained proper noun ontology from unrestricted news text and use this ontology to improve performance on a question answering task.

Ontology Evolution within Ontology Editors.

Abstract: An ontology over a period of time needs to be modified to reflect changes in the real world, changes in the user’s requirements, drawbacks in the initial design, to incorporate additional functionality or to allow for incremental improvement. Although changes are inevitable during the development and deployment of an ontology, most of the current ontology editors unfortunately do not provide enough support for efficient copying with changes. Since changes are the force that drives the evolution process, in this paper we discuss the requirements for the ontology editors in order to support ontology evolution.

Ontology Engineering: a Survey and a Return on Experience

Abstract: Ontology is a new object of IA that recently came to maturity and a powerful conceptual tool of Knowledge Modeling. It provides a coherent base to build on, and a shared reference to align with, in the form of a consensual conceptual vocabulary, on which one can build descriptions and communication acts. This report presents the object that is called "an ontology" and a state of the art of engineering techniques for ontologies. Then it describes a project for which we developed an ontology and used it to improve knowledge management. Finally it describes the design process and discuss the resulting ontology.

Overview of Ontology

Abstract: Ontology is defined as an explicit formal specification of a shared conceptualization.It can provide semantic meaning through relations between concepts.As a fine model for presenting hierarchy and semantic meaning of concepts,Ontology is widely concerned and extensively applied to many fields in computer science and technology.With regard to little research on ontology in China,The state of the art of ontology is surveyed in this paper.This work first analyzes connotation and methodology of ontology,and then analyzes its applications in information system in details.The paper ends with a short conclusion and future work.

User-Centred Ontology Learning for Knowledge Management

Abstract: Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained leaner to be used for further ontology modifications.

Evaluation of ontology merging tools in bioinformatics.

Abstract: Ontologies are being used nowadays in many areas, including bioinformatics. One of the issues in ontology research is the aligning and merging of ontologies. Tools have been developed for ontology merging, but they have not been evaluated for their use in bioinformatics. In this paper we evaluate two of the most well-known ontology merging tools with a bioinformatics perspective. As test ontologies we have used Gene Ontology and Signal-Ontology.

The Usable Ontology: An Environment for Building and Assessing a Domain Ontology

Abstract: Experience shows that the quality of the stored knowledge determines the success (therefore the effective usage) of an ontology. In fact, an ontology where relevant concepts are absent, or are not conformant to a domain view of a given community, will be scarcely used, or even disregarded. In this paper we present a method and a set of software tools aimed at supporting domain experts in populating a domain ontology and obtaining a shared consensus on its content. "Consensus" is achieved in an implicit and explicit way: implicitly, since candidate concepts are selected among the terms that are frequently and consistently referred in the documents produced by the virtual community of users; explicitly, through the use of a web-based groupware aimed at consensus building.

Ontology negotiation between intelligent information agents

Abstract: This paper describes an approach to ontology negotiation between agents supporting intelligent information management. Ontologies are declarative (data-driven) expressions of an agent's “world”: the objects, operations, facts and rules that constitute the logical space within which an agent performs. Ontology negotiation enables agents to cooperate in performing a task, even if they are based on different ontologies.Our objective is to increase the opportunities for “strange agents” – that is, agents not necessarily developed within the same framework or with the same contextual operating assumptions – to communicate in solving tasks when they encounter each other on the web. In particular, we have focused on information search tasks.We have developed a protocol that allows agents to discover ontology conflicts and then, through incremental interpretation, clarification and explanation, establish a common basis for communicating with each other. We have implemented this protocol in a set of Java classes that can be added to a variety of agents, irrespective of their underlying ontological assumptions. We have demonstrated the use of the protocol, through this implementation, in a test-bed that includes two large scientific archives: NASA's Global Change Master Directory and NOAA's Wind and Sea Index.This paper presents an overview of different methods for resolving ontology mismatches and motivates the Ontology Negotiation Protocol (ONP) as a method that addresses some problems with other approaches. Much remains to be done. The protocol must be tested in larger and less familiar contexts (for example, numerous archives that have not been preselected) and it must be extended to accommodate additional forms of clarification and ontology evolution.

User-centred ontology learning for knowledge management

Abstract: Automatic ontology building is a vital issue in many fields where they are currently built manually. This paper presents a user-centred methodology for ontology construction based on the use of Machine Learning and Natural Language Processing. In our approach, the user selects a corpus of texts and sketches a preliminary ontology (or selects an existing one) for a domain with a preliminary vocabulary associated to the elements in the ontology (lexicalisations). Examples of sentences involving such lexicalisation (e.g. ISA relation) in the corpus are automatically retrieved by the system. Retrieved examples are validated by the user and used by an adaptive Information Extraction system to generate patterns that discover other lexicalisations of the same objects in the ontology, possibly identifying new concepts or relations. New instances are added to the existing ontology or used to tune it. This process is repeated until a satisfactory ontology is obtained. The methodology largely automates the ontology construction process and the output is an ontology with an associated trained learner to be used for further ontology modifications.

Building a bioinformatics ontology using OIL

Abstract: This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualization is encoded using the ontology inference layer (OIL), a knowledge representation language that combines the modeling style of frame-based systems with the expressiveness and reasoning power of description logics (DLs). This paper is the second of a pair in this special issue. The first described the core of the OIL language and the need to use ontologies to deliver semantic bioinformatics resources. In this paper, the early stages of building an ontology component of a bioinformatics resource querying application are described. This ontology (TaO) holds the information about molecular biology represented in bioinformatics resources and the bioinformatics tasks performed over these resources. It, therefore, represents the metadata of the resources the application can query. It also manages the terminologies used in constructing the query plans used to retrieve instances from those external resources. The methodology used in this task capitalizes upon features of OIL-The conceptualization afforded by the frame-based view of OIL's syntax; the expressive power and reasoning of the logical formalism; and the ability to encode both handcrafted, hierarchies of concepts, as well as defining concepts in terms of their properties, which can then be used to establish a classification and infer relationships not encoded by the ontologist. This ability forms the basis of the methodology described here: For each portion of the TaO, a basic framework of concepts is asserted by the ontologist. Then, the properties of these concepts are defined by the ontologist and the logic's reasoning power used to reclassify and infer further relationships. This cycle of elaboration and refinement is iterated on each portion of the ontology until a satisfactory ontology has been created.