In recent years, as a knowledge-based discipline, bioinformatics has moved to make its knowledge more computationally amenable. After its beginnings in the disciplines as a technology advocated by computer scientists to overcome problems of heterogeneity, ontology has been taken up by the biologists themselves as a means to consistently annotate features from genotype to phenotype. In medical informatics, artifacts called ontologies have been used for a longer period of time to produce controlled lexicons for coding schemes. In this article, we review the current position in ontologies and how they have become institutionalized within biomedicine. As the field has matured, the much older philosophical aspects of ontology have come into play. With this and the institutionalization of ontology has come greater formality. We review this trend and what benefits it might bring to ontologies and their use within biomedicine. Author biographies:

This paper describes the initial stages of building an ontology of bioinformatics and molecular biology. The conceptualisation is encoded using the Ontology Inference Layer (OIL), a knowledge representation language that combines the modelling style of Frame-Based systems with the expressiveness and reasoning power of Description Logics. 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 applicationn are described. This ontology 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 capitalises upon features of OIL described in the first paper of this special issue -- The conceptualisation 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 hand-crafted, 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 frame-work 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 re-classify and ...

Traditional algorithms for description logic (DL) instance retrieval are inefficient for large amounts of underlying data. As description logic is becoming popular in areas such as the Semantic Web and information integration, it is very important to have systems that can reason efficiently over large data sets. In this paper we present an approach to transform description logic axioms described in the SHIQ DL language into a Prolog program. This transformation is done without any knowledge on the particular individuals: they are accessed dynamically during the normal Prolog execution of the generated program. This technique, together with the top-down Prolog execution, implies that only those pieces of data are accessed which are indeed important for answering the query. This allows us to store the individuals in a database instead of memory, which results in better scalability and helps using description logic ontologies directly on top of existing information sources. The transformation process consists of two steps: (1) first we create FOL clauses of restricted form from the DL axioms, (2) then we generate a Prolog program from these. Step (2), which is the focus of the present paper, actually works on more general clauses than those obtainable by applying step (1) to a $\mathcal{SHIQ}$ knowledge base. We first present a base transformation, the output of which can be either executed using a simple interpreter or further extended to executable Prolog code. We then discuss several optimisation techniques, applicable to the output of the base transformation. Some of these techniques are specific to our approach, while others are general enough to be interesting for DL reasoner implementors not using Prolog. We give an overview of DLog, a DL reasoner in Prolog, which is an implementation of the techniques outlined above. We evaluate the performance of DLog and compare it to some widely used DL reasoners, such as RacerPro, Pellet and KAON2.

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There is an increasing need for technology for merging semi-structured information (such as structured reports) from heterogeneous sources. For this, we advocate a knowledgebased approach when the information to be merged incorporates diverse, and potentially complex, conflicts (inconsistencies). In this paper, we contrast the goals of knowledgebased merging with other technologies such as semantic web technologies, information mediators, and database integration systems. We then explain how a system for knowledgebased merging can be constructed for a given application. To support the use of a knowledgebase, we use fusion rules to manage the semi-structured information that is input for merging. Fusion rules are a form of scripting language that defines how structured reports should be merged. The antecedent of a fusion rule is a call to investigate the information in the structured reports and the background knowledge, and the consequent of a fusion rule is a formula specifying an action to be undertaken to form a merged report. Fusion rules are not necessarily a definitive specification of how the input can be merged. They can be used by the user to explore different ways that the input can be merged. However, if the user has sufficient confidence in the output from a set of fusion rules, they can be regarded as a definitive specification for merging, and furthermore, they can then be treated as a form of meta-knowledge that gives the provenance of the merged reports. The integrated usage of fusion rules with a knowledgebase offers a practical and valuable technology for merging conflicting information. 1

Abstract: The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery. 1

The bio-ontologies community falls into two camps: First we have biology domain experts, who actually hold the knowledge we wish to capture in ontologies; second, we have ontology specialists, who hold knowledge about techniques and best practice on ontology development. In the bio-ontology domain, these two camps have often come into conflict, especially where pragmatism comes into conflict with perceived best practice. One of these areas is the insistence of computer scientists on a firm semantic basis for the representation language being used. In this article, we will first describe why this community is so insistent. Second, we will examine the semantics of the Web Ontology Language (OWL) and the directed acyclic graph (DAG) used by the Gene Ontology. Finally we will reconcile the two representations. The ability to exchange between the two representations means that we can capitalise on the features of both languages. 1