Merging and ranking answers in the semantic web:the wisdom of crowds

In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.

Ontology ranking based on the analysis of concept structures

In view of the need to provide tools to facilitate the re-use of existing knowledge structures such as ontologies, we present in this paper a system, AKTiveRank, for the ranking of ontologies. AKTiveRank uses as input the search terms provided by a knowledge engineer and, using the output of an ontology search engine, ranks the ontologies. We apply a number of metrics in an attempt to investigate their appropriateness for ranking ontologies, and compare the results with a questionnaire-based human study. Our results show that AKTiveRank will have great utility although there is potential for improvement.

Data driven ontology evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.

Background and foreground knowledge in dynamic ontology construction

Ontologies have become a key component in the Semantic Web and Knowledge management. One accepted goal is to construct ontologies from a domain specific set of texts. An ontology reflects the background knowledge used in writing and reading a text. However, a text is an act of knowledge maintenance, in that it re-enforces the background assumptions, alters links and associations in the ontology, and adds new concepts. This means that background knowledge is rarely expressed in a machine interpretable manner. When it is, it is usually in the conceptual boundaries of the domain, e.g. in textbooks or when ideas are borrowed into other domains. We argue that a partial solution to this lies in searching external resources such as specialized glossaries and the internet. We show that a random selection of concept pairs from the Gene Ontology do not occur in a relevant corpus of texts from the journal Nature. In contrast, a significant proportion can be found on the internet. Thus, we conclude that sources external to the domain corpus are necessary for the automatic construction of ontologies.

User-centred ontology learning for knowledge management

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.

The ontology: Chimaera or Pegasus

In the context of the needs of the Semantic Web and Knowledge Management, we consider what the requirements are of ontologies. The ontology as an artifact of knowledge representation is in danger of becoming a Chimera. We present a series of facts concerning the foundations on which automated ontology construction must build. We discuss a number of different functions that an ontology seeks to fulfill, and also a wish list of ideal functions. Our objective is to stimulate discussion as to the real requirements of ontology engineering and take the view that only a selective and restricted set of requirements will enable the beast to fly.

Dynamic iterative ontology learning

The fundamental failure of current approaches to ontology learning is to view it as single pipeline with one or more specific inputs and a single static output. In this paper, we present a novel approach to ontology learning which takes an iterative view of knowledge acquisition for ontologies. Our approach is founded on three open-ended resources: a set of texts, a set of learning patterns and a set of ontological triples, and the system seeks to maintain these in equilibrium. As events occur which disturb this equilibrium, actions are triggered to re-establish a balance between the resources. We present a gold standard based evaluation of the final output of the system, the intermediate output showing the iterative process and a comparison of performance using different seed input. The results are comparable to existing performance in the literature.

An incremental tri-partite approach to ontology learning

In this paper we present a new approach to ontology learning. Its basis lies in a dynamic and iterative view of knowledge acquisition for ontologies. The Abraxas approach is founded on three resources, a set of texts, a set of learning patterns and a set of ontological triples, each of which must remain in equilibrium. As events occur which disturb this equilibrium various actions are triggered to re-establish a balance between the resources. Such events include acquisition of a further text from external resources such as the Web or the addition of ontological triples to the ontology. We develop the concept of a knowledge gap between the coverage of an ontology and the corpus of texts as a measure triggering actions. We present an overview of the algorithm and its functionalities.

The anatomical and functional correlates of category-specificity

The dramatic effects of brain damage can provide some of the most interesting insights into the nature of normal cognitive performance. In recent years a number of neuropsychological studies have reported a particular form of cognitive impairment where patients have problems recognising objects from one category but remain able to recognise those from others. The most frequent ‘category-specific’ pattern is an impairment identifying living things, compared to nonliving things. The reverse pattern of dissociation, i.e., an impairment recognising and naming nonliving things relative to living things, has been reported albeit much less frequently. The objective of the work carried out in this thesis was to investigate the organising principles and anatomical correlates of stored knowledge for categories of living and nonliving things. Three complementary cognitive neuropsychological research techniques were employed to assess how, and where, this knowledge is represented in the brain: (i) studies of normal (neurologically intact) subjects, (ii) case-studies of neurologically impaired patients with selective deficits in object recognition, and (iii) studies of the anatomical correlates of stored knowledge for living and nonliving things on the brain using magnetoencephalography (MEG). The main empirical findings showed that semantic knowledge about living and nonliving things is principally encoded in terms of sensory and functional features, respectively. In two case-study chapters evidence was found supporting the view that category-specific impairments can arise from damage to a pre-semantic system, rather than the assumption often made that the system involved must be semantic. In the MEG study, rather than finding evidence for the involvement of specific brain areas for different object categories, it appeared that, when subjects named and categorised living and nonliving things, a non-differentiated neural system was involved.

Clinical decision support system for point of care use: ontology driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms-desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.

Anatomical correlates of dynamic auditory processing:relationship to literacy during early adolescence

Adults show great variation in their auditory skills, such as being able to discriminate between foreign speech-sounds. Previous research has demonstrated that structural features of auditory cortex can predict auditory abilities; here we are interested in the maturation of 2-Hz frequency-modulation (FM) detection, a task thought to tap into mechanisms underlying language abilities. We hypothesized that an individual's FM threshold will correlate with gray-matter density in left Heschl's gyrus, and that this function-structure relationship will change through adolescence. To test this hypothesis, we collected anatomical magnetic resonance imaging data from participants who were tested and scanned at three time points: at 10, 11.5 and 13 years of age. Participants judged which of two tones contained FM; the modulation depth was adjusted using an adaptive staircase procedure and their threshold was calculated based on the geometric mean of the last eight reversals. Using voxel-based morphometry, we found that FM threshold was significantly correlated with gray-matter density in left Heschl's gyrus at the age of 10 years, but that this correlation weakened with age. While there were no differences between girls and boys at Times 1 and 2, at Time 3 there was a relationship between gray-matter density in left Heschl's gyrus in boys but not in girls. Taken together, our results confirm that the structure of the auditory cortex can predict temporal processing abilities, namely that gray-matter density in left Heschl's gyrus can predict 2-Hz FM detection threshold. This ability is dependent on the processing of sounds changing over time, a skill believed necessary for speech processing. We tested this assumption and found that FM threshold significantly correlated with spelling abilities at Time 1, but that this correlation was found only in boys. This correlation decreased at Time 2, and at Time 3 we found a significant correlation between reading and FM threshold, but again, only in boys. We examined the sex differences in both the imaging and behavioral data taking into account pubertal stages, and found that the correlation between FM threshold and spelling was strongest pre-pubertally, and the correlation between FM threshold and gray-matter density in left Heschl's gyrus was strongest mid-pubertally.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

A formal model of the Semantic Web Service Ontology (WSMO)

Semantic Web Service, one of the most significant research areas within the Semantic Web vision, has attracted increasing attention from both the research community and industry. The Web Service Modelling Ontology (WSMO) has been proposed as an enabling framework for the total/partial automation of the tasks (e.g., discovery, selection, composition, mediation, execution, monitoring, etc.) involved in both intra- and inter-enterprise integration of Web services. To support the standardisation and tool support of WSMO, a formal model of the language is highly desirable. As several variants of WSMO have been proposed by the WSMO community, which are still under development, the syntax and semantics of WSMO should be formally defined to facilitate easy reuse and future development. In this paper, we present a formal Object-Z formal model of WSMO, where different aspects of the language have been precisely defined within one unified framework. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in this paper, can be used to identify and eliminate errors present in existing documentation.

Sensing presence (PreSense) ontology : user modelling in the semantic sensor web

Increasingly, people's digital identities are attached to, and expressed through, their mobile devices. At the same time digital sensors pervade smart environments in which people are immersed. This paper explores different perspectives in which users' modelling features can be expressed through the information obtained by their attached personal sensors. We introduce the PreSense Ontology, which is designed to assign meaning to sensors' observations in terms of user modelling features. We believe that the Sensing Presence ( PreSense ) Ontology is a first step toward the integration of user modelling and "smart environments". In order to motivate our work we present a scenario and demonstrate how the ontology could be applied in order to enable context-sensitive services. © 2012 Springer-Verlag.

Ontology augmentation:combining semantic web and text resources

This work investigates the process of selecting, extracting and reorganizing content from Semantic Web information sources, to produce an ontology meeting the specifications of a particular domain and/or task. The process is combined with traditional text-based ontology learning methods to achieve tolerance to knowledge incompleteness. The paper describes the approach and presents experiments in which an ontology was built for a diet evaluation task. Although the example presented concerns the specific case of building a nutritional ontology, the methods employed are domain independent and transferrable to other use cases. © 2011 ACM.