Computer-aided biomimetics : semi-open relation extraction from scientific biological texts

Engineering inspired by biology – recently termed biom* – has led to various ground-breaking technological developments. Example areas of application include aerospace engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains at large, despite the existence of a plethora of methods and design tools. In recent years computational tools have been proposed as well, which can potentially support a systematic integration of relevant biological knowledge during biom*. However, these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all the gaps in the biom* process. This thesis investigates why existing CAB tools fail, proposes a novel approach – based on Information Extraction – and develops a proof-of-concept for a CAB tool that does enable a systematic approach to biom*. Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated sentences, 3) a novel Information Extraction approach that combines the outputs from a supervised Relation Extraction system and an existing Open Information Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis by domain-experts, and ii) also improve the access to biology information for non-exper

A Two-Level Information Modelling Translation Methodology and Framework to Achieve Semantic Interoperability in Constrained GeoObservational Sensor Systems

As geographical observational data capture, storage and sharing technologies such as in situ remote monitoring systems and spatial data infrastructures evolve, the vision of a Digital Earth, first articulated by Al Gore in 1998 is getting ever closer. However, there are still many challenges and open research questions. For example, data quality, provenance and heterogeneity remain an issue due to the complexity of geo-spatial data and information representation. Observational data are often inadequately semantically enriched by geo-observational information systems or spatial data infrastructures and so they often do not fully capture the true meaning of the associated datasets. Furthermore, data models underpinning these information systems are typically too rigid in their data representation to allow for the ever-changing and evolving nature of geo-spatial domain concepts. This impoverished approach to observational data representation reduces the ability of multi-disciplinary practitioners to share information in an interoperable and computable way. The health domain experiences similar challenges with representing complex and evolving domain information concepts. Within any complex domain (such as Earth system science or health) two categories or levels of domain concepts exist. Those concepts that remain stable over a long period of time, and those concepts that are prone to change, as the domain knowledge evolves, and new discoveries are made. Health informaticians have developed a sophisticated two-level modelling systems design approach for electronic health documentation over many years, and with the use of archetypes, have shown how data, information, and knowledge interoperability among heterogenous systems can be achieved. This research investigates whether two-level modelling can be translated from the health domain to the geo-spatial domain and applied to observing scenarios to achieve semantic interoperability within and between spatial data infrastructures, beyond what is possible with current state-of-the-art approaches. A detailed review of state-of-the-art SDIs, geo-spatial standards and the two-level modelling methodology was performed. A cross-domain translation methodology was developed, and a proof-of-concept geo-spatial two-level modelling framework was defined and implemented. The Open Geospatial Consortium’s (OGC) Observations & Measurements (O&M) standard was re-profiled to aid investigation of the two-level information modelling approach. An evaluation of the method was undertaken using II specific use-case scenarios. Information modelling was performed using the two-level modelling method to show how existing historical ocean observing datasets can be expressed semantically and harmonized using two-level modelling. Also, the flexibility of the approach was investigated by applying the method to an air quality monitoring scenario using a technologically constrained monitoring sensor system. This work has demonstrated that two-level modelling can be translated to the geospatial domain and then further developed to be used within a constrained technological sensor system; using traditional wireless sensor networks, semantic web technologies and Internet of Things based technologies. Domain specific evaluation results show that twolevel modelling presents a viable approach to achieve semantic interoperability between constrained geo-observational sensor systems and spatial data infrastructures for ocean observing and city based air quality observing scenarios. This has been demonstrated through the re-purposing of selected, existing geospatial data models and standards. However, it was found that re-using existing standards requires careful ontological analysis per domain concept and so caution is recommended in assuming the wider applicability of the approach. While the benefits of adopting a two-level information modelling approach to geospatial information modelling are potentially great, it was found that translation to a new domain is complex. The complexity of the approach was found to be a barrier to adoption, especially in commercial based projects where standards implementation is low on implementation road maps and the perceived benefits of standards adherence are low. Arising from this work, a novel set of base software components, methods and fundamental geo-archetypes have been developed. However, during this work it was not possible to form the required rich community of supporters to fully validate geoarchetypes. Therefore, the findings of this work are not exhaustive, and the archetype models produced are only indicative. The findings of this work can be used as the basis to encourage further investigation and uptake of two-level modelling within the Earth system science and geo-spatial domain. Ultimately, the outcomes of this work are to recommend further development and evaluation of the approach, building on the positive results thus far, and the base software artefacts developed to support the approach

Computer-Aided Biomimetics : Semi-Open Relation Extraction from scientific biological texts

Engineering inspired by biology – recently termed biom* – has led to various groundbreaking technological developments. Example areas of application include aerospace engineering and robotics. However, biom* is not always successful and only sporadically applied in industry. The reason is that a systematic approach to biom* remains at large, despite the existence of a plethora of methods and design tools. In recent years computational tools have been proposed as well, which can potentially support a systematic integration of relevant biological knowledge during biom*. However, these so-called Computer-Aided Biom* (CAB) tools have not been able to fill all the gaps in the biom* process. This thesis investigates why existing CAB tools fail, proposes a novel approach – based on Information Extraction – and develops a proof-of-concept for a CAB tool that does enable a systematic approach to biom*. Key contributions include: 1) a disquisition of existing tools guides the selection of a strategy for systematic CAB, 2) a dataset of 1,500 manually-annotated sentences, 3) a novel Information Extraction approach that combines the outputs from a supervised Relation Extraction system and an existing Open Information Extraction system. The implemented exploratory approach indicates that it is possible to extract a focused selection of relations from scientific texts with reasonable accuracy, without imposing limitations on the types of information extracted. Furthermore, the tool developed in this thesis is shown to i) speed up a trade-off analysis by domain-experts, and ii) also improve the access to biology information for nonexperts

The Trouble With Big Data

This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission. This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation

A study of intercultural discourse between mainland Chinese speakers of English and Anglo-Australians

Intercultural communication between mainland Chinese speakers of English and Anglo-Australians is receiving ever-increasing attention in many fields. These fields include intercultural communication. English language teaching, education and business. This study approached the intercultural communication between mainland Chinese speakers of English and Anglo-Australians from a cognitive perspective by applying the theoretical framework of cultural linguistics. The intercultural discourse produced by mainland Chinese speakers of English in the context of them interacting with Anglo-Australians was analysed. The analysis was made by employing key concepts such as schemas, cultural schemas, discourse scenarios and discourse indexicals. A body of 39 audio-taped conversations between mainland Chinese speakers of English and Anglo-Australians which ran about 50 hours was collected according to the research tradition of the ethnography of communication. The data were transcribed and examined with the “emic” and “etic” insights provided by volunteer participants and informants. Fifty live excerpts of these conversations were analysed in line with cognitive anthropology and cultural linguistics

Re-examining and re-conceptualising enterprise search and discovery capability: towards a model for the factors and generative mechanisms for search task outcomes.

Many organizations are trying to re-create the Google experience, to find and exploit their own corporate information. However, there is evidence that finding information in the workplace using search engine technology has remained difficult, with socio-technical elements largely neglected in the literature. Explication of the factors and generative mechanisms (ultimate causes) to effective search task outcomes (user satisfaction, search task performance and serendipitous encountering) may provide a first step in making improvements. A transdisciplinary (holistic) lens was applied to Enterprise Search and Discovery capability, combining critical realism and activity theory with complexity theories to one of the worlds largest corporations. Data collection included an in-situ exploratory search experiment with 26 participants, focus groups with 53 participants and interviews with 87 business professionals. Thousands of user feedback comments and search transactions were analysed. Transferability of findings was assessed through interviews with eight industry informants and ten organizations from a range of industries. A wide range of informational needs were identified for search filters, including a need to be intrigued. Search term word co-occurrence algorithms facilitated serendipity to a greater extent than existing methods deployed in the organization surveyed. No association was found between user satisfaction (or self assessed search expertise) with search task performance and overall performance was poor, although most participants had been satisfied with their performance. Eighteen factors were identified that influence search task outcomes ranging from user and task factors, informational and technological artefacts, through to a wide range of organizational norms. Modality Theory (Cybersearch culture, Simplicity and Loss Aversion bias) was developed to explain the study observations. This proposes that at all organizational levels there are tendencies for reductionist (unimodal) mind-sets towards search capability leading to fixes that fail. The factors and mechanisms were identified in other industry organizations suggesting some theory generalizability. This is the first socio-technical analysis of Enterprise Search and Discovery capability. The findings challenge existing orthodoxy, such as the criticality of search literacy (agency) which has been neglected in the practitioner literature in favour of structure. The resulting multifactorial causal model and strategic framework for improvement present opportunities to update existing academic models in the IR, LIS and IS literature, such as the DeLone and McLean model for information system success. There are encouraging signs that Modality Theory may enable a reconfiguration of organizational mind-sets that could transform search task outcomes and ultimately business performance

Sustainability of systems interoperability in dynamic business networks

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de ComputadoresCollaborative networked environments emerged with the spread of the internet, contributing to overcome past communication barriers, and identifying interoperability as an essential property to support businesses development. When achieved seamlessly, efficiency is increased in the entire product life cycle support. However, due to the different sources of knowledge, models and semantics, enterprise organisations are experiencing difficulties exchanging critical information, even when they operate in the same business environments. To solve this issue, most of them try to attain interoperability by establishing peer-to-peer mappings with different business partners, or use neutral data and product standards as the core for information sharing, in optimized networks. In current industrial practice, the model mappings that regulate enterprise communications are only defined once, and most of them are hardcoded in the information systems. This solution has been effective and sufficient for static environments, where enterprise and product models are valid for decades. However, more and more enterprise systems are becoming dynamic, adapting and looking forward to meet further requirements; a trend that is causing new interoperability disturbances and efficiency reduction on existing partnerships. Enterprise Interoperability (EI) is a well established area of applied research, studying these problems, and proposing novel approaches and solutions. This PhD work contributes to that research considering enterprises as complex and adaptive systems, swayed to factors that are making interoperability difficult to sustain over time. The analysis of complexity as a neighbouring scientific domain, in which features of interoperability can be identified and evaluated as a benchmark for developing a new foundation of EI, is here proposed. This approach envisages at drawing concepts from complexity science to analyse dynamic enterprise networks and proposes a framework for sustaining systems interoperability, enabling different organisations to evolve at their own pace, answering the upcoming requirements but minimizing the negative impact these changes can have on their business environment

Challenging common sense about nonsense : an integrational approach to schizophrenic language behaviour

Includes bibliographical references (leaves 159-168).Due to certain fundamental flaws, orthodox linguistics has not succeeded in producing a coherent account of 'schizophrenic language' - the host of symptoms that are alternatively characterised as evidence of formal thought disorder or labelled as disorganised speech, a disorder in itself. The most important of these flaws are its treatment of languages as fixed codes, which doubles as an explanation of how linguistic communication works, and its postulation of the mental structures that would be necessary if languages were indeed fixed codes, and communication a matter of encoding and decoding messages. In particular, orthodox linguistics has bolstered the now-dominant neo-Kraepelinian, biomedical account of schizophrenia, which treats utterances as symptoms that give clues to brain (dis)organisation and (dys) function. Integrational linguistics, which criticises the culturally based assumptions - collectively referred to as 'the language myth' - that are at the heart of the orthodox account of languages and language, provides an alternative. It sympathises with the growing trend in cognitive science and philosophy towards 'embodiment' and 'distributed cognition', which recognises that encultured entities like languages, minds, brains, bodies, and world are intrinsically defined by their co-evolution in the species, and co-emergence during an individual's development. Integrationists argue that by focusing in the first instance on second-order cultural constructs called 'languages', orthodox linguistics fails to give an account of the first-order experience of language users. This thesis approaches the topic of 'schizophrenic language' from a broadly integrationist perspective in order to demonstrate that because orthodox linguistics is so widely taken for granted in psychiatry, its biases inform current mainstream accounts of schizophrenic language, motivate the outright dismissal of interpersonal accounts, past and present, and provide a skewed picture of the phenomenon it purports to be describing, by ultimately constructing an individual-focused, deficit-based account of what is not, as opposed to what is. That is, by holding up orthodox linguistics' idealised version of communication and speakers (which has little applicability even to 'normal' language users), it uses deviation from the ideal as description and explanation, rather than recognising the strategies actually employed by schizophrenics in their attempts to make sense, even if these attempts fail. The alternative argued for here is to apply the tenets of integrationist linguistics to schizophrenic language behaviour, to give a fuller account of communication situations involving schizophrenics and normal interlocutors. As a result, this thesis calls for a reformulation of the idea that incomprehensibility stems from deviant speech, itself the product of an irrational brain. 'Sense', 'deviance' and 'irrationality' are a moment-to-moment metalinguistic appraisals made by language users, second-order cultural constructions that shape the speech community's response to certain individuals. Describing the speech of schizophrenics as 'deviant', 'irrational', or 'nonsensical' constrains their jointly-constructed capability of making sense using the resources (which may include other individual's minds) at their disposal. Integration linguistics thus brings into focus a moral and political dimension to such descriptions which is obscured by an orthodox linguistics-biased biomedical approach