Extracting common sense knowledge via triple ranking using supervised and unsupervised distributional models

Jebbara S, Basile V, Cabrio E, Cimiano P. Extracting common sense knowledge via triple ranking using supervised and unsupervised distributional models. Semantic Web. 2019;10(1):139-158.In this paper we are concerned with developing information extraction models that support the extraction of common sense knowledge from a combination of unstructured and semi-structured datasets. Our motivation is to extract manipulation-relevant knowledge that can support robots' action planning. We frame the task as a relation extraction task and, as proof-ofconcept, validate our method on the task of extracting two types of relations: locative and instrumental relations. The locative relation relates objects to the prototypical places where the given object is found or stored. The second instrumental relation relates objects to their prototypical purpose of use. While we extract these relations from text, our goal is not to extract specific textual mentions, but rather, given an object as input, extract a ranked list of locations and uses ranked by `prototypicality'. We use distributional methods in embedding space, relying on the well-known skip-gram model to embed words into a low-dimensional distributional space, using cosine similarity to rank the various candidates. In addition, we also present experiments that rely on the vector space model NASARI, which compute embeddings for disambiguated concepts and are thus semantically aware. While this distributional approach has been published before, we extend our framework by additional methods relying on neural networks that learn a score to judge whether a given candidate pair actually expresses a desired relation. The network thus learns a scoring function using a supervised approach. While we use a ranking-based evaluation, the supervised model is trained using a binary classification task. The resulting score from the neural network and the cosine similarity in the case of the distributional approach are both used to compute a ranking. We compare the different approaches and parameterizations thereof on the task of extracting the above mentioned relations. We show that the distributional similarity approach performs very well on the task. The best performing parameterization achieves an NDCG of 0.913, a Precision@ 1 of 0.400 and a Precision@ 3 of 0.423. The performance of the supervised learning approach, in spite of having being trained on positive and negative examples of the relation in question, is not as good as expected and achieves an NCDG of 0.908, a Precision@ 1 of 0.454 and a Precision@3 of 0.387, respectively

Human-Machine Communication: Complete Volume. Volume 2

This is the complete volume of HMC Volume 2

Exploring identity processes in the work setting of a developing country through the lenses of social identity and post-colonialism

This thesis was submitted for the award of Doctor of Philosophy and awarded by Brunel University LondonThe concept of understanding one’s origin or existence spans across almost every sphere of social science; despite its popularity, there is still a lack of research exploring identity in the work setting of developing countries. This thesis aims to contribute to understanding identity processes of workers in developing countries through the lenses of social identity and post-colonialism. The rationale for using these areas lies in the perceived nature of identity processes for people in developing countries by taking into account historical and cultural influences; for social identity (Tajfel & Turner, 1979), the “prototype” and “cohesion, solidarity and harmony” and for post-colonialism (Sen, 2006; Ekeh, 1975; Ekanola, 2006; Mizuno & Okazawa, 2009), “power”, the “dialectics of the colonized mind” and “social formations”. This thesis takes a socio-psychological approach, which is based on a qualitative research method; in particular, 47 in-depth interviews with professionals from the oil and gas sector of Nigeria form a key aspect of the research method. Findings reveal that social identity theory can be used to interpret the propensity of Nigerians to identify with groups. The thesis finds that social identity captures the importance attached to group identification through an understanding of the drivers and benefits of harmony to the self-concept in the chosen context. However the thesis also finds that social identity but does not cater for other integral aspects of identity processes, such as power and identity struggle. The thesis finds that by addressing the perception of perpetuated colonialism produced by the persistent domination of foreign workers in senior roles and their interaction with indigenous workers, post-colonial theory adequately covers issues of power and struggle. In summary, the thesis finds that the integration of social identity theory and post-colonial theory facilitates a more holistic interpretation of identity processes in regions like Nigeria. Hence this thesis contributes to the literature on identity processes in the work setting of a developing country

Leading for Peace Leadership and its Development in Post-Conflict Contexts

This thesis examines the processes and practices of leadership and leadership development, in the context of post-conflict peacebuilding. The systematic literature review conducted in this thesis reveals that little is known about leadership in such a hostile context. The research focuses on how leaders in civil society lead for peace at grassroots and middle-range levels in extremely divided societies, and how they develop as leaders in these contexts. The overarching research question is: What does leadership for peacebuilding involve and how it has been developed in the post-conflict context? This research is an actor-focused inductive study based on empirical research into the role of civil society leadership in fostering and sustaining peace. It draws on semi-structured interviews with 32 long-standing civil society leaders in Northern Ireland, and Bosnia and Herzegovina. The empirical research examined the characteristics of the contexts of leadership in post-conflict peacebuilding from a social identity theory perspective (Tajfel, 1974). The key characteristics found in this research are: Hostility and violence; polarisation; and depersonalisation. The research then utilises the social identity theory of leadership to explore the nature of leading for peace in terms of processes and practices. These processes are: Differentiation; integration; and political astuteness. A framework is developed to show how leading for peace interacts with context. Finally, the research examines leadership learning and development in this context through the use of the technique of leadership journeys. Drawing on Tynjälä’s (2013) 3P model for workplace learning, a modified 3P model for peacebuilding leadership development in post-conflict contexts is proposed

Synthesis of aesthetics for ship design

In the search for consensus on a definition of beauty, fitting the task of appreciating a ship’s design, this research revealed that other components of visual appraisal and 3d pattern analysis are required for a systemic approach. The model process presented is built around local adaptation and Gestalt psychology and uses retrospective case studies to categorise and calculate proportions, and recognisable patterns. The number of results from each type of vessel were found to be different, due to each ship or boats various geometries and anatomy, which illuminated the importance of standardising a procedure of categorisation in the appreciative approach.The categorisation of functions around the philosophy of functional beauty and the maths of summation series, it is suggested here, will allow a library of algebraic patterns and parameters to penetrate further into the impending or emulated integrated systems of ship design. The process to derive physical parameters via the culturally focussed narrative of functional beauty, is deemed as a manageable and novel addition to the naval architect's role. However, for the results to have a decisive impact on commercial design or education, variance and validation through further case studies is required

Non classical concept representation and reasoning in formal ontologies

Formal ontologies are nowadays widely considered a standard tool for knowledge representation and reasoning in the Semantic Web. In this context, they are expected to play an important role in helping automated processes to access information. Namely: they are expected to provide a formal structure able to explicate the relationships between different concepts/terms, thus allowing intelligent agents to interpret, correctly, the semantics of the web resources improving the performances of the search technologies. Here we take into account a problem regarding Knowledge Representation in general, and ontology based representations in particular; namely: the fact that knowledge modeling seems to be constrained between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In particular, most common sense concepts seem not to be captured by the stringent semantics expressed by such formalisms as, for example, Description Logics (which are the formalisms on which the ontology languages have been built). The aim of this work is to analyse this problem, suggesting a possible solution suitable for formal ontologies and semantic web representations. The questions guiding this research, in fact, have been: is it possible to provide a formal representational framework which, for the same concept, combines both the classical modelling view (accounting for compositional information) and defeasible, prototypical knowledge ? Is it possible to propose a modelling architecture able to provide different type of reasoning (e.g. classical deductive reasoning for the compositional component and a non monotonic reasoning for the prototypical one)? We suggest a possible answer to these questions proposing a modelling framework able to represent, within the semantic web languages, a multilevel representation of conceptual information, integrating both classical and non classical (typicality based) information. Within this framework we hypothesise, at least in principle, the coexistence of multiple reasoning processes involving the different levels of representation

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways