Papers by Francesco Osborne
Lecture Notes in Computer Science, 2011
This paper proposes a new perspective, based on the concept of social masks, for the simulation o... more This paper proposes a new perspective, based on the concept of social masks, for the simulation of a realistic NPC (Non-Player Character) behavior. The Mask Model goal is to support AI techniques for autonomous agents by encouraging or discouraging behaviors according to the social environment and by providing knowledge about possible reactions to the agent actions. In this approach, the NPC tendencies are controlled by the interactions of three overlapping mask layers: selfperception layer, social layer and interpersonal layer. The masks mould the tendencies, the feelings and the ethics of a NPC. By changing the links between characters and masks, a wide variety of different behaviors and story-lines may arise. The paper present an algorithm for the selection of the actions and an example implementation.
LISC 2014 - Results: Discussion on Challenges in Making Sense Out Of Data Using Linked Data Technologies
Klink-2: Integrating Multiple Web Sources to Generate Semantic Topic Networks
Lecture Notes in Computer Science, 2015
In the Social Web, users typically interact with different Social network systems and can have di... more In the Social Web, users typically interact with different Social network systems and can have different accounts and different identities. Identifying the users across the Web, independently of the protocols supported by each social system and independently of the authentication data supplied by the user, is, so far, a challenge. This paper presents an approach to perform uniquely user identification by using the public user data distributed across the systems in the Social Web.
For a number of years now we have seen the emergence of repositories of research data specified u... more For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the fine-grained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore ...
Despite the large number and variety of tools and services available today for exploring scholarl... more Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors 'semantically' (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, thes...
Lecture Notes in Computer Science, 2014
Communities of academic authors are usually identified by means of standard community detection a... more Communities of academic authors are usually identified by means of standard community detection algorithms, which exploit 'static' relations, such as co-authorship or citation networks. In contrast with these approaches, here we focus on diachronic topic-based communities -i.e., communities of people who appear to work on semantically related topics at the same time. These communities are interesting because their analysis allows us to make sense of the dynamics of the research world -e.g., migration of researchers from one topic to another, new communities being spawn by older ones, communities splitting, merging, ceasing to exist, etc. To this purpose, we are interested in developing clustering methods that are able to handle correctly the dynamic aspects of topic-based community formation, prioritizing the relationship between researchers who appear to follow the same research trajectories. We thus present a novel approach called Temporal Semantic Topic-Based Clustering (TST), which exploits a novel metric for clustering researchers according to their research trajectories, defined as distributions of semantic topics over time. The approach has been evaluated through an empirical study involving 25 experts from the Semantic Web and Human-Computer Interaction areas. The evaluation shows that TST exhibits a performance comparable to the one achieved by human experts.
Inferring Semantic Relations by User Feedback
Lecture Notes in Computer Science, 2014
Lecture Notes in Computer Science, 2012
For a number of years now we have seen the emergence of repositories of research data specified u... more For a number of years now we have seen the emergence of repositories of research data specified using OWL/RDF as representation languages, and conceptualized according to a variety of ontologies. This class of solutions promises both to facilitate the integration of research data with other relevant sources of information and also to support more intelligent forms of querying and exploration. However, an issue which has only been partially addressed is that of generating and characterizing semantically the relations that exist between research areas. This problem has been traditionally addressed by manually creating taxonomies, such as the ACM classification of research topics. However, this manual approach is inadequate for a number of reasons: these taxonomies are very coarse-grained and they do not cater for the finegrained research topics, which define the level at which typically researchers (and even more so, PhD students) operate. Moreover, they evolve slowly, and therefore they tend not to cover the most recent research trends. In addition, as we move towards a semantic characterization of these relations, there is arguably a need for a more sophisticated characterization than a homogeneous taxonomy, to reflect the different ways in which research areas can be related. In this paper we propose Klink, a new approach to i) automatically generating relations between research areas and ii) populating a bibliographic ontology, which combines both machine learning methods and external knowledge, which is drawn from a number of resources, including Google Scholar and Wikipedia. We have tested a number of alternative algorithms and our evaluation shows that a method relying on both external knowledge and the ability to detect temporal relations between research areas performs best with respect to a manually constructed standard.
Understanding Research Dynamics
Communications in Computer and Information Science, 2014
Rexplore: Unveiling the dynamics of scholarly data
IEEE/ACM Joint Conference on Digital Libraries, 2014
Lecture Notes in Computer Science, 2014
In earlier papers we characterised the notion of diachronic topicbased communities -i.e., communi... more In earlier papers we characterised the notion of diachronic topicbased communities -i.e., communities of people who work on semantically related topics at the same time. These communities are important to enable topic-centred analyses of the dynamics of the research world. In this paper we present an innovative algorithm, called Research Communities Map Builder (RCMB), which is able to automatically link diachronic topic-based communities over subsequent time intervals to identify significant events. These include topic shifts within a research community; the appearance and fading of a community; communities splitting, merging, spawning other communities; etc. The output of our algorithm is a map of research communities, annotated with the detected events, which provides a concise visual representation of the dynamics of a research area. In contrast with existing approaches, RCMB enables a much more fine-grained understanding of the evolution of research communities, with respect to both the granularity of the events and the granularity of the topics. This improved understanding can, for example, inform the research strategies of funders and researchers alike. We illustrate our approach with two case studies, highlighting the main communities and events that characterized the World Wide Web and Semantic Web areas in the 2000 -2010 decade.
A Prismatic Cognitive Layout for Adapting Ontologies
Lecture Notes in Computer Science, 2013
Lecture Notes in Computer Science, 2013
Despite the large number and variety of tools and services available today for exploring scholarl... more Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors 'semantically' (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. 'ordinary' users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves.
TellEat: Sharing Experiences on the Move
Lecture Notes in Computer Science, 2014
Retrieval of Personal Public Data on Social Networks
Clustering citation distributions for semantic categorization and citation prediction
A Proposal for an Open Local Movie Recommender
A POV-Based User Model: From Learning Preferences to Learning Personal Ontologies
Lecture Notes in Computer Science, 2013
Escaping the Big Brother: An empirical study on factors influencing identification and information leakage on the Web
Journal of Information Science, 2014
ABSTRACT This paper presents a study on factors that may increase the risks of personal informati... more ABSTRACT This paper presents a study on factors that may increase the risks of personal information leakage, owing to the possibility of connecting user profiles that are not explicitly linked together. First, we introduce a technique for user identification based on cross-site checking and linking of user attributes. Then, we describe the experimental evaluation of the identification technique both in a real setting and on an online sample, showing its accuracy to discover unknown personal data. Finally, we combine the results on the accuracy of identification with the results of a questionnaire completed by the same subjects who performed the test in the real setting. The aim of the study was to discover possible factors that make users vulnerable to this kind of technique. We found that the number of social networks used, their features and especially the amount of profiles abandoned and forgotten by the user are factors that increase the likelihood of identification and the privacy risks.
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Papers by Francesco Osborne