15,866 research outputs found
Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts
In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing âhiddenâ or not easily discernible data concepts. PLSA provides a âbottom upâ approach to interoperability problems for users in the face of âtop downâ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
Genetic and pharmacological perturbation experiments, such as deleting a gene
and monitoring gene expression responses, are powerful tools for studying
cellular signal transduction pathways. However, it remains a challenge to
automatically derive knowledge of a cellular signaling system at a conceptual
level from systematic perturbation-response data. In this study, we explored a
framework that unifies knowledge mining and data mining approaches towards the
goal. The framework consists of the following automated processes: 1) applying
an ontology-driven knowledge mining approach to identify functional modules
among the genes responding to a perturbation in order to reveal potential
signals affected by the perturbation; 2) applying a graph-based data mining
approach to search for perturbations that affect a common signal with respect
to a functional module, and 3) revealing the architecture of a signaling system
organize signaling units into a hierarchy based on their relationships.
Applying this framework to a compendium of yeast perturbation-response data, we
have successfully recovered many well-known signal transduction pathways; in
addition, our analysis have led to many hypotheses regarding the yeast signal
transduction system; finally, our analysis automatically organized perturbed
genes as a graph reflecting the architect of the yeast signaling system.
Importantly, this framework transformed molecular findings from a gene level to
a conceptual level, which readily can be translated into computable knowledge
in the form of rules regarding the yeast signaling system, such as "if genes
involved in MAPK signaling are perturbed, genes involved in pheromone responses
will be differentially expressed"
Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)
This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory
2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational
Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP
2015) at the China National Convention Center in Beijing, on July 31st 2015.
Narratives are at the heart of information sharing. Ever since people began to share their experiences,
they have connected them to form narratives. The study od storytelling and the field of literary theory
called narratology have developed complex frameworks and models related to various aspects of
narrative such as plots structures, narrative embeddings, charactersâ perspectives, reader response, point
of view, narrative voice, narrative goals, and many others. These notions from narratology have been
applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g.
Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an
autonomous field of study and research. Narrative has been the focus of a number of workshops and
conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of
Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML
by Mani (2013)).
The workshop aimed at bringing together researchers from different communities working on
representing and extracting narrative structures in news, a text genre which is highly used in NLP
but which has received little attention with respect to narrative structure, representation and analysis.
Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic
extraction of events from single documents and work towards extracting story structures from multiple
documents, while these documents are published over time as news streams. Policy makers, NGOs,
information specialists (such as journalists and librarians) and others are increasingly in need of tools
that support them in finding salient stories in large amounts of information to more effectively implement
policies, monitor actions of âbig playersâ in the society and check facts. Their tasks often revolve around
reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g.
hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections
of relevant information but also projections to the future. They form a valuable potential for exploiting
news data in an innovative way.JRC.G.2-Global security and crisis managemen
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Mining Useful Information from Big Data Models Through Semantic-based Process Modelling and Analysis
Over the past few decades, most of the existing methods for analysing large growing knowledge bases, particularly Big Data, focus on building algorithms and/or technologies to help the knowledge-bases automatically or semi-automatically extend. Indeed, a vast number of such systems that construct the said large knowledge-bases continuously grow, and most often, they do not contain all of the facts about each process instance or elements that can be found within the process base. As a consequence, the resultant process models tend to be vague or missing value datasets. In view of such challenge, the work in this paper demonstrates that a well-designed information retrieval system or the process mining (PM) methods should present the results or discovered patterns in a formal and structured format qua being interpreted as domain knowledge. To this end, the work introduces a process mining approach that supports further enhancement of existing information systems or knowledge-base through the conceptual means of data analysis. In turn, the paper proposes a semantic-based process mining and analysis method, or better still, information retrieval and extraction system - that is capable of detecting patterns or unobserved behaviours within any given knowledge base by making use of the underlying semantics or properties (metadata) that describes the available data. Thus, the proposed approach is grounded on the semantic modelling and process mining techniques. The work illustrates this method using the case study of Learning Process. The goal is to discover user interaction patterns within a learning execution environment and respond by making decisions based on the semantical analysis of the captured users data. Practically, the method applies semantic annotation and ontological representation of the learning process domain data and the resultant models in order to discover patterns automatically by means of semantic reasoning. Theoretically, the process mining and modelling method show that a way of addressing the common challenge with computational intelligent systems or methods is through an effectively well-designed and fit for purpose system that meets the requirements and needs of the intended users. In other words, this paper applies effective reasoning methods to make inferences over a process knowledge-base (e.g. learning process) that leads to an automated discovery of learning patterns or behaviour
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Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling
Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis
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