A Decision Support System For The Intelligence Satellite Analyst

The study developed a decision support system known as Visual Analytic Cognitive Model (VACOM) to support the Intelligence Analyst (IA) in satellite information processing task within a Geospatial Intelligence (GEOINT) domain. As a visual analytics, VACOM contains the image processing algorithms, a cognitive network of the IA mental model, and a Bayesian belief model for satellite information processing. A cognitive analysis tool helps to identify eight knowledge levels in a satellite information processing. These are, spatial, prototypical, contextual, temporal, semantic, pragmatic, intentional, and inferential knowledge levels, respectively. A cognitive network was developed for each knowledge level with data input from the subjective questionnaires that probed the analysts’ mental model. VACOM interface was designed to allow the analysts have a transparent view of the processes, including, visualization model, and signal processing model applied to the images, geospatial data representation, and the cognitive network of expert beliefs. VACOM interface allows the user to select a satellite image of interest, select each of the image analysis methods for visualization, and compare ‘ground-truth’ information against the recommendation of VACOM. The interface was designed to enhance perception, cognition, and even comprehension to the multi and complex image analyses by the analysts. A usability analysis on VACOM showed many advantages for the human analysts. These include, reduction in cognitive workload as a result of less information search, the IA can conduct an interactive experiment on each of his/her belief space and guesses, and selection of best image processing algorithms to apply to an image context

A Hybrid Model for Document Retrieval Systems.

A methodology for the design of document retrieval systems is presented. First, a composite index term weighting model is developed based on term frequency statistics, including document frequency, relative frequency within document and relative frequency within collection, which can be adjusted by selecting various coefficients to fit into different indexing environments. Then, a composite retrieval model is proposed to process a user\u27s information request in a weighted Phrase-Oriented Fixed-Level Expression (POFLE), which may apply more than Boolean operators, through two phases. That is, we have a search for documents which are topically relevant to the information request by means of a descriptor matching mechanism, which incorporate a partial matching facility based on a structurally-restricted relationship imposed by indexing model, and is more general than matching functions of the traditional Boolean model and vector space model, and then we have a ranking of these topically relevant documents, by means of two types of heuristic-based selection rules and a knowledge-based evaluation function, in descending order of a preference score which predicts the combined effect of user preference for quality, recency, fitness and reachability of documents

Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

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

The Abstract Language: Symbolic Cogniton And Its Relationship To Embodiment

Embodied theories presume that concepts are modality specific while symbolic theories suggest that all modalities for a given concept are integrated. Symbolic and embodied theories do fairly well with explaining and describing concrete concepts. Specifically, embodied theories seem well suited to describing the actual content of a concept while symbolic theories provide insight into how concepts operate. Conversely, neither symbolic nor embodied theories have been fully sufficient when attempting to describe and explain abstract concepts. Several pluralistic accounts have been put forth to describe how the semantic/lexical system interacts with the conceptual system. In this respect, they attempt to “embody” abstract concepts to the same extent as concrete concepts. Nevertheless, a concise and comprehensive theory for explaining how we learn/understand abstract concepts to the extent that we learn/understand concrete concepts remains elusive. One goal of the present review paper is to consider if abstract concepts can be defined by a unified theory or if subsets of abstract concepts will be defined by separate theories. Of particular focus will be Symbolic Interdependency Theory (SIT). It will be argued that SIT is suitable for grounding abstract concepts, as this theory infers that symbols bootstrap meaning from other symbols, highlighting the importance of abstract-to-abstract mapping in the same way that concrete-to-abstract mappings are created. Research will be considered to help outline a cohesive strategy for describing and understanding abstract concepts. Finally, as research has demonstrated efficiencies with concrete concept processing, analogous efficiencies will be explored for developing an understanding of abstract concepts. Such efforts could have both theoretical and practical implications for bolstering our knowledge of concept learning