- Two groups of keywords were used: "+ontology +behavior" and "+ontology +cognitive" on Google Scholar
- Round 1: Results (161000 and 101000 results respectively) were filtered by relevancy (based on Google Scholar ranking algorithm), with publication year of 2011 and later. Only first 300 results of each keyword group search were screened.
- Round 2: Results of round 2 are listed below. Most relevant papers were selected based on their Abstracts.
Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.
An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management (2011/37 cites)
This paper presents a generic Ontology-based User Modeling framework (OntobUMf), its components, and its associated user modeling processes. This framework models the behavior of the users and classifies its users according to their behavior. The user ontology is the backbone of OntobUMf and has been designed according to the Information Management System Learning Information Package (IMS LIP). The user ontology includes a Behavior concept that extends IMS LIP specification and defines characteristics of the users interacting with the system. Concrete examples of how OntobUMf is used in the context of a Knowledge Management (KM) System are provided.
The Neurobehavior Ontology: An Ontology for Annotation and Integration of Behavior and Behavioral Phenotypes
The provision of a method for recording behavior-related phenomena is necessary to enable integrative and comparative analyses of data and knowledge about behavior. The neurobehavior ontology facilitates the systematic representation of behavior and behavioral phenotypes, thereby improving the unification and integration behavioral data in neuroscience research.
The paper contributes with a new open ontology describing both low-level and high-level context information, as well as their relationships. Furthermore, a framework building on the developed ontology and reasoning models is presented and evaluated. The proposed method proves to be robust while identifying high-level contexts even in the event of erroneously-detected low-level contexts.
(**) Ontology-based deep learning for human behavior prediction with explanations in health social networks
In this paper, we study the research problem, human behavior prediction with explanations, for healthcare intervention systems in health social networks. We propose an ontology-based deep learning model (ORBM) for human behavior prediction over undirected and nodes-attributed graphs. We first propose a bottom-up algorithm to learn the user representation from health ontologies. Then the user representation is utilized to incorporate self-motivation, social influences, and environmental events together in a human behavior prediction model, which extends a well-known deep learning method, the Restricted Boltzmann Machine. ORBM not only predicts human behaviors accurately, but also, it generates explanations for each predicted behavior. Experiments conducted on both real and synthetic health social networks have shown the tremendous effectiveness of our approach compared with conventional methods.
Ontology for behavior requires two distinctions: (a) between classes and individuals; and (b) between objects and processes. These distinctions allow a workable ontology in which behavior consists of activities that are extended in time (i.e., processes) and are ontological individuals-functional wholes with parts that also are activities. Such an ontology provides coherence to a science of behavior.
Changing the behavior of human operators is an underutilized approach to reduce the resource consumption of manufacturing. We created an ontology to make more accessible the existing work on behavior change, and categorized current knowledge under the headings: Problem Types, Barriers, Principles, Strategies, Mechanisms, Applications and Authors. Constructed using a web ontology language, the structure allows free navigation from any of the above category headings, and enables design practitioners better access to the strategies most relevant to their problem. We provide an example of how researchers can identify useful strategies for a specific problem in manufacturing.
In this paper, we propose a novel method based on ontology for human behavior recognition. The state-of-art behavior recognition methods based on low visual features or high visual features have still achieved low recognition accuracy rate in reality. The two most important challenges of this problem are still remaining such as the semantic gap and the variety of appearance of human behaviors in reality. By using prior knowledge, our system could completely detect a behavior without training data of entire process and could be reused in other cases. In experimental results, our method have achieved the encouraging performance on PETS 2006 and PETS 2007 datasets.
We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers.
Due to its popularity, several research trends are emerged to service the huge volume of users including, Location Based Social Networks (LBSN), Recommendation Systems, Sentiment Analysis Applications, and many others. LBSNs applications are among the highly demanded applications that do not focus only on analyzing the spatio-temporal positions in a given raw trajectory but also on understanding the semantics behind the dynamics of the moving object. LBSNs are possible means of predicting human mobility based on users social ties as well as their spatial preferences. LBSNs rely on the efficient representation of users' trajectories. Hence, traditional raw trajectory information is no longer convenient. In our research we focus on studying human behavior trajectory which is the major pillar in location recommendation systems. In this paper we propose an ontology model with its underlying description logics to efficiently annotate human behavior trajectories.
Security agencies across the world are facing major challenge from the terrorism. Challenge lies in establishing inter-gang relationship and intra-gang behaviour. Though security agencies maintain structured and unstructured data, mining the database and doing predictive analysis is still very difficult task. Call records of phones can be analyzed and ontology can link various criminals belonging to various gangs and can predict inter-gang relationship. Based on the field data we have shown how terrorists based in Northeast India have developed links with Left-wing extremists operating in central part of India. At the end we provide a mathematical model based on fuzzy logic to understand extent of criminality.
Current healthcare systems facilitate patients in provision of healthcare services by using their context information. However, the problem is that the context information received from various sources is of heterogeneous nature which is not useful for conventional systems. To overcome this issue, we propose an ontology-based context fusion framework in this research that fuses the related and relevant context information collected about the patient’s daily life activities for better understanding of patient’s situation and behavior. The fused context information is logged using ontological representation in Life Log deployed on cloud server. On top of the Life Log, behavior analysis and prediction services are developed to analyze the behavior of the patient and provide better healthcare, wellness, and behavior prediction services.
This paper reports on research activities which aim to exploit the information extracted from Web logs (or query logs) in personalized user ontologies, with the objective to support the user in the process of discovering Web information relevant to her/his information needs. Personalized ontologies are used to improve the quality of Web search by applying two main techniques: query reformulation and re-ranking of query evaluation results. In this paper we analyze various methodologies presented in the literature aimed at using personalized ontologies, defined on the basis of the observation of Information Behaviour to help the user in finding relevant information.
A business process model defines how an organization perform its activities. Since the incorrect definition of business processes behavior may increase costs and development time, it is required the verification of process behavior. Verification methods based on anti-patterns are a promising approach to deal with this issue, but their informal definition may lead to ambiguities and different interpretations of what problem a given anti-pattern represents, and how it should be applied or implemented to detect behavioral errors in process models. The aim of this paper is to assess the feasibility of business process behavior verification by means of the ontological specification of behavioral anti-patterns. The study is based on the detection of anti-patterns in a BPMN process model by exploiting a set of standard ontological reasoning services.
The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data.
Human System Integration Ontology: Enhancing Model Based Systems Engineering to Evaluate Human-system Performance
To better integrate humans into and with systems, new semantics are needed to extend current system modeling representations. The integration of new semantics will allow human elements to be analyzed in a more holistic perspective. This paper looks into identifying core building blocks for creating the ontology for human system interaction, interfaces, and integration. This ontology, once fully developed, will extend current system modeling capabilities that will enable the human element to be analyzed as part of the overall system development process.
Friend of a Friend with Benefits ontology (FOAF+): extending a social network ontology for public health
The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data.
We present the basic structure of the Cognitive Paradigm Ontology (CogPO) for human behavioral experiments. While the experimental psychology and cognitive neuroscience literature may refer to certain behavioral tasks by name (e.g., the Stroop paradigm or the Sternberg paradigm) or by function (a working memory task, a visual attention task), these paradigms can vary tremendously in the stimuli that are presented to the subject, the response expected from the subject, and the instructions given to the subject. Drawing from the taxonomy developed and used by the BrainMap project (www.brainmap.org) for almost two decades to describe key components of published functional imaging results, we have developed an ontology capable of representing certain characteristics of the cognitive paradigms used in the fMRI and PET literature. The Cognitive Paradigm Ontology is being developed to be compliant with the Basic Formal Ontology (BFO), and to harmonize where possible with larger ontologies such as RadLex, NeuroLex, or the Ontology of Biomedical Investigations (OBI). The key components of CogPO include the representation of experimental conditions focused on the stimuli presented, the instructions given, and the responses requested. The use of alternate and even competitive terminologies can often impede scientific discoveries. Categorization of paradigms according to stimulus, response, and instruction has been shown to allow advanced data retrieval techniques by searching for similarities and contrasts across multiple paradigm levels. The goal of CogPO is to develop, evaluate, and distribute a domain ontology of cognitive paradigms for application and use in the functional neuroimaging community.
Decision models are essential theoretical tools in the study of choice behavior, but there is little consensus about the best model for describing choice, with different fields and different research programs favoring their own idiosyncratic sets of models. Even within a given field, decision models are seldom studied alongside each other, and insights obtained using 1 model are not typically generalized to others. We present the results of a large-scale computational analysis that uses landscaping techniques to generate a representational structure for describing decision models. Our analysis includes 89 prominent models of risky and intertemporal choice, and results in an ontology of decision models, interpretable in terms of model spaces, clusters, hierarchies, and graphs. We use this ontology to measure the properties of individual models and quantify the relationships between different models. Our results show how decades of quantitative research on human choice behavior can be synthesized within a single representational framework.
Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals
The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts.
Behaviour analysis provides a functional definition of operant behaviour but at the same time often uses topographic definitions of classically-conditioned behaviour. This may be an ontological error, which would have serious consequences. Functional accounts of classical conditioning have been proposed, and they are part of a move towards locating all conditioning processes within behavioural ecology. It is recommended that behaviour analysts use a systematically functional account of all behaviour, which distinguishes between the roles of phylogenetic and ontogenetic functions, and which can embrace unconditioned behavioural repertoires as well as the behaviour changes produced by classical conditioning and operant conditioning in one framework.
In this paper we address the problem of propagating user interests in ontology-based user models. Our ontology-based user model (OBUM) is devised as an overlay over the domain ontology. Using ontologies as the basis of the user profile allows the initial user behavior to be matched with existing concepts in the domain ontology. Such ontological approach to user profiling has been proven successful in addressing the cold-start problem in recommender systems, since it allows for propagation from a small number of initial concepts to other related domain concepts by exploiting the ontological structure of the domain. The main contribution of the paper is the novel algorithm for propagation of user interests which takes into account i) the ontological structure of the domain and, in particular, the level at which each domain item is found in the ontology; ii) the type of feedback provided by the user, and iii) the amount of past feedback provided for a certain domain object.
Currently, emotion is considered as a critical aspect of human behavior; thus it should be embedded within the reasoning module in an intelligent system where the aim is to anticipate or respond to human reactions. Therefore, current research in data mining shows an increasing interest in emotion assessment for improving human–machine interaction. Based on the analysis of electroencephalogram (EEG) which derives from automatic nervous system responses, computers can assess user emotions and find correlations between significant EEG features extracted from the raw data and the human emotional states. With the advent of modern signal processing techniques, the evaluative power of human emotion derived from EEG is increased exponentially due to the huge number of features that are typically extracted from the EEG signals.
Our proposed solution is based upon high-level domain concept reasoning, to account for more complex scenarios. The solution, referred to as iMessenger, addresses the problem of efficient and appropriate delivery of feedback by combining context such as current activity, posture, location, time and personal schedule to manage any inconsistency between what the user is expected to do and what the user is actually doing. The ontology-based context-aware approach has the potential to integrate knowledge and data from different ontology-based repositories. Therefore, iMessenger can utilize a set of potential ontological, context extracting frameworks, to locate, monitor, address and deliver personalized behaviour related feedback, aiding people in the self-management of their well-being.
Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research
Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static “snapshots” of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing “gold standard” measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a “knowledge commons,” which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.
Development of an Intervention Setting Ontology for behaviour change: Specifying where interventions take place
Background: Contextual factors such as an intervention’s setting are key to understanding how interventions to change behaviour have their effects and patterns of generalisation across contexts. The intervention’s setting is not consistently reported in published reports of evaluations. Using ontologies to specify and classify intervention setting characteristics enables clear and reproducible reporting, thus aiding replication, implementation and evidence synthesis. This paper reports the development of a Setting Ontology for behaviour change interventions as part of a Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project.
Methods: The Intervention Setting Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project: 1) Defining the ontology’s scope, 2) Identifying key entities by reviewing existing classification systems (top-down) and 100 published behaviour change intervention reports (bottom-up), 3) Refining the preliminary ontology by literature annotation of 100 reports, 4) Stakeholder reviewing by 23 behavioural science and public health experts to refine the ontology, 5) Assessing inter-rater reliability of using the ontology by two annotators familiar with the ontology and two annotators unfamiliar with it, 6) Specifying ontological relationships between setting entities and 7) Making the Intervention Setting Ontology machine-readable using Web Ontology Language (OWL) and publishing online.
Re sults: The Intervention Setting Ontology consists of 72 entities structured hierarchically with two upper-level classes: Physical setting including Geographic location, Attribute of location (including Area social and economic condition, Population and resource density sub-levels) and Intervention site (including Facility, Transportation and Outdoor environment sub-levels), as well as Social setting. Inter-rater reliability was found to be 0.73 (good) for those familiar with the ontology and 0.61 (acceptable) for those unfamiliar with it.
A key goal of cognitive science is to understand and map the relationship between cognitive processes. Previous works have manually curated cognitive terms and relations, effectively creating an ontology, but do they reflect how cognitive scientists study cognition in practice? In addition, cognitive science should provide theories that inform experimentalists in neuroscience studying implementations of cognition in the brain. But do neuroscientists and cognitive scientists study the same things? We set out to answer these questions in a data-driven way by text-mining and automated clustering to build a cognitive ontology from existing literature. We find automatically generated relationships to be missing in existing ontologies, and that cognitive science does not always inform neuroscience. Thus, our work serves as an efficient hypothesis-generating mechanism, inferring relationships between cognitive processes that can be manually refined by experts. Furthermore, our results highlight the gap between theories of cognition and the study of their implementation.
We present an emotion ontology for describing and reasoning on emotion context in order to improve emotion detection based on bodily expression. We incorporate context into the two-factor theory of emotion (bodily reaction plus cognitive input) and demonstrate the importance of context in the emotion experience. In attempting to determine emotion felt by another person, the bodily expresson of their emotion is the only evidence directly available, eg, “John looks angry”. Our motivation in this paper is to bring context into the emotion-modulating cognitive input, eg, we know that John is a generally calm person, so we can conclude from expression (anger) plus context (calm) that John is not only angry, but that “John must be furious”. We use a well known interoperable reasoning tool, an ontology, to bring context into the implementation of the emotion detection process. Our emotion ontology (EmOCA) allow us to describe and to reason about philia and phobia in order to modulate emotion determined from expression. We present an experiment suggesting that people use such a strategy to incorporate contextual information when determining what emotion another person may be feeling.
Character consists of personality, affect, socio-cultural embedding, cognitive abilities, health, and all other attributes distinguishing one individual from another. Ontology-based conceptual models representing individuals i.e. their character and resulting behavior in situations is needed for providing a unified framework for building truly interactive and adaptive systems. We propose CCOnto, an ontology for Character Computing that models human character. The ontology is to be used for adaptive interactive systems to understand and predict an individual’s behavior in a given situation, more specifically their performance in different tasks. The developed ontology models the different character attributes, their building blocks, and interactions with each other and with a person’s performance in different tasks.
A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings—for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis—including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.
Affective science conducts interdisciplinary research into the emotions and other affective phenomena. Currently, such research is hampered by the lack of common definitions of te rms used to describe, categorise and report both individual emotional experiences and the results of scientific investigations of such experiences. High quality ontologies provide formal definitions for types of entities in reality and for the relationships between such entities, definitions which can be used to disambiguate and unify data across different disciplines. Heretofore, there has been little effort directed towards such formal representation for affective phenomena, in part because of widespread debates within the affective science community on matters of definition and categorization. We describe our efforts towards developing an Emotion Ontology (EMO) to serve the affective science community. We here focus on conformity to the BFO upper ontology and disambiguation of polysemous terminology.
A business process is a sequence of activities that aims at creating products or services, granting value to the customer, and is generally represented by a business process model. Business process models play an important role in bridging the gap between the business domain and the information technology, increasing the weight of business modeling as first step of software development. However, the traditional way of representing a process is not suitable for the so-called Knowledge-Intensive Processes (KIP). This type of process comprises sequences of activities based on intensive acquisition, sharing, storage and (re)use of knowledge, so that the amount of value added to the organization depends on the actor knowledge. Current research in the literature points to the lack of approaches to make this kind of process explicit and strategies for handling information that is necessary for their understanding and support. The goal of this paper is to present KIPO—a knowledge-intensive process ontology, which encompasses a clear and semantically rich definition of KIPs, and to discuss the results of a case study to evaluate KIPO with regard to its applicability and capability of making all relevant knowledge embedded in a KIP explicit.
Human cognition is still a puzzling issue in research and its appropriate modeling. It depends on how the brain behaves at that particular instance and identifies and responds to a signal among myriads of noises that are present in the surroundings (called external noise) as well as in the neurons themselves (called internal noise). Thus it is not surprising to assume that the functionality consists of various uncertainties, possibly a mixture of aleatory and epistemic uncertainties. It is also possible that a complicated pathway consisting of both types of uncertainties in continuum play a major role in human cognition. The ability to predict the outcome of future events is, arguably, the most universal and significant of all global brain functions. The ability to anticipate the outcome of a given action depends on sensory stimuli from the outside world and previously learned experience or inherited instincts. So, one needs to formulate a theory of inference using prior knowledge for decision-making and judgment. Typically, Bayesian models of inference are used to solve such problems involving probabilistic frameworks. However, recent empirical findings in human judgment suggest that a reformulation of Hierarchical Bayesian theory of inference under this set-up or a more general probabilistic framework based approach like quantum probability would be more plausible than a Bayesian model or the standard probability theory. However, as the framework of quantum probability is an abstract one needs to study the context dependence so as understand the new empirical evidences in cognitive domain.
Coherent interpretations of many claims about measurement in the psychological sciences depend on philosophically realist commitments regarding the psychological attributes purportedly being measured. However, what it means to be a realist regarding psychological attributes has not been clarified, and this may contribute to the reluctance of psychometricians and others to embrace realist positions. This paper attempts to clarify what a psychological attribute might believably be. Drawing on conceptual resources provided by Searle’s (1992) biological naturalism, as well as other perspectives from the philosophies of mind and language, it is argued that psychological attributes can, in principle, be said to exist. On the realist perspective, the existence of an attribute is a necessary but insufficient condition for its measurability. However, existence is not the same as immutability or independence from human intentionality, and the role that humans play in creating psychological attributes cannot be ignored.
In this work, we present OntoSenticNet, a commonsense ontology for sentiment analysis based on SenticNet, a semantic network of 100,000 concepts based on conceptual primitives. The key characteristics of OntoSenticNet are: (i) the definition of precise conceptual hierarchy and properties associating concepts and sentiment values; (ii) the support for connecting external information (e.g., word embedding, domain information, and different polarity representations) to each individual defined within the ontology; and (iii) the capability of associating each concept with annotations contained in external resources (e.g., documents and multimodal resources).