Event Ontology Research Articles

Event prediction based on evolutionary event ontology knowledge

The evolution and development of breaking news events usually present regular patterns, leading to the happening of sequential events. Therefore, the analysis of such evolutionary patterns among events and prediction to breaking news events from free text is a valuable capability for decision support systems. Traditional systems tend to focus on contents distribution information but ignore the inherent regularity of evolutionary events. We introduce evolutionary event ontology knowledge (EEOK) structuring the evolutionary patterns in five different event domains, namely Explosion, Conflagration, Geological Hazard, Traffic Accident, Personal Injury. Based on EEOK which provides a representing general-purpose ontology knowledge, we also explore a framework with a pipeline semantic analysis procedure of event extraction, evolutionary event recognition, and event prediction. Since the evolutionary event under each event domain has different evolution patterns, our proposed event prediction model combines the event types to capture the inherent regulation of evolutionary events. Comparative analyses are presented to show the effectiveness of the proposed prediction model compared to other alternative methods.

Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events.

The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen's interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.

Topic Detection and Tracking Based on Event Ontology

In recent years, Topic Detection and Tracking (TDT) has served as a core technology for searching, organizing and structuring news oriented textual materials from a variety of internet news and social media. The biggest challenges of TDT are the sparsity and complexity of data, organization of topic granularity, unexpectedness of emergency topics, and the unpredictability of topics evolution. This paper proposes a new TDT method based on event ontology for hierarchical topic detection and tracking topic evolution, named TDTEO. As domain-oriented event knowledge base, the event ontology provides event classes hierarchy based on domain common sense, as well a set of scenario models that describe the occurrence and evolution of different types of emergency events. The proposed method solves the problem that new emerging events are easy to be missed in the process of topic detection, and solves the problem that the topic model is easy to result in semantics drift due to the dynamic evolution of topic, and it can effectively improve the accuracy of topic detection and tracking. Experiments show our models achieve satisfactory performance of topic detection with a maximum macro-F1 value of 85.25%, and the (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Det</sub> )Norm of topic tracking in the datasets is as low as 0.1028. Experimental results show that hierachical topic model can effectively detect the topics and tracking model based on event scenario can reflect the trend of topic evolution.

Modeling artificial agents’ actions in context – a deontic cognitive event ontology

Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation.

Neo-Davidsonian ontology of events

Recent Neo-Davidsonian accounts of the semantics of progressive constructions of action verbs (‘John is building a house’) reflect an ontological distinction between processes or incomplete events on the one hand, and complete events on the other. This paper has two goals. First, it attempts to show that this putative ontological distinction is beset with problems. The second goal of this paper is to offer the beginnings of a positive proposal that seeks to show how the ontologically austere Davidsonian can account for the truth conditions of progressive constructions without the need for an enriched ontology.

Time as an Empirical Concept in Special Relativity

According to a widespread view, Einstein's definition of time in his special relativity is founded on the positivist verification principle. The present paper challenges this received outlook. It argues that Einstein's position on the concept of time, to wit, simultaneity, is best understood as a mitigated version of concept empiricism. He contrasts his position to Newton's absolutist and Kant's transcendental arguments, and in part sides with Hume's and Mach's empiricist arguments. Nevertheless, Einstein worked out a concept empiricism that is considerably more moderate than what we find in the preceding empiricist tradition and early logical positivism. He did not think that the origin of concepts is in observations, but in conventions, and he also maintained a realist ontology of physical events, which he thought is necessary for his theory. Consequently, his philosophy of time in special relativity is not couched in terms of an antimetaphysical verificationism.

Criminal Event Ontology Population and Enrichment using Patterns Recognition from Text

Thousands of criminal events are reported in newspapers and social networks every day. They describe violent acts that include actors, places, times, causes and any information concerning them. Verbal and nominal phrases are used to characterize and expose criminal events, which employ an important variety of natural language structures in the newspapers. In addition, causes, times and spaces of criminal events, use linguistic phrases to represent them in text. All of them need to be extracted as a pattern recognition process in order to extract criminal events from text and the information that concerns them. The extracted events, as a knowledge base, are very useful for information retrieval tasks. Therefore, this paper presents an approach based on pattern recognition in order to extract criminal events from Spanish text, by populating and enriching an ontology model. Ontology population and enrichment involve the instantiation of criminal events and their cause relationships. An evaluation process is carried out with a set of manually tagged newspapers with categories of specific events, and shows promising results.

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties.

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

Advanced Knowledge Representation and Reasoning Making Use of an Advanced N-ary Model

We discuss in this paper some aspects of NKRL, the Narrative Knowledge Representation Language. This is a high-level n-ary conceptual tool specially conceived for the representation and management of real world, dynamically characterized entities like situations, events and complex events, actions (e.g., in a robotics context) scripts/scenarios/narratives etc. After having pointed out some shortcomings of the standard ontological solutions for dealing with this sort of information, and having recalled some general characteristics of NKRL (like the addition of an "ontology of events" to the usual "ontology of objects"), we focus on the rules/inferential aspects proper to this language. We introduce, then, the general, formal model of "rule" used in an NKRL context and we show how this can be appropriately adapted to the setup of advanced types of inference operations based, e.g., on "analogical" and "causal" reasoning.

Ontology-based question understanding with the constraint of Spatio-temporal geological knowledge

Spatio-temporal geological big data contain a large amount of spatial and nonspatial data. It is important to effectively manage and retrieve these existing data for geological research, and understanding the question represents the first step. This paper aims to better understand the problem to improve the retrieval efficiency. In geology, the organization of massive unstructured geological data and the discovery of implicit content based on knowledge and relationships have been realized. However, previous findings are primarily based on spatial and nonspatial dimensions, and the key words searched are often just segmented words. In geological research, the dimension of time is as important as spatial and other nonspatial dimensions. In addition, an individual user’s goal may be more than a superficial representation of the problem. In this paper, we first construct the geological event ontology, organize Spatio-temporal big data with this dimension, and expand the concept of geological time. Next, based on geology knowledge, we propose spatio-temporal rules, spatial characteristics, and domain constraint rules to assess the consistency of the ontology and to maximize the relationship between the information and improvements in the efficiency of information retrieval. Then, the ontology question is extended, and the rules between this question and other ontologies are expounded to deepen the understanding of the problem. Finally, we evaluate our contribution over a real geology dataset on a knowledge-driven geologic survey information smart-service platform (GSISSP), which integrates geological thematic ontology, geological temporal ontology, and toponymy ontology. This study reveals a positive impact of the incorporation of multiple ontologies and feature rules, which is meaningful for improving accuracy and comprehensiveness.

Representing a reference foundational ontology of events in SROIQ

Representing a reference foundational ontology of events in SROIQ

Sensing, territory, population: Computation, embodied sensors, and hamlet control in the Vietnam War

This article analyses a mid-20th century computerized pacification reporting system, the Hamlet Evaluation System (HES), used by the US military to measure hamlet-level security and development trends in the Vietnam War. The significance of the HES was its capacity to translate US Military Advisor observations of Vietnamese hamlet life into a machine-readable format used by US military systems analysts to disclose ‘patterns of life.’ I show how US Military Advisors operated as ‘embodied sensors’ within the HES, producing a distinctive location-based event ontology – a ‘view of below’ – accompanied by rudimentary digital maps in-formation from incoming hamlet-level observation streams. I argue that acts of translating the rich texture of hamlet and village life into an objectified information format constituted a unique form of ‘epistemic violence,’ rooted not so much in the narrative subjection of the ‘Other,’ but in the pure abstraction of life into a digitally stored data trace.

ODAE: Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions

BackgroundDrug adverse events (AEs), or called adverse drug events (ADEs), are ranked one of the leading causes of mortality. The Ontology of Adverse Events (OAE) has been widely used for adverse event AE representation, standardization, and analysis. OAE-based ADE-specific ontologies, including ODNAE for drug-associated neuropathy-inducing AEs and OCVDAE for cardiovascular drug AEs, have also been developed and used. However, these ADE-specific ontologies do not consider the effects of other factors (e.g., age and drug-treated disease) on the outcomes of ADEs. With more ontological studies of ADEs, it is also critical to develop a general purpose ontology for representing ADEs for various types of drugs.ResultsOur survey of FDA drug package insert documents and other resources for 224 neuropathy-inducing drugs discovered that many drugs (e.g., sirolimus and linezolid) cause different AEs given patients’ age or the diseases treated by the drugs. To logically represent the complex relations among drug, drug ingredient and mechanism of action, AE, age, disease, and other related factors, an ontology design pattern was developed and applied to generate a community-driven open-source Ontology of Drug Adverse Events (ODAE). The ODAE development follows the OBO Foundry ontology development principles (e.g., openness and collaboration). Built on a generalizable ODAE design pattern and extending the OAE and NDF-RT ontology, ODAE has represented various AEs associated with the over 200 neuropathy-inducing drugs given different age and disease conditions. ODAE is now deposited in the Ontobee for browsing and queries. As a demonstration of usage, a SPARQL query of the ODAE knowledge base was developed to identify all the drugs having the mechanisms of ion channel interactions, the diseases treated with the drugs, and AEs after the treatment in adult patients. AE-specific drug class effects were also explored using ODAE and SPARQL.ConclusionODAE provides a general representation of ADEs given different conditions and can be used for querying scientific questions. ODAE is also a robust knowledge base and platform for semantic and logic representation and study of ADEs of more drugs in the future.

Event Ontology, Habit, and Agency

Abstract The following is an outline of an emerging foundation for science that begins to explain living forms and their patterns of movement beyond the sphere of mechanistic interactions. Employing an event ontology based on a convergence of quantum physics and Alfred North Whitehead’s process philosophy, coupled with the controversial yet promising theory of formative causation, this development will explore possible influences on the outcomes of events beyond any combination of external forces, laws of Nature, and chance. If it turns out there are no such additional influences—beyond mechanistic causes—it is difficult to see how agency or free will could exist. Assuming agency exists, as Whitehead apparently does, while committing to an event ontology in which process is fundamental leads to interesting questions about the natures of any entities that might participate in events. Furthermore, what might the purposes and agendas of such entities be based upon, beyond memory traces or DNA code? This ontological model, recognizing processes as fundamental, leads to a revised cosmology where the trajectory of a series of events may be due to more than rearrangement of material bits according to external forces and where goal-directed, recurring processes and the emergence of mind are not so surprising. Just as special relativity reduces to classical treatment when speeds slow down, this scientific model for a living world reduces to mechanistic materialism whenever conditions are more limited. Though this development is based on a philosophy of process, there are some dissimilarities with respect to Whitehead’s particular version.

Research on Multi-Source Data Integration Based on Ontology and Karma Modeling

The purpose of data integration is that integrates multi-source heterogeneous data. Ontology solves semantic describing of multi-source heterogeneous data. The authors propose a practical approach based on ontology modeling and an information toolkit named Karma modeling for fast data integration, and demonstrate an application example in detail. Armed Conflict Location &amp; Event Data Project (ACLED) is a publicly available conflict event dataset designed for disaggregated conflict analysis and crisis mapping. The authors analyzed the ACLED dataset and domain knowledge to build an Armed Conflict Event ontology, then constructed Karma models to integrate ACLED datasets and publish RDF data. Through SPARQL query to check the correctness of published RDF data. Authors design and developed an ACLED Query System based on Jena API, Canvas JS, and Baidu API, etc. technologies, which provides convenience for governments and researches to analyze regional conflict events and crisis early warning, and it verifies the validity of constructed ontology and the correctness of Karma modeling.

Event Ontology, Habit, and Agency

The following is an outline of an emerging foundation for science that begins to explain living forms and their patterns of movement beyond the sphere of mechanistic interactions. Employing an event ontology based on a convergence of quantum physics and Alfred North Whitehead’s process philosophy, coupled with the controversial yet promising theory of formative causation, this development will explore possible influences on the outcomes of events beyond any combination of external forces, laws of Nature, and chance. If it turns out there are no such additional influences—beyond mechanistic causes—it is difficult to see how agency or free will could exist. Assuming agency exists, as Whitehead apparently does, while committing to an event ontology in which process is fundamental leads to interesting questions about the natures of any entities that might participate in events. Furthermore, what might the purposes and agendas of such entities be based upon, beyond memory traces or DNA code? This ontological model, recognizing processes as fundamental, leads to a revised cosmology where the trajectory of a series of events may be due to more than rearrangement of material bits according to external forces and where goal-directed, recurring processes and the emergence of mind are not so surprising. Just as special relativity reduces to classical treatment when speeds slow down, this scientific model for a living world reduces to mechanistic materialism whenever conditions are more limited. Though this development is based on a philosophy of process, there are some dissimilarities with respect to Whitehead’s particular version.

How to be an adverbialist about phenomenal intentionality

Kriegel has revived adverbialism as a theory of consciousness. But recent attacks have shed doubt on the viability of the theory. To save adverbialism, I propose that the adverbialist take a stance on the nature of adverbial modification. On one leading theory, adverbial modification turns on the instantiation by a substance of a psychological type. But the resulting formulation of adverbialism turns out to be a mere notational variant on the relationalist approaches against which Kriegel dialectically situates adverbialism. By contrast, I argue that the way to be an adverbialist is to adopt an event ontology, emphasizing the active contribution of the mind to the phenomenology of experience. My close examination of the semantics of adverbial modification throws this metaphysical distinction into sharp relief. The event-based semantics overcomes recent objections in a way superior to the methods that would have been obviously available in the absence of a sophisticated semantics.

Linking historical collections in an event-based ontology

PurposeThis study aims to explore a way of representing historical collections by examining the features of an event in historical documents and building an event-based ontology model.Design/methodology/approachTo align with a domain-specific and upper ontology, the Basic Formal Ontology (BFO) model is adopted. Based on BFO, an event-based ontology for historical description (EOHD) is designed. To define events, event-related vocabularies are taken from the Library of Congress’ event types (2012). The three types of history and six kinds of changes are defined.FindingsThe EOHD model demonstrates how to apply the event ontology to biographical sketches of a creator history to link event types.Research limitations/implicationsThe EOHD model has great potential to be further expanded to specific events and entities through different types of history in a full set of historical documents.Originality/valueThe EOHD provides a framework for modeling and semantically reforming the relationships of historical documents, which can make historical collections more explicitly connected in Web environments.

A Framework for Service Robots in Smart Home: An Efficient Solution for Domestic Healthcare

PurposeAs the elder population grows, the need for domestic healthcare is on the rise. Both robotics and smart environments, including smart homes, provide a promising solution to monitor, interact and keep company to users. However, in real case scenarios, sensors data are not perfect and the environment changes over time, leading to erroneous understanding of the context and inappropriate responses. The purpose of this work is to tackle those challenges in order to improve the autonomy and efficiency of robots in smart environments. MethodsThe problematic was structured into three steps: (1) perception, (2) cognition and (3) action. We proposed and evaluated a software framework that covers the challenges of each step. It includes respectively: (1) a context acquisition method that supports and models the uncertainty of data by using complex event processing, fuzzy logic and ontologies; (2) an activity recognition system that combines vision, context knowledge and semantic reasoning; (3) a dynamic hierarchical task planner that alternates planning and execution. For each step, the framework was evaluated through simulations and/or experiments using a robot and a smart room. ResultsThe quality of the perception was assessed by measuring the efficiency of a cognition process using the acquired context knowledge. An uncertain environment was simulated, and results show our framework to enable a gain of 10% of correctness for an activity recognition process. The cognition part of the framework was evaluated by observing several persons performing activities. It achieved an overall 90% correct recognition, yet, such result questions the relevance of our approach. Finally, the action step was confronted a simulated scenario with various levels of dynamism. Our task planner appeared to reduce, by up to 23%, the number of tasks required to reach a goal in a dynamic environment. ConclusionOur framework provides software tools that make robots and smart environments more relevant in real housings. By supporting the uncertainty of context data and the dynamism of the environment, robots and smart environments can achieve more effectively their purposes in domestic healthcare applications.

Disease Specific Ontology of Adverse Events: Ontology extension and adaptation for Chronic Kidney Disease

BackgroundAdverse Event (AE) ontology can be used to support interoperability and computer-assisted reasoning of AEs. Despite significant progress in developing biomedical ontologies, they are facing the obstacle of adoption partly because those ontologies are too general to meet the requirements of a specific domain. Understanding and representing of AEs for a specific domain such as Chronic Kidney Disease (CKD) has both theoretical and clinical significance. CKD patients are at a high risk for an array of disease-intervention specific AEs, and these in turn can contribute to disease progression unlike other diseases. This study proposes Disease Specific Ontology of Adverse Events (DSOAE) to address specific requirements of CKD, and applies it to different usage scenarios with real data. MethodsWe introduce a method for developing DSOAE through the extension and adaption of general ontologies by incorporating domain-specific information and usage requirements. It starts with specifying the goal and scope of a target domain (i.e. selecting seed ontologies), followed by identifying main AE classes and relations, extracting and creating classes and relations, aligning and identifying upper-level classes and lower-level classes, and finally populating the ontology with instances. Any of these steps may be repeated to refine the ontology. ResultsDSOAE contains 22 CKD-specific AE classes, which are grouped into two general categories: patient-reported AEs and biochemical/laboratory-related AEs. In addition, disease history and comorbidity classes as introduced in this study help model patient-related risk factors for AEs. With the support of DSOAE, we build a knowledge base of CKD-specific AEs using data from different sources (e.g. patient cohort data and social media), and apply the knowledge base to data analysis and data integration. ConclusionsDSOAE enables the interoperability of AEs across different sources and supports the development of a knowledge base of domain-specific AEs. DSOAE can also meet the needs of different usage scenarios. The approach to constructing DSOAE is generalizable and can be used to develop AE ontology in other domains.