On the basis of our 25-year experience with the GLARE (Guideline Acquisition, Representation and Execution) clinical decision support system, we have started to analyze the adoption of computer-interpretable clinical guidelines (CIGs) and AI techniques to train and test medical students about how to act on patients . Moving from decision support to the educational task involves significant research challenges. In this paper, we propose a new facility that supports teachers in the definition of tests, by selecting and hiding to students specific parts of the CIG, and asking students how they would act on the given case study (patient) in the selected parts. Students are provided with a medical ontology to identify proper actions/decisions, and students' proposals are then automatically compared with what the CIG (considered as a “golden standard”) would suggest to do to the patient through knowledge representation and reasoning techniques. Our basic explanation mechanism exploits the medical ontology to show to students the differences (if any) between their proposals and the ones of the CIG. • New educational approach based on Computer-interpretable clinical guidelines (CIGs) • Training and testing medical students about how to act on patients (following CIGs) • Medical ontology for action selection, conformance check (wrt CIG), and explanation • Test definition and acquisition

In medical process mining, specific domain characteristics have to be dealt with: in particular, in medicine, a significant amount of expert knowledge is typically available; moreover, an interactive approach, letting medical users be involved in the work of process model discovery, is more acceptable than a completely automated strategy. To this end, in our recent work we have defined SIM (Semantic Interactive Miner), an innovative process mining tool able to: (i) support the interaction with medical experts, who can progressively merge parts of the initially mined model, obtaining a more generalized version; (ii) exploit pre-encoded domain knowledge, to move from a model where activities are reported at the ground level to a more user-interpretable high-level version. In this paper we illustrate the features of our tool by showing its application to the case study of neonatal resuscitation simulation: we use SIM to mine the process models produced by two different groups of students of a simulation course, aiming at verifying whether differently skilled young professionals produce different processes, which can finally be compared to the correct guideline.

Artificial intelligence largely relies on bounds on differences (BoDs) to model binary constraints regarding different dimensions, such as time, space, costs, and calories. Recently, some approaches have extended the BoDs framework in a fuzzy, “noncrisp” direction, considering probabilities or preferences. While previous approaches have mainly aimed at providing an optimal solution to the set of constraints, we propose an innovative class of approaches in which constraint propagation algorithms aim at identifying the “space of solutions” (i.e., the minimal network) with their preferences, and query answering mechanisms are provided to explore the space of solutions as required, for example, in decision support tasks. Aiming at generality, we propose a class of approaches parametrized over user-defined scales of qualitative preferences (e.g., Low, Medium, High, and Very High), utilizing the resume and extension operations to combine preferences, and considering different formalisms to associate preferences with BoDs. We consider both “general” preferences and a form of layered preferences that we call “pyramid” preferences. The properties of the class of approaches are also analyzed. In particular, we show that, when the resume and extension operations are defined such that they constitute a closed semiring, a more efficient constraint propagation algorithm can be used. Finally, we provide a preliminary implementation of the constraint propagation algorithms.

Clinical Guidelines (CGs) provide general evidence-based recommendations and physicians often have to resort also to their Basic Medical Knowledge (BMK) to cope with specific patients. In this paper, we explore the interplay between CGs and BMK from the viewpoint of a-posteriori conformance analysis, intended as the adherence of a specific execution log to both the CG and the BMK. In this paper, we consider also the temporal dimension: the guideline may include temporal constraints for the execution of actions, and its adaptation to a specific patient and context may add or modify conditions and temporal constraints for actions. We propose an approach for analyzing execution traces in Answer Set Programming with respect to a guideline and BMK, pointing out discrepancies - including temporal discrepancies - with respect to the different knowledge sources, and providing explanations regarding how the applications of the CG and the BMK have interacted, especially in case strictly adhering to both is not possible.

Representing and managing temporal knowledge, in the form of temporal constraints, is a crucial task in many areas, including knowledge representation, planning, and scheduling. The current literature in the area is moving from the treatment of “crisp” temporal constraints to fuzzy or probabilistic constraints, to account for preferences and\or uncertainty. Given a set of temporal constraints, the evaluation of the tightest implied constraints is a fundamental task, which is essential also to provide reliable query-answering facilities. However, while such tasks have been widely addressed for “crisp” temporal constraints, they have not attracted enough attention in the “non-crisp” context yet. We overcome such a limitation, by (i) extending quantitative temporal constraints to cope with preferences, (ii) defining a temporal reasoning algorithm which evaluates the tightest temporal constraints, and (iii) providing suitable query-answering facilities based on it.

The telic\atelic distinction, first introduced by Aristotle in his Categories [1], has a long tradition in the Western culture. Prior research in philosophy, linguistics, cognitive science, artificial intelligence and other areas suggests the need to differentiate between temporal facts with goal-related semantics (i.e., telic) from those that are intrinsically devoid of goal\culmination (i.e., atelic). Surprisingly, such a need has been quite neglected in the area of temporal databases (TDBs). However, the telic\atelic distinction has deep effects on TDB data and query semantics, and on conceptual modelling.

Now-related temporal data play an important role in many applications. Clifford et al.'s approach is a milestone to model the semantics of `now' in temporal relational databases. Several relational representation models for now-related data have been presented; however, the semantics of such representations has not been explicitly studied. Additionally, the definition of a relational algebra to query now-related data is an open problem. We propose the first integrated approach that provides both a neat semantics for now-related data and a compact 1NF representation (data model and relational algebra) for them. Additionally, our approach also extends current approaches to consider (i) domains where it is not always possible to know when changes in the world are recorded in the database and (ii) now-related data with a bound on their persistency in the future. To do so, we explicitly model the notion of temporal indeterminacy in the future for now-related data. The properties of our approach are also analyzed both from a theoretical (semantic correctness and reducibility of the algebra) and from an experimental point of view. Experiments show that, despite the fact that our approach is a major extension to current temporal relational approaches, no significant overhead is added to deal with `now'.

Time is pervasive of reality, and many relational database approaches have been developed to cope with it. In practical applications, facts can repeat several times, and only the overall period of time containing all the repetitions may be known (consider, e.g., On January, John attended five meetings of the Bioinformatics project). While some temporal relational databases have faced facts repeated at (known) periodic time, or single facts occurred at temporally indeterminate time, the conjunction of non-periodic repetitions and temporal indeterminacy has not been faced yet. Coping with this problem requires an in-depth extension of current techniques. In this paper, we have introduced a new data model, and new definitions of relational algebraic operators coping with the above issues. We have studied the properties of the new model and algebra (with emphasis on the reducibility property), and how it can be integrated with other models in the literature.

Despite the huge amount of work devoted to the treatment of time within the relational context, few relevant temporal phenomena still remain to be addressed. One of them is the treatment of “nearly periodic events ”, i.e., events/facts that occur in intervals of time which repeat periodically (e.g., a meeting occurring twice each Monday, possibly not at regular times). Nearly periodic events are quite frequent in everyday life, and thus in many applicative contexts. Their treatment within the relational model is quite challenging, since it involves the integrated treatment of three aspects: (i) the number of repetitions, (ii) their periodicity, and (iii) temporal indeterminacy. Coping with this problem requires an in-depth extension of current temporal relational database techniques. In this paper, we introduce a new data model, and new definitions of relational algebraic operators coping with the above issues. We ascertain the properties of the new model and algebra, with emphasis on the expressiveness of our representation model, on the reducibility property, and on the correctness of the algebraic operators.

Logs recording the traces of execution of previous process instances can be exploited for different process management tasks, such as prediction and recommendation in operational support. Efficient retrieval of past traces can be very important to achieve such tasks, while building a model of the process from the log can support problem/anomaly detection and, more generally, process analysis. Trace retrieval is gaining attention in the Case Based Reasoning research community, but so far it has been faced in a completely separate way from the construction of a process model from the log, instead, we propose an approach aiming at integrating these two goals. The core notion of our proposal is the log-tree, which constitutes a "bridge" between the notions of (log) index and process model. We propose a mining algorithm to build it, and an algorithm exploiting it for trace retrieval. Future extensions of our initial contribution are also widely discussed.

Time provides context for all our experiences, cognition, and coordinated collective action. Prior research in linguistics, artificial intelligence, and temporal databases suggests the need to differentiate between temporal facts with goal-related semantics (i.e., telic) from those are intrinsically devoid of culmination (i.e., atelic). To differentiate between telic and atelic data semantics in conceptual database design, we propose an annotation-based temporal conceptual model that generalizes the semantics of a conventional conceptual model. Our temporal conceptual design approach involves: 1) capturing "what" semantics using a conventional conceptual model; 2) employing annotations to differentiate between telic and atelic data semantics that help capture "when" semantics; 3) specifying temporal constraints, specifically nonsequenced semantics, in the temporal data dictionary as metadata. Our proposed approach provides a mechanism to represent telic/atelic temporal semantics using temporal annotations. We also show how these semantics can be formally defined using constructs of the conventional conceptual model and axioms in first-order logic. Via what we refer to as the "semantics of composition," i.e., semantics implied by the interaction of annotations, we illustrate the logical consequences of representing telic/atelic data semantics during temporal conceptual design.

Computer Interpretable Guidelines (CIG) are an emerging area of research, to support medical decision making through evidence-based recommendations. However, new challenges in the data management field have to be faced, to integrate CIG management with a proper treatment of patient data, and of other forms of medical knowledge (e.g., causal and behavioral knowledge). In this position paper, we summarize a proposal for a research agenda that, in our opinion, can lead to a significant advancement in the field. The goal of the work is to provide suitable models and reasoning methodologies to cope with the aforementioned aspects, and to properly integrate them for medical decision support. Achieving such a goal requires advances in data management, and, in particular, in the treatment of indeterminate valid-time data in relational databases, of temporal abstraction on time series, of case retrieval on time series, of design-time and run-time model-based verification of guidelines, of case-based reasoning, of non-monotonic logics, of formal ontologies, of probabilistic graphical models (Bayesian Networks and Influence Diagrams).

The cooperative construction of data/knowledge bases has recently had a significant impulse (see, e.g., Wikipedia [1]). In cases in which data/knowledge quality and reliability are crucial, proposals of update/insertion/deletion need to be evaluated by experts. To the best of our knowledge, no theoretical framework has been devised to model the semantics of update proposal/ evaluation in the relational context. Since time is an intrinsic part of most domains (as well as of the proposal/evaluation process itself), semantic approaches to temporal relational databases (specifically, Bitemporal Conceptual Data Model (henceforth, BCDM) [2]) are the starting point of our approach. In this paper, we propose BCDMPV, a semantic temporal relational model that extends BCDM to deal with multiple update/insertion/deletion proposals and with acceptances/rejections of proposals themselves. We propose a theoretical framework, defining the new data structures, manipulation operations and temporal relational algebra and proving some basic properties, namely that BCDMPVis a consistent extension of BCDM and that it is reducible to BCDM. These properties ensure consistency with most relational temporal database frameworks, facilitating implementations.

Valid-time indeterminacy is "don't know when" indeterminacy, coping with cases in which one does not exactly know when a fact holds in the modeled reality. In this paper, we first propose a reference representation (data model and algebra) in which all possible temporal scenarios induced by valid-time indeterminacy can be extensionally modeled. We then specify a family of 16 more compact representational data models. We demonstrate their correctness with respect to the reference representation and analyze several properties, including their data expressiveness. Then, we compare these compact models along several relevant dimensions. Finally, we also extend the reference representation and a representative of compact representations to cope with probabilities.

Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrieval have to be addresses to this end. Classical methodological solutions proposed to cope with these issues, typically based on mathematical transforms, are characterized by strong limitations, such as a difficult interpretation of retrieval results for end users, reduced flexibility and interactivity, or inefficiency. In this paper, we describe a novel framework, in which time-series features are summarized by means of Temporal Abstractions, and then retrieved resorting to abstraction similarity. Our approach grants for interpretability of the output results, and understandability of the (user-guided) retrieval process. In particular, multilevel abstraction mechanisms and proper indexing techniques are provided, for flexible query issuing, and efficient and interactive query answering. Experimental results have shown the efficiency of our approach in a scalability test, and its superiority with respect to the use of a classical mathematical technique in flexibility, user friendliness, and also quality of results.

Event relations are used in many temporal relational database approaches to represent facts occurring at time instants. However, to the best of our knowledge, none of such approaches fully copes with the definition of events as provided, e.g., by the “consensus” temporal database glossary. We propose a new approach which overcomes such a limitation, allowing one to cope with multiple events occurring in the same temporal granule. This move involves major extensions to current approaches, since indeterminacy about the time and number of occurrences of events need to be faced. Specifically, we have introduced a new data model, and new definitions of relational algebraic operators coping with the above issues, and we have studied their reducibility. Last, but not least, we have shown that our approach can be easily extended in order to cope with a general form of temporal indeterminacy. Such an extension further increases the applicability of our approach.

Valid-time indeterminacy concerns not knowing exactly when a fact holds in the modeled reality. In this paper, we first propose a reference approach (data model and algebra) in which all possible temporal scenarios induced by valid-time indeterminacy can be extensionally modeled. We then specify a family of sixteen more compact representational data models. We demonstrate their correctness with respect to the reference approach and analyze several properties, including their data expressiveness and correctness with respect to the reference approach. Finally, we compare these compact models along several relevant dimensions.

In this paper, we propose a framework for representing and reasoning about qualitative and quantitative temporal constraints between periodic events. In particular, our contribution is twofold: (i) we provide a formalism to deal with both qualitative and quantitative “periodicity-dependent” constraints between repeated events, considering user-defined periodicities as well; and (ii) we propose an intensional approach to temporal reasoning, which is based on the operations of intersection and composition. Such a comprehensive approach, to the best of our knowledge, represents an innovative contribution that integrates and extends results from both the artificial intelligence and the temporal databases literature. © 2009 Wiley Periodicals, Inc.

Supporting therapy selection is a fundamental task for a system for computerized management of clinical guidelines (GL). To this end, decision theory concepts could provide significant advances. In this article, we propose a systematic analysis of the main GL representation primitives and of how they could be related to decision theory concepts. The knowledge representation contribution we provide can be seen as a basis for implementing a decision support tool within any of the systems described in the literature: As a matter of fact, at a sufficiently abstract level, the GL primitives we treat are shared by all of the systems. Such a tool could be adopted when executing a GL on a single patient (in clinical practice) and for simulation purposes. In particular, a decision theory tool based on this analysis is being implemented in the GLARE system. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 585–599, 2006.

In many application areas, including planning, workflow, guideline and protocol management, the description of the domain involves composite and/or periodic events, mutually related by temporal constraints on the execution order. Such events represent ‘classes’, since they can be instantiated to specific executions of the plan, guideline etc., and each execution must ‘respect’ the temporal constraints imposed on the corresponding classes. The main goal of our work is to propose an approach dealing with the above-mentioned temporal phenomena. To achieve such an objective, the authors propose a tractable domain-independent temporal reasoner. This enhances the generality of our approach, which provides a domain-independent module that can be integrated with other software tools to solve temporal problems in specific domains. From the methodological point of view, the authors first devise a representation formalism coping with the aforesaid phenomena, and then they describe temporal constraint propagation algorithms to deal with constraint inheritance and to perform temporal consistency checking. The representation formalism has been designed carefully, to obtain algorithms that are both complete and tractable. Finally, the paper also shows experimental results, including an application of the authors’ approach to clinical guidelines, evaluating their impact on future applications and research activities.

User-defined calendars and periodicities are gaining an increasing relevance in AI and DB theory and applications. Thus several representation languages have been introduced to model them. Even focusing the attention on symbolic languages only, several different proposals can be chosen, and the comparisons between them are not trivial at all. In this paper, we propose a lattice of properties about periodicity and use it in order to classify different symbolic approaches in the literature. We then propose a language which covers all the properties in the lattice.

In several areas, including temporal databases, Presburger arithmetic has been chosen as a standard reference to express the semantics of languages representing periodic time, and to study their expressiveness. On the other hand, the proposal of most symbolic languages in the AI literature has not been paired with an adequate semantic counterpart, making the task of studying the expressiveness of such languages and of comparing them a very complex one. In this paper, we first define a representation language which enables us to handle each temporal point as a complex object enriched with all the structure it is immersed in, and then we use it in order to provide a Presburger semantics for classes of symbolic languages coping with periodicity.

Reasoning with temporal constraints is a ubiquitous issue in many computer science tasks, for which many dedicated approaches have been and are being built. In particular, in many areas, including planning, workflow, guidelines, and protocol management, one needs to represent and reason with temporal constraints between classes of events (e.g., between the types of actions needed to achieve a goal) and temporal constraints between instances of events (e.g., between the specific actions being executed). The temporal constraints between the classes of events must be inherited by the instances, and the consistency of both types of constraints must be checked. In this article, we design a general-purpose domain-independent knowledge server dealing with these issues. In particular, we propose a formalism to represent temporal constraints, and we point out two orthogonal parameters that affect the definition of reasoning algorithms operating on them. We then show four algorithms to deal with inheritance and to perform temporal consistency checking (depending on the parameters) and we study their properties. Finally, we report the results we obtained by applying our system to the treatment of temporal constraints in clinical guidelines. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 919–947, 2004.

The analysis of the semantics of temporal data and queries plays a central role in the area of temporal databases. Although many different algebrae and models have been proposed, almost all of them are based on a point-based (snapshot) semantics for data. On the other hand, in the areas of linguistics, philosophy, and recently, artificial intelligence, an oft-debated issue concerns the use of an interval-based versus a point-based semantics. In this paper, we first show some problems inherent in the adoption of a point-based semantics for data, then argue that these problems arise because there is no distinction drawn in the data between telic and atelic facts. We then introduce a three-sorted temporal model and algebra including coercion functions for transforming relations of one sort into relations of the other at query time which properly copes with these issues.

In many application areas, including planning, workflow, guidelines and protocol management, the description of the domain requires the use of part-of relations between events, the modeling of periodic repetitions and the treatment of "standard" temporal constraints between such events. Events in plans, workflows etc. represent "classes", in the sense that they can be instantiated to specific executions of the plan guideline, etc. Of course, such executions must respect (i.e., be consistent with) the temporal constraints explicitly or implicitly (e.g., by the part-of relation) conveyed by the class descriptions. In this paper, we propose a tractable domain-independent temporal server dealing with the above phenomena. We first sketch a representation formalism coping with part-of and instance-of relations, periodicity and temporal constraints, and then we describe two algorithms to deal with inheritance and to perform temporal consistency checking.

In this article, we propose an Allen-like approach to deal with different types of temporal constraints about periodic events. We consider the different components of such constraints (thus, unlike Allen, we also take into account quantitative constraints) including frame times, user-defined periods, qualitative temporal constraints, and numeric quantifiers and the interactions between such components. We propose a specialized high-level formalism to represent temporal constraints about periodic events; temporal reasoning on the formalism is performed by a path-consistency algorithm repeatedly applying our operations of inversion, intersection, and composition and by a specialized reasoner about periods and numeric quantification. The high-level formalism has been designed in such a way that different types of temporal constraints about periodic events can be represented in a compact and (hopefully) user-friendly way and path-consistency-based temporal reasoning on the formalism can be performed in polynomial time. We also prove that our definitions of inversion, intersection, and composition and, thus, of our path-consistency algorithm, are correct. This article also sketches the general architecture of the temporal manager for periodic events (TeMP+), that has been designed on the basis of our approach. As a working example, we show an application of our approach to scheduling in a school. © 2003 Wiley Periodicals, Inc.

Abstract—Calendars and periodicity play a fundamental role in many applications. Recently, some commercial databases started to support user-defined periodicity in the queries in order to provide “a human-friendly way of handling time ” (see, e.g., TimeSeries in Oracle 8). On the other hand, only few relational data models support user-defined periodicity in the data, mostly using “mathematical” expressions to represent periodicity. In this paper, we propose a high-level “symbolic ” language for representing user-defined periodicity which seems to us more human-oriented than mathematical ones, and we use the domain of Gadia’s temporal elements in order to define its properties and its extensional semantics. We then propose a temporal relational model which supports user-defined “symbolic ” periodicity (e.g., to express “on the second Monday of each month”) in the validity time of tuples and also copes with frame times (e.g., “from 1/1/98 to 28/2/98”). We define the temporal counterpart of the standard operators of the relational algebra, and we introduce new temporal operators and functions. We also prove that our temporal algebra is a consistent extension of the classical (atemporal) one. Moreover, we define both a fully symbolic evaluation method for the operators on the periodicities in the validity times of tuples, which is correct but not complete, and semisymbolic one, which is correct and complete, and study their computational complexity. Index Terms—Temporal relational model and algebra, user-defined symbolic periodicity in the validity time, high-level “symbolic” language, symbolic (intensional) evaluation method, semisymbolic evaluation method, user-friendly treatment of periodicity, integration and extension of artificial intelligence and temporal databases techniques. 1

Representing and reasoning with both temporal constraints between classes of events (e.g., between the types of actions needed to achieve a goal) and temporal constraints between instances of events (e.g., between the specific actions being executed) is a ubiquitous task in many areas of computer science, such as planning, workflow, guidelines and protocol management. The temporal constraints between the classes of events must be inherited by the instances, and the consistency of both types of constraints must be checked. We propose a general-purpose domain-independent knowledge server dealing with these issues. In particular, we propose a formalism to represent temporal constraints, we show two algorithms to deal with inheritance and to perform temporal consistency checking, and we study the properties of the algorithms.

In this paper, we focus on an application and extension of artificial intelligence temporal reasoning techniques in order to represent and reason with temporal constraints in clinical guidelines. Particular attention is dedicated to the treatment of repeated (periodic) events, which play a major role in clinical therapies. We also discuss some limitations of our current approach, highlighting possible future enhancements. The work in this paper has been developed in the GLARE project, meant to realize a prototype of a domain-independent manager of clinical guidelines. The GLARE system has been built in cooperation with Azienda Ospedaliera S. Giovanni Battista of Turin, and has been successfully tested on different clinical domains.

This article proposes a first–order logic axiomatic description of some basic ontological notions underlying the meaning of different types of temporal constraints about repeated events. The article also shows that such an ontology provides an homogeneous framework that can be used to model and compare the intended meaning of different formalisms in the artificial intelligence and temporal databases literature which deal with user–defined calendar dates and/or qualitative and quantitative constraints between different types of repeated events.

Generating abductive explanations is the basis of several problem solving activities such as diagnosis, planning, and interpretation. Temporal abduction means generating explanations that do not only account for the presence of observations, but also for temporal information on them, based on temporal knowledge in the domain theory. We focus on the case where such a theory contains temporal constraints that are required to be consistent with temporal information on observations. Our aim is to propose efficient algorithms for computing temporal abductive explanations. Temporal constraints in the theory and in the observations can be used actively by an abductive reasoner in order to prune inconsistent candidate explanations at an early stage during their generation. However, checking temporal constraint satisfaction frequently generates some overhead. We analyze two incremental ways of making this process efficient. First we show how, using a specific class of temporal constraints (which is expressive enough for many applications), such an overhead can be reduced significantly, yet preserving a full pruning power. In general, the approach does not affect the asymptotic complexity of the problem, but it provides significant advantages in practical cases. We also show that, for some special classes of theories, the asymptotic complexity is also reduced. We then show how, compiled knowledge based on temporal information, can be used to further improve the computation, thus, extending to the temporal framework previous results in the case of atemporal abduction. The paper provides both analytic and experimental evaluations of the computational advantages provided by our approaches.

In many areas of Computer Science, including planning, workflows, guidelines, and protocol management, one has to deal with abstract plans, procedures, or schedules involving temporal constraints between classes of actions that have to be repeated at periodic times and may be instantiated in different ways for different executions of the plans (procedures, schedules). In this paper we propose an integrated framework to deal with both qualitative temporal constraints on classes of actions and temporal constraints between instances of actions, in which temporal reasoning is used to amalgamate both types of constraints and to check their consistency. In particular, we consider an expressive formalism to deal with temporal constraints between classes of actions (with special attention to periodic actions) which takes into account different components such as frame times, numeric quantification, periods, and qualitative relations. We define the notions of (contextual) concretization of qualitative temporal constraints between classes and use this notion to formally define the consistency of a knowledge base of temporal constraints between classes and a set of temporal constraints on instances, and to define the algorithm for checking such a consistency. An application for scheduling lessons in a school is shown in an example.

Many different applications in different areas need to deal with both: databases, in order to take into account large amounts of structured data; and quantitative and qualitative temporal constraints about such data. We propose an approach that extends: temporal databases and artificial intelligence temporal reasoning techniques and integrate them in order to face such a need. Regarding temporal reasoning, we consider some results that we proved recently about efficient query answering in the Simple Temporal Problem framework and we extend them in order to deal with partitioned sets of constraints and to support relational database operations. Regarding databases, we extend the relational model in order to consider also qualitative and quantitative temporal constraints both in the data (data expressiveness) and in the queries (query expressiveness). We then propose a modular architecture integrating a relational database with a temporal reasoner. We also consider classes of applications that fit into our approach and consider patient management in a hospital as an example.

The paper describes a framework for representing and reasoning with periodic events. In particular, it proposes a temporal formalism which deals with both (1) quantitative information concerning the frame of time (e.g., between 1990 and 1993) and the user-defined calendar-dates (e.g., on the first Mondays of April) in which periodic events are located and (2) the qualitative relations between periodic events (e.g., Sam visits the branch office XO1 before going to his office). The meaning of the temporal specifications in our formalism is described in logical terms. The paper defines the basic operations of inversion, intersection and composition of temporal specifications. These operations are correct (with respect to the logical definition of the specifications) and do not lose information. Finally, the paper also describes a correct algorithm which takes advantage of these operations for performing temporal reasoning, and analyses its complexity. An application of the temporal framework to the scheduling in an office is shown in an example.

This article focuses on the problem of determining the qualitative temporal constraints between the eventualities described by “when” sentences. It faces the problem of integrating the purely linguistic temporal constraints provided by the anchoring of aspect and aktionsart of the “when” clause and of the main clause with the temporal constraints provided by the causal connections constituting the world knowledge. The modular approach we propose is based on the consideration that world knowledge and linguistic tools such as aspect and aktionsart work independently of each other: The former conveys temporal constraints between related eventualities, while the latter provide the temporal perspectives selected by the speaker. Thus, independent modules are proposed which: (1) represent the causal information and reason with it, extracting the qualitative temporal constraints between the eventualities regardless of the temporal perspectives introduced by the descriptions of the speaker; (2) determine (considering aspect and aktionsart) the temporal content of the clauses considered in isolation (and, in particular, the temporal perspectives introduced by the speaker); (3) anchor the temporal perspectives of the main clause and of the “when” clause; and (4) integrate the qualitative temporal constraints determined by (1), (2), and (3), to arrive at a single, integrated interpretation of the “when” sentence as a whole. © 1996 John Wiley & Sons, Inc.

In this paper we focus on the temporal constraints between causes and effects of causal relations, and, to deal correctly with such relations, we stress the importance of analyzing the action‐types (aspectual category) of causes and effects. In particular, we propose a domain‐independent ontology in which the distinctions between action‐types (e.g., the distinction between durative and punctual situations) are dealt with, and different types of causal relations are distinguished, on the basis of (i) the temporal constraints they impose between causes and effects (these constraints are expressed in a temporal formalism that extends Vilain's point interval algebra) and (ii) the action‐types of their causes and effects. Our ontology allows one to capture precisely the temporal constraints imposed by causation and the action‐types of the related situations. Moreover, in case the user has no accurate knowledge about the action‐types of some situations and/or the types of some causal connections to be dealt with, our formalism allows the user to leave the descriptions underspecified, and more specific pieces of information may be inferred. Inferences provide a flow of information about action‐types to information about temporal constraints in causation and vice versa, and demonstrate that a deep integration of time and causation is provided also at the inferential level.

The article focuses on the relations between time and causation and proposes a general framework in which a strict integration is achieved both in the representation formalism and in the reasoning process. By taking advantage of the basic capabilities of a Hybrid Knowledge Representation formalism (in particular, we use BACK), we provide an explicit and integrated representation of the basic entities in the temporal and causal ontology, and propose a taxonomy in which different types of causal relations found in the literature are classified, depending on the temporal relations they impose between causes and effects. Moreover, we develop a specialized causal-temporal reasoner which operates on causal nets built upon the basic causal-temporal representation. Such a reasoner is flexible (in the sense that different definitions of causation may be considered in the same reasoning process) and integrated (since the mutual effects of time and causation are coped with), and takes advantage of a specialized temporal reasoner. © 1994 John Wiley & Sons, Inc.