A sound and complete fuzzy temporal constraint logic.

In this work, we define an extended fuzzy temporal constraint logic (EFTCL) based on possibilistic logic. EFTCL allows us to handle fuzzy temporal constraints between temporal variables and, therefore, enables us to express interrelated events through fuzzy temporal constraints. EFTCL is compatible with a theoretical temporal reasoning model: the fuzzy temporal constraint networks (FTCN). The syntax, the semantics and the deduction and refutation theorems for EFTCL are similar to those defined for the sound and noncomplete fuzzy temporal constraint logic (FTCL). In this paper, a resolution principle for performing inferences which take these constraints into account is proposed for EFTCL. Moreover, we prove the soundness and the completeness of the refutation by resolution in EFTCL.     

Event graphs for information retrieval and multi-document summarization

With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks.     

Causal temporal constraint networks for representing temporal knowledge

In this work we describe causal temporal constraint networks (CTCN) as a new computable model for representing temporal information and efficiently handling causality. The proposed model enables qualitative and quantitative temporal constraints to be established, introduces the representation of causal constraints, and suggests mechanisms for representing inexact temporal knowledge. The temporal handling of information is achieved by structuring the information in different interpretation contexts, linked to each other through an inference mechanism which obtains interpretations that are consistent with the original temporal information. In carrying out inferences, we take into account the temporal relationships between events, the possible inexactitude associated with the events, and the atemporal or static information which affects the interpretation pattern being considered. The proposed schema is illustrated with an application developed using the CommonKADS methodology.     

A Fuzzy Temporal Object‐Relational Database: Model and Implementation

In real world, some data have a specific temporal validity that must be appropiately managed. To deal with this kind of data, several proposals of temporal databases have been introduced. Moreover, time can also be affected by imprecision, vagueness, and/or uncertainty, since human beings manage time using temporal indications and temporal notions, which may also be imprecise. For this reason, information systems require appropriate support to accomplish this task. In this work, we present a novel possibilistic valid time model for fuzzy databases including the data structures, the integrity constraints, and the DML. Together with this model, we also present its implementation by means of a fuzzy valid time support module on top of a fuzzy object‐relational database system. The integration of these modules allows to perform queries that combines fuzzy valid time constraints together with fuzzy predicates. Besides, the model and implementation proposed support the crisp valid time model as a particular case of the fuzzy valid time support provided.     

Dynamic consistency of fuzzy conditional temporal problems

Conditional Temporal Problems (CTPs) can deal simultaneously with uncertainty and temporal constraints, allowing for the representation of temporal and conditional plans. CTPPs generalize CTPs by adding preferences to the temporal constraints and by allowing fuzzy thresholds for the occurrence of some events. Here we focus on dynamic consistency of CTPPs, the most useful notion of consistency in practice. We describe an algorithm which allows for testing if a CTPP is dynamically consistent and we study its complexity. Simple temporal problems with preferences and uncertainty (STPPUs) are another formalism to model temporal constraints where preference and uncertainty coexist. While uncertainty is CTPPs is modeled via conditions on the execution of variables, in STPPUs it is modelled by means of events whose occurrence time is not known. We consider the relation between CTPPs and STPPUs and we show that the former framework is at least as expressive as the second one. Such a result is obtained by providing a polynomial mapping from STPPUs to CTPPs.     

Syntax and Semantics for a Fuzzy Temporal Constraint Logic

In this paper we establish the relationship between the syntax and semantics of a fuzzy temporal constraint logic (FTCL) proposed by Cárdenas et al. FTCL enables us to express interrelated events by means of fuzzy temporal constraints. Moreover, it provides a resolution principle for performing inferences which take these constraints into account. FTCL is compatible with the theoretical temporal reasoning model proposed by Marín et al. – the Fuzzy Temporal Constraint Networks (FTCN). The main contributions of this paper are, on the one hand, the proofs of the FTCL-deduction and the FTCL-refutation theorems, and, on the other, the proof of the soundness of the refutation by resolution in this formal system, together with an exhaustive study of its completeness.     

Fuzzy branching temporal logic

Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.     

T-STAR: 一种基于关键词的关系数据库时态信息检索方法*

将时态信息融入到信息检索技术中可以有效提高检索效果,时态信息检索已有较多的研究,而现有数据库信息检索方法还缺乏对时态信息有效利用。针对这一研究问题,提出关系数据库上基于时态语义的关键词检索方法,引入时态信息构建时态数据图,设计时态相关性评分机制,在时态图搜索过程中引入时态语义约束,设计基于关键词的时态检索算法。实验验证了该方法可以有效提高数据库信息检索效果,而检索性能并没有降低。     

Planning routes through uncertain territory

Planning routes and executing them requires both topological and metric information. A natural implementation of a ‘cognitive map’ might therefore consist of an assertional data base for topological information and a ‘fuzzy map’ for the metric information. A fuzzy map captures facts about objects by recording their relative positions, orientations, and scales in convenient frames of reference. It is fuzzy in the sense that coordinates are specified to lie in a range rather than having fixed values. The fuzzy map allows easy retrieval of information. The same information is also represented in a discrimination tree, which allows an object to be retrieved given its location and other attributes. The problem of constructing a fuzzy map is more difficult; we present a partial solution, an algorithm that assimilates a fact first by imposing constraints on the fuzzy coordinates of the objects involved, then by rearranging or growing the tree of frames of reference. Route planning is modelled as a process of finding the overall direction and topology of the path, then filling in the details by deciding how to go around barriers. It uses the retrieval algorithms. Our program SPAM carries out all these processes.     

A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

In this paper, we extend the work of Kraft et al. to present a new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques. First, we present a fuzzy agglomerative hierarchical clustering algorithm for clustering documents and to get the document cluster centers of document clusters. Then, we present a method to construct fuzzy logic rules based on the document clusters and their document cluster centers. Finally, we apply the constructed fuzzy logic rules to modify the user's query for query expansion and to guide the information retrieval system to retrieve documents relevant to the user's request. The fuzzy logic rules can represent three kinds of fuzzy relationships (i.e., fuzzy positive association relationship, fuzzy specialization relationship and fuzzy generalization relationship) between index terms. The proposed fuzzy information retrieval method is more flexible and more intelligent than the existing methods due to the fact that it can expand users' queries for fuzzy information retrieval in a more effective manner.     

A knowledge server for reasoning about temporal constraints between classes and instances of events

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.     

On analyzing user preference dynamics with temporal social networks

The preferences adopted by individuals are constantly modified as these are driven by new experiences, natural life evolution and, mainly, influence from friends. Studying these temporal dynamics of user preferences has become increasingly important for personalization tasks in information retrieval and recommendation systems domains. However, existing models are too constrained for capturing the complexity of the underlying phenomenon. Online social networks contain rich information about social interactions and relations. Thus, these become an essential source of knowledge for the understanding of user preferences evolution. In this work, we investigate the interplay between user preferences and social networks over time. First, we propose a temporal preference model able to detect preference change events of a given user. Following this, we use temporal networks concepts to analyze the evolution of social relationships and propose strategies to detect changes in the network structure based on node centrality. Finally, we look for a correlation between preference change events and node centrality change events over Twitter and Jam social music datasets. Our findings show that there is a strong correlation between both change events, specially when modeling social interactions by means of a temporal network.     

Constraint Reasoning about Repeating Events: Satisfaction and Optimization

Effective manipulation of temporal information about periodic events is required for solving complex problems such as long‐range scheduling or querying temporal information. Furthermore, many problems involving repeating events involve the optimization of temporal aspects of these events (e.g., minimizing make‐span in job‐shop scheduling). In this paper, a constraint‐based formulation of reasoning problems with repeating events is presented, and its complexity is analyzed for a range of problems. Optimization constraints are interpreted formally using the Semiring CSPs (SCSP) representation of optimization in constraint reasoning. This allows for familiar algorithms such as branch‐and‐bound to be applied to solving them.     

Using Syntactic-Based Kernels for Classifying Temporal Relations

Temporal relation classification is one of contemporary demanding tasks of natural language processing. This task can be used in various applications such as question answering, summarization, and language specific information retrieval. In this paper, we propose an improved algorithm for classifying temporal relations, between events or between events and time, using support vector machines (SVM). Along with gold-standard corpus features, the proposed method aims at exploiting some useful automatically generated syntactic features to improve the accuracy of classification. Accordingly, a number of novel kernel functions are introduced and evaluated. Our evaluations clearly demonstrate that adding syntactic features results in a considerable improvement over the state-of-the-art method of classifying temporal relations.     

Determining relevance of imprecise temporal intervals for cultural heritage information retrieval

Time is an essential concept in cultural heritage applications. Instances of temporal concepts such as time intervals are used for the annotation of cultural objects and also for querying datasets containing information about these objects. Hence it is important to match query and annotation intervals by examining their similarity or closeness. One of the problems is that in many cases time intervals are imprecise. For example, the boundaries of the “Pre-Roman age” and the “Roman age” are inherently imprecise and it may be difficult to distinguish them with clear-cut intervals. In this paper we apply the fuzzy set theory to model imprecise time intervals in order to determine relevance of the relationship between two time intervals. We present a method for matching query and annotation intervals based on their weighted mutual overlapping and closeness. We present (1) methods for calculating these weights to produce a combined measure and (2) results of comparing the combined measure with human evaluators as a case study. The case study takes into consideration archaeological temporal information, which is in most cases inherently fuzzy, and therefore offers a particularly complex and challenging scenario. The results show that our new combined measure that utilizes different weighted measures together in rankings, performs the best in terms of precision and recall. It should be used when ranking annotation intervals according to a given query interval in cultural heritage information retrieval. Our approach intends to be generalizable: overlapping and closeness may be calculated between any two fuzzy temporal intervals. The presented procedure of using user evaluation results as a basis for assigning weights for overlapping and closeness could potentially be used to reveal weights in other domains and purposes as well.     

A new approach for fuzzy information retrieval based on weighted power-mean averaging operators

An information retrieval system can help users to retrieve documents relevant to the users’ queries. In recent years, some researchers used averaging operators (i.e., Infinite–One operators, Waller–Kraft operators, P-Norm operators and GMA operators) to handle “AND” and “OR” operations of users’ fuzzy queries for fuzzy information retrieval, but they still have some drawbacks, e.g., sometimes query results do not coincide with the intuition of the human being. In this paper, we present new averaging operators, called weighted power-mean averaging (WPMA) operators, based on the weighted power mean for dealing with fuzzy information retrieval to overcome the drawbacks of the existing methods. Furthermore, we also extend the proposed WPMA operators into the extended WPMA operators to handle weighted fuzzy queries for fuzzy information retrieval. The proposed WPMA operators are more flexible and more intelligent than the existing averaging operators to handle users’ fuzzy queries for fuzzy information retrieval.     

Toward a comprehensive treatment of temporal constraints about periodic events

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.     

A Novel User Profile Learning Approach with Fuzzy Constraint for News Retrieval

This study conducts a novel user profile learning approach based on fuzzy constraints. From the vantage of knowledge representation, a fuzzy constraint network is used not only to present the ambiguity of concepts and the diversity between concepts but also to express a single user profile with dependent multisubjects of interest. From the vantage of problem solving, the construction of a user profile is viewed as a problem of fuzzy constraint satisfaction. The subject of interest is extracted by a spreading activation model. To achieve the information filtering of the retrieved data, fuzzy information gain is employed to reduce unnecessary user feedback for matching the user's retrieval requirements.     

Development of a Temporal Extension to Query Travel Behavior Time Paths Using an Object-Oriented GIS

An essential requirement to better understand activity-based travel behavior (ABTB) at the disaggregate level is the development of a spatio-temporal model able to support queries related to activities of individuals or groups of individuals. This paper describes the development and implementation of a temporal extension to a geographic information system (GIS) object-oriented model for the modeling of the time path and the retrieval of its event chaining. In this approach, time path is formulated as a totally time ordered set composed by activity events and trip events, themselves organized into time ordered sets. As sets, the time path and its components can be searched using their respective indexes. A series of methods were built that implement temporal predicates as an interface to temporally query the database. A set of positional operator methods were also designed that transform temporal topological queries into retrieval functions based on set ordering indices. Taken together, the temporal predicates and the positional operator methods define a temporal query extension that meets the retrieval needs of an ABTB database.     

Information retrieval with FROM: The fuzzy relational ontological model

This paper presents FROM, the fuzzy relational ontological model, a novel approach to encode knowledge for information retrieval applications based upon a fuzzy set framework that consider more generic concepts differently from specific terms. Besides the model itself, the paper also presents a retrieval algorithm that exploits FROM features through the application of fuzzy operations that uses this knowledge to extend a user's query based on these fuzzy associations. Experimental results have shown that retrieval with FROM presented better overall performance than other fuzzy‐based approaches for information retrieval. © 2009 Wiley Periodicals, Inc.