Time intervals like ‘tonight’, which are usually not very precise, can be modeled as fuzzy sets. But this causes the problem that the relations between points and intervals and between two intervals, which are usually very trivial, become very complex when the intervals are fuzzy sets. Moreover, there are many different possibilities to define such relations. In this paper I propose a very flexibe operator-based approach to point–interval and interval-interval relations, where the intervals are fuzzy time intervals over the real numbers. The relations yield non-trivial fuzzy values even if the intervals are crisp.

Abstract. The huge number of available documents on the Web makes finding relevant ones a challenging task. The quality of results that traditional full-text search engines provide is still not optimal for many concepts, semantic relations and temporal issues are handled inadequately by full-text search. Ontologies and semantic metadata can provide a solution for these problems. This work examines how ontologies can be optimally exploited during the information retrieval process, and proposes a general framework which is based on ontology-supported semantic metadata generation and ontology-based query expansion. The framework can handle imperfect ontologies and metadata by combining results of simple heuristics, instead of relying on a “perfect ” ontology. This allows integrating results from traditional full-text engines, and thus supports a gradual transition from classical full-text search engines to ontology-based ones. 1

Abstract. When the time span of an event is imprecise, it can be represented by a fuzzy set, called a fuzzy time interval. In this paper we propose a representation for 13 relations that can hold between intervals. Since our model is based on fuzzy orderings of time points, it is not only suitable to express precise relationships between imprecise events (“the mid 1930’s came before the late 1930’s) but also imprecise relationships (“the late 1930’s came long before the early 1990’s). Furthermore we show that our model preserves many of the properties of the 13 relations Allen introduced for crisp time intervals.

Traditional algorithms for description logic (DL) instance retrieval are inefficient for large amounts of underlying data. As description logic is becoming popular in areas such as the Semantic Web and information integration, it is very important to have systems that can reason efficiently over large data sets. In this paper we present an approach to transform description logic axioms described in the SHIQ DL language into a Prolog program. This transformation is done without any knowledge on the particular individuals: they are accessed dynamically during the normal Prolog execution of the generated program. This technique, together with the top-down Prolog execution, implies that only those pieces of data are accessed which are indeed important for answering the query. This allows us to store the individuals in a database instead of memory, which results in better scalability and helps using description logic ontologies directly on top of existing information sources. The transformation process consists of two steps: (1) first we create FOL clauses of restricted form from the DL axioms, (2) then we generate a Prolog program from these. Step (2), which is the focus of the present paper, actually works on more general clauses than those obtainable by applying step (1) to a $\mathcal{SHIQ}$ knowledge base. We first present a base transformation, the output of which can be either executed using a simple interpreter or further extended to executable Prolog code. We then discuss several optimisation techniques, applicable to the output of the base transformation. Some of these techniques are specific to our approach, while others are general enough to be interesting for DL reasoner implementors not using Prolog. We give an overview of DLog, a DL reasoner in Prolog, which is an implementation of the techniques outlined above. We evaluate the performance of DLog and compare it to some widely used DL reasoners, such as RacerPro, Pellet and KAON2.

The temporal boundaries of many real–world events are inherently vague. In this paper, we discuss the problem of qualitative temporal reasoning about such vague events. We show that several interesting reasoning tasks, such as checking satisfiability, checking entailment, and calculating the best truth value bound, can be reduced to reasoning tasks in a well–known point algebra with disjunctions. Furthermore, we identify a maximal tractable subset of qualitative relations to support efficient reasoning. 1

Comparison measures to assess similarity, inclusion, and overlap of fuzzy sets are well known and widely used. This paper goes one step further by bootstrapping a more general concept of relatedness of fuzzy sets based on the relatedness of their elements. We study properties of 6 relatedness measures of this kind, and we illustrate their use in inter-esting applications such as information retrieval, temporal reasoning and recommender systems. 1

Abstract. Although the first digital archives storing a huge number of resources came into existence years ago, they still lack effective retrieval methods. The most obvious example is the World Wide Web: search engines are improved constantly, however, their hits are still unsatisfactory apart from simplest queries. Most prosperous solutions employ user contexts to estimate the user’s information demand and use this information to deliver more adequate results. In the current paper we introduce the idea of resource context-based information retrieval. In this approach, semantic context description is assigned to each digital resource known to the system and this semantic metadata are exploited by each step during an intelligent search process. Our solution is implemented and evaluated in the VICODI project, as part of a web portal for European history. 1

Large amounts of data in modern information systems, such as the World Wide Web, require innovative information retrieval techniques to effectively satisfy users ’ information need. A promising approach is to exploit document semantics in the IR process. For this purpose, high-quality semantic metadata is needed. This paper introduces a method to automatically create semantic metadata by using ontologically enhanced versions of common information extraction methods, such as named entity recognition and coreference resolution. Furthermore, this work also proposes the application of ontology-specific heuristic rules to further improve the quality of generated metadata. The results of our method was evaluated using a small test collection.

Abstract: The paper presents time-related part of PSI 1 theoretical framework. In comparison to other theories of time based on interval logics our approach presents the advancement by introducing fuzziness of time intervals as transition periods at beginnings and endings. It is argued that, though quite simple (discrete, linear, and anisotropic), our theoretical model is expressive enough to be used as a logical formalism for reasoning about stochastic, unpredictable, weakly defined action and process flows. A metric and a rich set of axiomatic relationships among time intervals are introduced for that. Further on, a means for modeling and reasoning about singular, repeated, regular events and actions having phases and vague durations is elaborated. Presented theory of time is used for modeling and reasoning about events, environmental influences, happenings, and actions while planning and scheduling in our simulations of dynamic engineering design processes. 1

Abstract — Fuzzy qualitative temporal relations have been proposed to reason about events whose temporal boundaries are ill– defined. Although the corresponding reasoning tasks are in the same complexity class as their crisp counterparts, in practice, the scalability of fuzzy temporal reasoners may be insufficient for applications which require a high expressivity and deal with a large number of events. On the other hand, transitivity rules can be used to make sound, but incomplete inferences in polynomial time, utilizing a variant of Allen’s path–consistency algorithm. The aim of this paper is to investigate how this polynomial time algorithm can be improved without altering its time complexity. To this end, we establish a characterization of 2–consistency of fuzzy temporal relations and provide transitivity rules which are significantly stronger than those resulting from straightforwardly generalizing transitivity rules for crisp temporal relations. We furthermore provide experimental evidence for the effectiveness of our improved algorithm.