Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Case Adaptation with Qualitative Algebras

This paper proposes an approach for the adaptation of spatial or temporal cases in a case-based reasoning system. Qualitative algebras are used as spatial and temporal knowledge representation languages. The intuition behind this adaptation approach is to apply a substitution and then repair potential inconsistencies, thanks to belief revision on qualitative algebras. A temporal example from the cooking domain is given. (The paper on which this extended abstract is based was the recipient of the best paper award of the 2012 International Conference on Case-Based Reasoning.

Temporal Representation and Reasoning in OWL 2

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as “dynamic” information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as “before” or “after”, for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the “4D-fluents” or, alternatively, the “N-ary relations” mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach

Integrating sensor streams in pHealth networks

Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries

Representing time and space for the semantic web

Representation of temporal and spatial information for the Semantic Web often involves qualitative defined information (i.e., information described using natural language terms such as "before" or "overlaps") since precise dates or coordinates are not always available. This work proposes several temporal representations for time points and intervals and spatial topological representations in ontologies by means of OWL properties and reasoning rules in SWRL. All representations are fully compliant with existing Semantic Web standards and W3C recommendations. Although qualitative representations for temporal interval and point relations and spatial topological relations exist, this is the first work proposing representations combining qualitative and quantitative information for the Semantic Web. In addition to this, several existing and proposed approaches are compared using different reasoners and experimental results are presented in detail. The proposed approach is applied to topological relations (RCC5 and RCC8) supporting both qualitative and quantitative (i.e., using coordinates) spatial relations. Experimental results illustrate that reasoning performance differs greatly between different representations and reasoners. To the best of our knowledge, this is the first such experimental evaluation of both qualitative and quantitative Semantic Web temporal and spatial representations. In addition to the above, querying performance using SPARQL is evaluated. Evaluation results demonstrate that extracting qualitative relations from quantitative representations using reasoning rules and querying qualitative relations instead of directly querying quantitative representations increases performance at query time

Computational Treatment of Temporal Notions -- The CTTN-System

The CTTN-system is a computer program which provides advanced processing or temporal notions. The basic data structures of the CTTN-system are time points, crisp and fuzzy time intervals, labelled partitionings of the time line, durations, and calendar systems. The labelled partitionings are used to model periodic temporal notions, quite regular ones like years, months etc., partially regular ones like timetables, but also very irregular ones like, for example, dates of a conference series. These data structures can be used in the temporal specification language GeTS (GeoTemporal Specifications). GeTS is a functional specification and programming language with a number of built-in constructs for specifying customized temporal notions. CTTN is implemented as a Web server and as a C++ library. This paper gives a short overview over the current state of the system and its components

Generating Automated News to Explain the Meaning of Sensor Data

An important competence of human data analysts is to interpret and explain the meaning of the results of data analysis to end-users. However, existing automatic solutions for intelligent data analysis provide limited help to interpret and communicate information to non-expert users. In this paper we present a general approach to generating explanatory descriptions about the meaning of quantitative sensor data. We propose a type of web application: a virtual newspaper with automatically generated news stories that describe the meaning of sensor data. This solution integrates a variety of techniques from intelligent data analysis into a web-based multimedia presentation system. We validated our approach in a real world problem and demonstrate its generality using data sets from several domains. Our experience shows that this solution can facilitate the use of sensor data by general users and, therefore, can increase the utility of sensor network infrastructures

TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS

This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies

Extracting Temporal Expressions from Unstructured Open Resources

AETAS is an end-to-end system with SOA approach that retrieves plain text data from web and blog news and represents and stores them in RDF, with a special focus on their temporal dimension. The system allows users to acquire, browse and query Linked Data obtained from unstructured sources