Time-Aware Probabilistic Knowledge Graphs

The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model

The Events method for temporal integrity constraint handling in bitemporal deductive databases

A bitemporal deductive database is a deductive database that supports valid and transaction time. A temporal integrity constraint deals with only valid time, only transaction time or both times. A set of facts to be einserted and deleted in a bitemporal deductive database can be done in a past, present or future valid time and at current transaction time. The temporal integrity constraint handling in bitemporal deductive databases causes that the maintenance of consistency becomes more complex than another databases. The $events method$is based on applying transition and event rules, which explicitly define the insertions and deletions given by a database update. In the conceptual model, we augment the database with temporal transition and event rules and then standard SLDNF-resolution can be used to verify that a transaction does not violate any temporal integrity constraint. In the representational data model, we use time point-based intervals to store temporal information. In this paper, we adapt the$$$events method$$$ for handling temporal integrity constraints. Finally, we present the interaction between the above-mentioned conceptual and representational data models.Postprint (published version

Modeling temporal dimensions of semistructured data

In this paper we propose an approach to manage in a correct way valid time semantics for semistructured temporal clinical information. In particular, we use a graph-based data model to represent radiological clinical data, focusing on the patient model of the well known DICOM standard, and define the set of (graphical) constraints needed to guarantee that the history of the given application domain is consistent

6 Access Methods and Query Processing Techniques

The performance of a database management system (DBMS) is fundamentally dependent on the access methods and query processing techniques available to the system. Traditionally, relational DBMSs have relied on well-known access methods, such as the ubiquitous B +-tree, hashing with chaining, and, in som

On the Semantics of "Now" in Databases

While "now" is expressed in SQL as CURRENT-TIMESTAMP within queries, this value cannot be stored in the database. However, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as "now," "until-changed," "â," "@" and "-." Time variables are very desirable, but their use also leads to a new type of database, consisting of tuples with variables, termed a variable database. This paper proposes a framework for defining the semantics of the variable databases of temporal relational data models. A framework is presented because several reasonable meanings may be given to databases that use some of the specific temporal variables that have appeared in the literature. Using the framework, the paper defines a useful semantics for such databases. Because situations occur where the existing time variables are inadequate, two new types of modeling entities that address these shortcomings, timestamps which we call now-relative and now-relative indeterminate, are introduced and defined within the framework. Moreover, the paper provides a foundation, using algebraic bind operators, for the querying of variable databases via existing query languages. This transition to variable databases presented here requires minimal change to the query processor. Finally, to underline the practical feasibility of variable databases, we show that database variables can be precisely specified and efficiently implemented in conventional query languages, such as SQL, and in temporal query languages, such as TSQL2.Information Systems Working Papers Serie

Applying transition rules to bitemporal deductive databases for integrity constraint checking

A bitemporal deductive database is a deductive database that supports valid and transaction time. A set of facts to be inserted and/or deleted in a bitemporal deductive database can be done in a past, present or future valid time. This circumstance causes that the maintenance of database consistency becomes more hard. In this paper, we present a new approach to reduce the difficulty of this problem, based on applying transition and event rules, which explicitly define the insertions and deletions given by a database update. Transition rules range over all the possible cases in which an update could violate some integrity contraint. Although, we have a large amount of transition rules, for each one we argue its utility or we eliminate it. We augment a database with this set of transition and event rules and then standard SLDNF resolution can be used to check satisfaction of integrity constraints.Peer ReviewedPostprint (author's final draft

Challenging Issues of Spatio-Temporal Data Mining

The spatio-temporal database (STDB) has received considerable attention during the past few years, due to the emergence of numerous applications (e.g., flight control systems, weather forecast, mobile computing, etc.) that demand efficient management of moving objects. These applications record objects' geographical locations (sometimes also shapes) at various timestamps and support queries that explore their historical and future (predictive) behaviors. The STDB significantly extends the traditional spatial database, which deals with only stationary data and hence is inapplicable to moving objects, whose dynamic behavior requires re-investigation of numerous topics including data modeling, indexes, and the related query algorithms. In many application areas, huge amounts of data are generated, explicitly or implicitly containing spatial or spatiotemporal information. However, the ability to analyze these data remains inadequate, and the need for adapted data mining tools becomes a major challenge. In this paper, we have presented the challenging issues of spatio-temporal data mining. Keywords: database, data mining, spatial, temporal, spatio-tempora