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

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Uncertain Temporal Knowledge Graphs

Abstract. Temporal data can be found in various sources from patient histories, purchase histories, employee histories, to web logs. Recent advances in open information extraction have paved the way for automatic construction of knowledge graphs (kgs) from such sources. Often the extraction tools used to construct kgs produce facts and rules along with their confidence scores, leading to the notion of uncertain temporal kgs. The facts and rules contained in these graphs tend to be noisy and erroneous due to either the accuracy of the extraction tools or uncertainty in the source data. In this work, we use a numerical extension of Markov logic networks to provide formal syntax and semantics for uncertain temporal kgs. Moreover, we propose a set of datalog constraints with inequalities, that extend the underlying schema of the kgs and help in resolving conflicting facts. Finally, we characterize the complexity of two important queries, maximum a-posteriori and conditional probability inference, for uncertain temporal kgs

Temporal Reasoning for RDF(S): A Markov Logic based Approach

In this work, we propose a formalism that is suitable to carry out temporal reasoning for probabilistic knowledge bases. In particular, we focus on detecting erroneous statements by exploiting temporal relations of facts. Therefore, we rely on RDF(S) and its associating entailment rules which provide a data representation model as well as a basic logical expressiveness. Moreover, we use Allen 19s interval algebra to express the relations of facts based on their associated temporal information. We carry out reasoning by transforming the statements and constraints to Markov Logic and compute the most probable consistent state (MAP inference) with respect to the defined constraints. Moreover, we evaluate the proposed approach in order to demonstrate its practicality and flexibility

Accommodating Complex Chained Prepositional Phrases in Natural Language Query Interface to an Event-Based Triplestore

Building Natural language query interfaces (NLI) to databases is one the most interesting and challenging fields of study for computer scientists and researchers. There have been many advancements and achievements in this area that enables NLIs to operate more efficiently and have wide NL coverage. However, there exists some shortcomings in query interface to semantic web triplestores. Some researchers have attempted to extend the range of queries that can be answered. However, only a few techniques can handle queries containing complex chained prepositional phrases. This thesis involves extending an existing method that can accommodate prepositional phrases to also be able to handle when..., where..., and with what... type queries. The approach developed is implemented in the Miranda programing environment

Ontology evolution: a process-centric survey

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage