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

On the containment of SPARQL queries under entailment regimes

Most description logics (DL) query languages allow instance retrieval from an ABox. However, SPARQL is a schema query language allowing access to the TBox (in addition to the ABox). Moreover, its entailment regimes enable to take into account knowledge inferred from knowledge bases in the query answering process. This provides a new perspective for the containment problem. In this paper, we study the containment of SPARQL queries over OWL EL axioms under entailment. OWL EL is the language used by many large scale ontologies and is based on EL++. The main contribution is a novel approach to rewriting queries using SPARQL property paths and the μ-calculus in order to reduce containment test under entailment into validity check in the μ-calculus

Towards Log-Linear Logics with Concrete Domains

We present $\mathcal{MEL}^{++}$ (M denotes Markov logic networks) an extension of the log-linear description logics $\mathcal{EL}^{++}$-LL with concrete domains, nominals, and instances. We use Markov logic networks (MLNs) in order to find the most probable, classified and coherent $\mathcal{EL}^{++}$ ontology from an $\mathcal{MEL}^{++}$ knowledge base. In particular, we develop a novel way to deal with concrete domains (also known as datatypes) by extending MLN's cutting plane inference (CPI) algorithm.Comment: StarAI201

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

A Study on the Correspondence between FCA and ELI Ontologies

Abstract. The description logic EL has been used to support ontology design in various domains, and especially in biology and medicine. EL is known for its efficient reasoning and query answering capabilities. By contrast, ontology design and query answering can be supported and guided within an FCA framework. Accordingly, in this paper, we propose a formal transformation of ELI (an extension of EL with inverse roles) ontologies into an FCA framework, i.e. KELI, and we provide a formal characterization of this transformation. Then we show that SPARQL query answering over ELI ontologies can be reduced to lattice query answering over KELI concept lattices. This simplifies the query answering task and shows that some basic semantic web tasks can be improved when considered from an FCA perspective

A Study on the Correspondence between FCA and ELI Ontologies

International audienceThe description logic EL has been used to support ontology design in various domains, and especially in biology and medicine. EL is known for its efficient reasoning and query answering capabilities. By contrast, ontology design and query answering can be supported and guided within an FCA framework. Accordingly, in this paper, we propose a formal transformation of ELI (an extension of EL with inverse roles) ontologies into an FCA framework, i.e. KELI, and we provide a formal characterization of this transformation. Then we show that SPARQL query answering over ELI ontologies can be reduced to lattice query answering over KELI concept lattices. This simplifies the query answering task and shows that some basic semantic web tasks can be improved when considered from an FCA perspective

TeCoRe: temporal conflict resolution in knowledge graphs

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs (kgs), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing kgs focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in kgs are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate TeCoRe, a system for temporal infer- ence and conflict resolution in uncertain temporal knowledge graphs (utkgs). At the heart of TeCoRe are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in utkgs. TeCoRe provides an inter- face allowing to select utkgs and editing constraints; shows the maximal consistent subset of the utkg, and displays statistics (e.g., number of noisy facts removed) about the debugging process

BOLD: Knowledge Graph Exploration and Analysis Platform

The linked open data (LOD) cloud maintains several interlinked knowledge graphs. These graphs span various domains such as government, media, life sciences, etc. The graphs are often manually curated or automatically extracted (e.g. YAGO—Yet Another Great Ontology) using information extraction techniques. They are used in various applications such as data governance, fraud detection, fact checking, etc. Although the graphs in LOD are widely used, they do not contain metadata about their representativeness (distribution of key features). Since most of the graphs are automatically curated, bias can manifest due to sensitive features and their causal influences, or through under (over)representation of certain entities (e.g. people) and relations (e.g. president-of, works-for). The aim of this work is to develop a system to automatically generate bias profiles (metadata about the representativeness of data) for knowledge graphs. As a result, the metadata can be used as a guide for users to choose bias free (balanced) datasets for their studies. Moreover, it enables researchers to quickly gauge the relevance of a graph for a problem at hand (e.g. classification task)

Evaluating and benchmarking SPARQL query containment solvers

International audienceQuery containment is the problem of deciding if the answers to a query are included in those of another query for any queried database. This problem is very important for query optimization purposes. In the SPARQL context, it can be equally useful. This problem has recently been investigated theoretically and some query containment solvers are available. Yet, there were no benchmarks to compare theses systems and foster their improvement. In order to experimentally assess implementation strengths and limitations, we provide a first SPARQL containment test benchmark. It has been designed with respect to both the capabilities of existing solvers and the study of typical queries. Some solvers support optional constructs and cycles, while other solvers support projection, union of conjunctive queries and RDF Schemas. No solver currently supports all these features or OWL entailment regimes. The study of query demographics on DBPedia logs shows that the vast majority of queries are acyclic and a significant part of them uses UNION or projection. We thus test available solvers on their domain of applicability on three different benchmark suites. These experiments show that (i) tested solutions are overall functionally correct, (ii) in spite of its complexity, SPARQL query containment is practicable for acyclic queries, (iii) state-of-the-art solvers are at an early stage both in ter