Melisachew Wudage Chekol

Scaling Probabilistic Temporal Query Evaluation

Open information extraction has driven automatic construction of (temporal) knowledge graphs (e.g. YAGO) that maintain probabilistic (temporal) facts and inference rules. One of the most important tasks in these knowledge graphs is query evaluation. This task is well known to be #P-hard. One of the bottlenecks of probabilistic (temporal) query evaluation is finding efficient ways of grounding the query and inference rules, to generate a factor graph that can be used for approximate query evaluation or to retrieve lineages of queries for exact evaluation. In this work, we propose the PRATiQUE (PRobAbilistic Temporal QUery Evaluation) framework for scalable temporal query evaluation. It harnesses the structure of temporal inference rules for efficient in-database grounding, i.e., it uses partitions to store structurally equivalent rules. Besides,PRATiQUE leverages a state-of-the-art Gibbs sampler to compute marginal probabilities of query answers. We report on an extensive experimental evaluation, which confirms the efficiency of our proposal.

Containment of Expressive SPARQL Navigational Queries

Query containment is one of the building block of query optimization techniques. In the relational world, query containment is a well-studied problem. At the same time it is well-understood that relational queries are not enough to cope with graph-structured data, where one is interested in expressing queries that capture navigation in the graph. This paper contributes a study on the problem of query containment for an expressive class of navigational queries called Extended Property Paths (EPPs). EPPs are more expressive than previous navigational extension of SPARQL (e.g., nested regular expressions) as they allow to express path conjunction and path negation, among others. We attack the problem of EPPs containment and provide complexity bounds.

Towards partition-aware lifted inference

There is an ever increasing number of rule learning algorithms and tools for automatic knowledge base (KB) construction. These tools often produce weighted rules and facts that make up a probabilistic KB (PKB). In such a PKB, probabilistic inference is used in order to perform marginal inference, consistency checking and other tasks. However, in general, inference is known to be intractable. Hence, recently, there are a number of studies aimed at lifting (making tractable or approximating) inference by exploiting symmetries in the structure of a PKB. These studies alleviate grounding entirely a given PKB which can generate a sizable factor graph for inference (e.g. to compute the probability of a query). In line with this, we propose a novel technique to automatically partition rules based on their structure for efficient parallel grounding. In addition, we perform query expansion so as to generate a factor graph small enough to be used for efficient probability computation. We present a novel approximate marginal inference algorithm that uses N-hop subgraph extraction and query expansion. Moreover, we show that our system is much faster than state-of-the-art systems.

Tractable reasoning in probabilistic OWL profiles

Through the advance of information extraction and data mining, a number of knowledge bases (KBs) have been created, for instance, NELL and Google knowledge Vault. In line with this, probabilistic extensions of various description logics have been proposed for reasoning in probabilistic KBs. However, most of these languages are not tractable impeding their practical use. Since present-day KBs can be very large, tractable reasoning is essential. In this work, we propose probabilistic extensions of OWL 2 RL and OWL 2 EL by using probabilistic soft logic for which inference is known to be tractable. We show that inference in probabilistic extensions of OWL 2 RL and OWL 2 EL remains tractable. We present experimental results over a YAGO KB that contains hundreds of schema axioms and thousands of instances.

Towards Probabilistic Bitemporal 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 (KGs) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KGs, such as NELL, the facts in the KGs 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 only 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 probabilistic temporal knowledge graphs. We report our evaluation results of the proposed approach.

Deriving Validity Time in Knowledge Graph

Knowledge Graphs (KGs) are a popular means to represent knowledge on the Web, typically in the form of node/edge labelled directed graphs. We consider temporal KGs, in which edges are further annotated with time intervals, reflecting when the relationship between entities held in time. In this paper, we focus on the task of predicting time validity for unannotated edges. We introduce the problem as a variation of relational embedding. We adapt existing approaches, and explore the importance example selection and the incorporation of side information in the learning process. We present our experimental evaluation in details.

SPARQL Query Containment under Schema

Query containment is defined as the problem of determining if the result of a query is included in the result of another query for any dataset. It has major applications in query optimization and knowledge base verification. To date, testing query containment has been performed using different techniques: containment mapping, canonical databases, automata theory techniques and through a reduction to the validity problem in logic. Here, we use the latter technique to test containment of SPARQL queries using an expressive modal logic called

Automated Fine-Grained Trust Assessment in Federated Knowledge Bases

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