LARS: A Logic-Based Framework for Analytic Reasoning over Streams

Beck, Harald; Dao-Tran, Minh; Eiter, Thomas; Fink, Michael

doi:10.1007/978-3-319-73117-9_6

Cited by 21 publications

(19 citation statements)

References 21 publications

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“…First, this requires temporal knowledge to be expressed in the schema information. Several formalisms have been investigated for representing temporal knowledge over data streams [28], [29], [30]. In particular, [30] shows how temporal rules can be applied for efficient data stream processing.…”

Section: Rule Learning From Kg Streamsmentioning

confidence: 99%

An Embedding-Based Approach to Rule Learning in Knowledge Graphs

Omran

Wang

2021

IEEE Trans. Knowl. Data Eng.

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It is natural and effective to use rules for representing explicit knowledge in knowledge graphs. However, it is challenging to learn rules automatically from very large knowledge graphs such as Freebase and YAGO. This paper presents a new approach, RLvLR (Rule Learning via Learning Representations), to learning rules from large knowledge graphs by using the technique of embedding in representation learning together with a new sampling method. Based on RLvLR, a new method RLvLR-Stream is developed for learning rules from streams of knowledge graphs. Both RLvLR and RLvLR-Stream have been implemented and experiments conducted to validate the proposed methods regarding the tasks of rule learning and link prediction. Experimental results show that our systems are able to handle the task of rule learning from large knowledge graphs with high accuracy and outperform some state-of-the-art systems. Specifically, for massive knowledge graphs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in knowledge graphs such as AMIE+. In the setting of knowledge graph streams, RLvLR-Stream significantly improved RLvLR for both rule learning and link prediction.

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Section: Rule Learning From Kg Streamsmentioning

confidence: 99%

An Embedding-Based Approach to Rule Learning in Knowledge Graphs

Omran

Wang

2021

IEEE Trans. Knowl. Data Eng.

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“…A different approach to enhance the scalability of expressive stream reasoning is based on incremental methods. There are two reasoners proposed recently based on the LARS framework [7], namely Ticker [8] and Laser [6]. Ticker translates the plain LARS (more specifically, a fragment of LARS) to ASP and supports two reasoning strategies: one utilizes Clingo with a static ASP encoding and the other applies truth maintenance techniques to adjust models incrementally.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Pham

Ali

Mileo

2019

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Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-ofthe-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.

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“…In many stream reasoning systems, the collected data is transformed into an abstract logical representation, and situation recognition is performed by some kind of logical inference over the abstract logical representation. There are stream reasoning approaches based on rules, such as answer set programming [1][2][3], (datalog) rules and approaches based on ontology languages [4][5][6][7]. The ontology-based approaches mostly employ the framework of ontology-mediated queries, where forms of conjunctive queries are answered over data that is enriched by an ontology, to perform situation recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In stream reasoning approaches in general, the temporal information is often represented by associating data with the time point at which it was collected. Regarding the language in which queries can be formulated, many variations that capture the temporal aspect have been studied in recent research [2][3][4]13]. Window-based approaches admit to concentrate on recent continuous substreams when answering queries over the data, and are the most prominent in implemented systems [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the language in which queries can be formulated, many variations that capture the temporal aspect have been studied in recent research [2][3][4]13]. Window-based approaches admit to concentrate on recent continuous substreams when answering queries over the data, and are the most prominent in implemented systems [2][3][4]. Ontology-based approaches mostly cover classical temporal logics such as linear temporal logic (LTL) [14] (see [5,[15][16][17][18]) or metric variants of temporal logics [19,20] to enrich the query language.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-mediated query answering over temporal and inconsistent data

Bourgaux

Koopmann

Turhan

2019

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Stream-based reasoning systems process data stemming from different sources that are received over time. In this kind of applications, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontologymediated query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic (LTL), and consider these under three inconsistency-tolerant semantics that have been introduced for querying inconsistent description logic knowledge bases. We investigate their complexity for temporal EL ⊥ and DL-LiteR knowledge bases. In particular, we consider two different cases, depending on the presence of negations in the query. Furthermore, we complete the complexity picture for the consistent case. We also provide two approaches toward practical algorithms for inconsistency-tolerant temporal query answering.

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LARS: A Logic-Based Framework for Analytic Reasoning over Streams

Cited by 21 publications

References 21 publications

An Embedding-Based Approach to Rule Learning in Knowledge Graphs

An Embedding-Based Approach to Rule Learning in Knowledge Graphs

Enhancing the scalability of expressive stream reasoning via input-driven parallelization

Ontology-mediated query answering over temporal and inconsistent data

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