Cetto, Matthias

Freitas, Andre

Handschuh, Siegfried

Niklaus, Christina

English

We introduce Graphene, an Open IE system whose goal is to generate accurate, meaningful and complete propositions that may facilitate a variety of downstream semantic applications. For this purpose, we transform syntactically complex input sentences into clean, compact structures in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them in order to maintain their semantic relationship. In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions

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The University of Manchester ResearchGraphene: A Context-Preserving Open InformationExtraction SystemDocument VersionAccepted author manuscriptLink to publication record in Manchester Research ExplorerCitation for published version (APA):Cetto, M., Niklaus, C., Freitas, A., & Handschuh, S. (2018). Graphene: A Context-Preserving Open InformationExtraction System. In Proceedings of the 27th International Conference on Computational Linguisticshttps://arxiv.org/abs/1808.09463Published in:Proceedings of the 27th International Conference on Computational LinguisticsCiting this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. 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Jun. 2022Graphene: A Context-Preserving Open Information Extraction SystemMatthias Cetto1, Christina Niklaus1, André Freitas2, and Siegfried Handschuh11 Faculty of Computer Science and Mathematics, University of Passau{matthias.cetto, christina.niklaus, siegfried.handschuh}@uni-passau.de2 School of Computer Science, University of Manchesterandre.freitas@manchester.ac.ukAbstractWe introduce Graphene, an Open IE system whose goal is to generate accurate, meaningful andcomplete propositions that may facilitate a variety of downstream semantic applications. For thispurpose, we transform syntactically complex input sentences into clean, compact structures inthe form of core facts and accompanying contexts, while identifying the rhetorical relations thathold between them in order to maintain their semantic relationship. In that way, we preserve thecontext of the relational tuples extracted from a source sentence, generating a novel lightweightsemantic representation for Open IE that enhances the expressiveness of the extracted proposi-tions.1 IntroductionInformation Extraction (IE) is the task of turning the unstructured information expressed in natural lan-guage (NL) text into a structured representation in the form of relational tuples consisting of a set ofarguments and a phrase denoting a semantic relation between them: 〈arg1; rel; arg2〉. Unlike traditionalIE methods, Open IE is not limited to a small set of target relations known in advance, but rather ex-tracts all types of relations found in a text. In that way, it facilitates the domain-independent discoveryof relations extracted from text and scales to large, heterogeneous corpora such as the Web. Since itsintroduction by Banko et al. (2007), a large body of work on the task of Open IE has been described.By analyzing the output of state-of-the-art systems (e.g., (Mausam et al., 2012; Del Corro and Gemulla,2013; Angeli et al., 2015)), we observed three common shortcomings.First, relations often span over long nested structures or are presented in a non-canonical form thatcannot be easily captured by a small set of extraction patterns. Therefore, such relations are commonlymissed by state-of-the-art approaches. Second, current Open IE systems tend to extract propositions withlong argument phrases that can be further decomposed into meaningful propositions, with each of themrepresenting a separate fact. Overly specific constituents that mix multiple - potentially semanticallyunrelated - propositions are difficult to handle for downstream applications, such as question answering(QA) or textual entailment tasks. Instead, such approaches benefit from extractions that are as compactas possible. Third, state-of-the-art Open IE systems lack the expressiveness needed to properly repre-sent complex assertions, resulting in incomplete, uninformative or incoherent propositions that have nomeaningful interpretation or miss critical information asserted in the input sentence.To overcome these limitations, we developed an Open IE framework called ”Graphene” that trans-forms syntactically complex NL sentences into clean, compact structures that present a canonical formwhich facilitates the extraction of accurate, meaningful and complete propositions. The contributionsof our work are two-fold. First, to remove the complexity of determining intricate predicate-argumentstructures with variable arity from syntactically complex input sentences, we propose a two-layeredtransformation process consisting of a clausal and phrasal disembedding layer. It removes clauses andphrases that convey no central information from the input and converts them into independent con-text sentences, thereby reducing the source sentence to its main information. In that way, the inputThis work is licenced under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/arXiv:1808.09463v1  [cs.CL]  28 Aug 2018is transformed into a novel hierarchical representation in the form of core facts and accompany-ing contexts. Second, we identify the rhetorical relations by which core sentences and their as-sociated contexts are connected in order to preserve their semantic relationship. These two in-novations enable us to enrich extracted relational tuples of the form 〈arg1; rel; arg2〉 with contextualinformation that further specifies the tuple and to establish semantic links between them, resulting ina novel lightweight semantic representation for Open IE that provides highly informative extractionsand thus supports their interpretability in downstream applications. The source code is available athttps://github.com/Lambda-3/Graphene.2 The System in a NutshellGraphene makes use of a two-layered transformation stage consisting of a clausal and phrasal disembed-ding layer, which is followed by a final relation extraction (RE) stage. It takes a text document as aninput and returns a set of semantically typed and interconnected relational tuples. The workflow of ourapproach is displayed in Figure 1.Input-Document[...] Althoughthe Treasurywill announcedetails of theNovemberrefunding onMonday, thefunding willbe delayedif Congressand PresidentBush fail toincrease theTreasury’sborrowingcapacity. [...]→Transformation StageDOCUMENT-ROOTCoordinationContrastSubordinationConditionCoordinationListPresident Bush fail toincrease the Treasury’sborrowing capacity.Congress fail toincrease the Treasury’sborrowing capacity.core coreThe fundingwill be delayed.core contextThe Treasury willannounce details ofthe November refunding.TEMPORAL(on Monday)core corecore→Relation ExtractionThe Treasury willannounce details [...]TEMPORAL(on Monday)The fundingwill be delayed.Congress failto increase [...]President Bush failto increase [...]〈The Treasury;will announce;details [...]〉TEMPORAL(on Monday)〈The funding;will be delayed; 〉〈Congress; fail;to increase [...]〉〈President Bush;fail; to increase [...]〉ContrastConditionConditionListFigure 1: Extraction workflow for an example sentence.2.1 Transformation StageDuring the transformation process, source sentences that present a complex linguistic structure are con-verted into a hierarchical representation of core facts and associated contexts that are connected by rhetor-ical relations capturing their semantic relationship similar to Rhetorical Structure Theory (RST) (Mannand Thompson, 1988). These compact, syntactically sound structures ease the problem of recognizingpredicate-argument relations that are contained in the input without losing their semantic dependencies.Clausal Disembedding. In the clausal disembedding layer, we split up complex multi-clause sentencesthat are composed of coordinated and subordinated clauses, relative clauses, or attributions into sim-pler, stand-alone sentences that contain one clause each. This is done in a recursive fashion so that weobtain a hierarchical structure of the transformation process comparable to the diagrams used in RST.As opposed to RST, however, the transformation process is carried out in a top-down fashion, startingwith the input document and using a set of hand-crafted syntactic rule patterns that define how to splitup, transform and recurse on complex syntactic patterns1. Each split will create two or more simplifiedsentences that are connected with information about (1) their constituency type depicting their seman-tic relevance (coordinate or subordinate) and (2) the rhetorical relation that holds between them. Theconstituency type infers the concept of nuclearity from RST, where coordinate sentences (which we callcore sentences) represent nucleus spans that embody the central part of information, while subordinatesentences (context sentences) represent satellite spans that provide background information on the nu-cleus. The classification of the rhetorical relations is based on both syntactic and lexical features. While1The complete rule set can be found online: https://github.com/Lambda-3/Graphene/blob/master/wiki/supplementary/syntactic-simplification-patterns.pdfformer are manifested in the phrasal composition of a sentence’s phrasal parse tree, latter rely on a setof manually defined cue phrases. In this way, a hierarchical tree representation of the recursive transfor-mation process for the whole document is constructed which we denote as discourse tree. Its leaf nodesrepresent the simplified sentences that were generated during the clausal disembedding layer.Phrasal Disembedding. After recursively dividing multi-clause sentences into stand-alone sentencesthat contain one clause each, they are further simplified on a phrasal level. For this purpose, sentencesare processed separately and transformed into simpler structures by extracting the following phrasalcomponents from the input: prepositional phrases, participial phrases, adjectival/adverbial phrases,appositive phrases, lead noun phrases, coordinations of verb phrases, enumerations of noun phrases andpurposes. This task is assisted by the sentence simplification system described in Niklaus et al. (2016).2.2 Relation ExtractionAfter the transformation stage, RE is performed by using the simplified sentences as an input. The frame-work is designed to accept any type of RE implementation which is able to extract relational tuples fromsingle sentences. The identified rhetorical relations from the transformation stage are then mapped to thecorresponding relational tuples in the form of simple and linked contextual arguments (see Section 3).As a result, different approaches for RE can be complemented with contextual information that furtherspecifies the extracted relational tuples. In that way, a new layer of semantics is added to the task of REthat can be used in other NLP tasks (see Section 6).3 Output FormatIn order to represent contextual relations between propositions, the default representation of a relationaltuple of the form 〈arg1; rel; arg2〉 needs to be extended. Therefore, we present a novel lightweightsemantic representation for Open IE that is both machine processable and human readable. It extends abinary subject-predicate-object tuple t← (rel, argsubj , argobj) with: a unique identifier id; informationabout the contextual hierarchy, the so-called context-layer cl; and two sets of semantically classified con-textual arguments CS (simple contextual arguments) and CL (linked contextual arguments), yielding thefinal representation of (id, cl, t, CS , CL) tuples. The context-layer cl encodes the contextual hierarchy ofcore and contextual facts. Propositions with a context-layer of 0 carry the core information of a sentence,whereas propositions with a context-layer of cl > 0 provide contextual information about propositionswith a context-layer of cl−1. Both types of contextual arguments CS and CL provide (semantically clas-sified) contextual information about the statement expressed in t. Whereas a simple contextual argumentcS ∈ CS , cS ← (s, r) contains a textual expression s that is classified by the semantic relation r, a linkedcontextual argument cL ∈ CL, cL ← (id(z), r) refers to the content expressed in another proposition z.To facilitate the inspection of the extracted propositions, a human-readable format, called RDF-NL, isgenerated by Graphene (see Figure 2). In this format, propositions are grouped by sentences in whichthey occur and are represented by tab-separated strings for the identifier id, context-layer cl and the coreextraction that is represented by the binary relational tuple t← (rel, argsubj , argobj): subject argumentargsubj , relation name r and object argument argobj . Contextual arguments (CS and CL) are indicatedby an extra indentation level to their parent tuples. The representation of a contextual argument consistsof a type string and a tab-separated content. The type string encodes both the context type (S for asimple contextual argument cS ∈ CS and L for a linked contextual argument cL ∈ CL) and the classifiedsemantic relation (e.g. Cause, Purpose), if present. The content of a simple contextual argument is thetextual expression, whereas the content of a linked contextual argument is the identifier of the targetproposition.Besides, the framework can materialize its relations into a graph serialized under the N-Triples2 spec-ification of the Resource Description Framework (RDF) standard. In that way, the consumption of theextracted relations by downstream applications is facilitated. A detailed description as well as someexamples of the machine-readable RDF format are available online3.2https://www.w3.org/TR/n-triples3https://github.com/Lambda-3/Graphene/blob/master/wiki/RDF-Format.mdAlthough the Treasury will announce details of the November refunding on Monday, the fundingwill be delayed if Congress and President Bush fail to increase the Treasury’s borrowing capacity.#1 0 the Treasury will announce details of the November refundingS:TEMPORAL on MondayL:CONTRAST #2#2 0 the funding will be delayedL:CONTRAST #1L:CONDITION #3L:CONDITION #4#3 1 Congress fail to increase the Treasury ’s borrowing capacity#4 1 president Bush fail to increase the Treasury ’s borrowing capacityFigure 2: Proposed representation format (RDF-NL) - human readable representation.4 UsageGraphene can be either used as a Java API, imported as a Maven dependency, or as a service which weprovide through a command line interface or a REST-like web service that can be deployed via docker.A demonstration video is available online4.5 BenchmarkingFigure 3: Performance of Graphene.We evaluated the performance of our Open IE sys-tem Graphene using the benchmark framework pro-posed in Stanovsky and Dagan (2016), which isbased on a QA-Semantic Role Labeling corpus withmore than 10,000 extractions over 3,200 sentencesfrom Wikipedia and the Wall Street Journal5. Thisbenchmark allowed us to compare our frameworkwith a set of state-of-the-art Open IE approaches inrecall and precision (see Figure 3). With a scoreof 50.1% in average precision, Graphene achievesthe best performance of all the systems in extractingaccurate tuples. Considering recall, our framework(27.2%) is able to compete with the best-performingbaseline approaches (32.5% and 33.0%). The inter-ested reader can refer to Cetto et al. (2018) for moredetails.6 Application Scenarios of the Lightweight Semantic Open IE representationThe resulting lightweight semantic representation of the source text in the form of a two-layered hier-archy of semantically-linked relational tuples can be used to facilitate a variety of artificial intelligencetasks, such as building QA systems, creating text summarization applications or supporting semanticinferences.For example, QA systems could build upon the semantically typed and interconnected relational tuplesproduced by our Open IE system Graphene to investigate the dependencies between extracted proposi-tions (such as causalities, attributions and local or temporal contexts) and map specific question typesto the corresponding semantic relationships when querying the underlying data. Based on the examplegiven in Figure 2, one can imagine the following user query:Under which circumstances will the funding be delayed?Here, the system could infer from the interrogative expression ”Under which circumstances?” tosearch for propositions that are linked to the extraction stating that 〈the funding; will be delayed; ∅〉4https://asciinema.org/a/bvhgIP8ZEgDwtmRPFctHyxALu?speed=35available under https://github.com/gabrielStanovsky/oie-benchmarkby a conditional (CONDITION) relation. Accordingly, in this scenario the system is expected to returnpropositions #3 and #4 of Figure 2.7 ConclusionWe presented Graphene, an Open IE system that transforms sentences which present a complex linguisticstructure into a novel hierarchical representation in the form of core facts and accompanying contextswhich are connected by rhetorical relations capturing their semantic relationship. In that way, the inputtext is turned into clean, compact structures that show a canonical form, thus facilitating the extractionof accurate, meaningful and complete propositions based on a novel lightweight semantic representationconsisting of a set of semantically typed and interconnected relational tuples. In the future, we aim toport this idea to languages other than English.References[Angeli et al.2015] Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D. Manning. 2015. Leverag-ing linguistic structure for open domain information extraction. In Proceedings of the 53rd Annual Meeting ofthe Association for Computational Linguistics and the 7th International Joint Conference on Natural LanguageProcessing, pages 344–354. ACL.[Banko et al.2007] Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni.2007. Open information extraction from the web. In Proceedings of the 20th International Joint Conference onArtifical Intelligence, pages 2670–2676. Morgan Kaufmann Publishers Inc.[Cetto et al.2018] Matthias Cetto, Christina Niklaus, André Freitas, and Siegfried Handschuh. 2018. Graphene:Semantically-linked propositions in open information extraction. In Prooceedings of COLING 2018. To appear.[Del Corro and Gemulla2013] Luciano Del Corro and Rainer Gemulla. 2013. Clausie: Clause-based open infor-mation extraction. In Proceedings of the 22Nd International Conference on World Wide Web, pages 355–366.ACM.[Mann and Thompson1988] William C Mann and Sandra A Thompson. 1988. Rhetorical structure theory: Towarda functional theory of text organization. Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243–281.[Mausam et al.2012] Mausam, Michael Schmitz, Stephen Soderland, Robert Bart, and Oren Etzioni. 2012. Openlanguage learning for information extraction. In Proceedings of the 2012 Joint Conference on Empirical Meth-ods in Natural Language Processing and Computational Natural Language Learning, pages 523–534. ACL.[Niklaus et al.2016] Christina Niklaus, Bernhard Bermeitinger, Siegfried Handschuh, and André Freitas. 2016. Asentence simplification system for improving relation extraction. In Proceedings of COLING 2016: SystemDemonstrations.[Stanovsky and Dagan2016] Gabriel Stanovsky and Ido Dagan. 2016. Creating a large benchmark for open in-formation extraction. In Proceedings of the 2016 Conference on Empirical Methods in Natural LanguageProcessing (EMNLP). ACL.

Graphene: A Context-Preserving Open Information Extraction System

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Graphene: A Context-Preserving Open Information Extraction System

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