The Predicate Matrix and the Event and Implied Situation Ontology: Making More of Events (2016)

Abstract: This paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology reuses and maps across existing resources such as WordNet, SUMO, VerbNet, PropBank and FrameNet. We describe how ESO is injected into a new version of the Predicate Matrix and illustrate how these resources are used to detect information in large document collections that otherwise would have remained implicit. The model targets interpretations of situations rather than the semantics of verbs per se. The event is interpreted as a situation using RDF taking all event components into account. Hence, the ontology and the linked resources need to be considered from the perspective of this interpretation model.

## introduction in this paper, we present the new release of the event and implied situation ontology (eso) that is matched with a new version of the predicate matrix (pm). both resources rely on semantic role labeling (srl) descriptions and are used to detect and abstract over events, their participants and event implications in a large document collection about ten years of global automotive industries. eso @xcite is a newly developed domain ontology to enhance the extraction and linking of dynamic and static events and their implications in text. explicit modeling of event implications allows for extracting sequences of states and changes over time regardless of if this information was directly expressed in text, or inferred by a reasoner. figure 1 shows such a chain of expressions for dynamic (hire, starts at, fire, leave) and static events (works for, employs, is ceo) and their implied situations. lexicons that define implications of events, e.g. verbnet @xcite , are rare and usually focus on the meaning of verbs in isolation. however, lexical structures do not make explicit how the meaning of a verb needs to be combined with other event components, such as the participants and the temporal properties for the purpose of semantic parsing. we therefore follow an ontological approach to interpret situations on the basis of text interpretation of all the event components to make the implications explicit. though some research on deductive reasoning over frame annotated text (e.g. @xcite ) and defining pre and post situations of predicates exist @xcite , to the best of our knowledge, ontologies that model both events, roles and implications do not. most closest comes the extension to dolce-lite @xcite that models property values as quality regions for reasoning. however, these quality regions are not connected to the events in the ontology as pre and post situations. axioms in generic and top ontologies such as sumo @xcite and dolce @xcite provide a comprehensive semantic specification of the concepts, but these axioms do not always provide the information relevant and specific for our domain. furthermore, such ontologies need to be integrated with semantic parsing systems that deal with expressions on natural language to be able to test these models. we therefore decided to develop a new ontology for modeling static and dynamic events and their implications that is tailored to a semantic parsing system for text. figure 1 : a chain of dynamic and static event expressions and their implied situation. version 2 of eso was released in july 2015 and now includes modeling of scalar values and an extended expressivity of the assertions that define the situation that holds before, during and after the event. it also includes updated mappings to sumo classes and to framenet frames and frame entities. the predicate matrix 1 (de @xcite is the second resource presented in this paper. it integrates predicate and role information from e.g. framenet, verbnet, propbank, nombank and wordnet. this resource is used to assign role and predicate annotations at sentence level. all classes and roles in eso are fed back into to the predicate matrix. as such the ontology provides an additional layer of annotations in text that allow for inferencing over events and implications. the current version of the pred-icatematrix contains 8,495 predicates from prop-bank and nombank connected to 4,704 synsets of wordnet, 554 frames of framenet and 55 eso classes. on the other hand, this resource contains 23,386 roles of propbank and nombank mapped to 2,343 frame-elements of framenet and 53 eso roles. the remainder of this paper is organized as follows. section 2 presents the ontological meta model and the content of eso. section 3 describes the predicate matrix and the integration with eso. in section 4 we provide a preliminary overview of the predicate matrix and eso in our document collection. in section 5 we report on an experiment carried out on a small corpus. we conclude in section 6 with a discussion and some outlines for future work. ## the event and implied situation ontology in this paragraph, we briefly describe the meta model of the ontology with a focus on the instantiation of the rules that define what situation holds before, during and after some event. next, we describe how the ontology was built and we provide an overview of the current content. the eso ontology and a detailed documentation can be found online: https://github.com/ newsreader/eso eso is an owl 2 ontology. 2 it assumes that the semantic representation of text is converted to an rdf representation of event and entity instances, between which relations are expressed as triples. for instance, the statement :obj-graph-eventx { :eventx a eso:translocation; eso:translocation-theme :enzo ferrari; eso:translocation-goal :rome; eso:translocation-source :napels; sem:hastime :time_eventx. } specifies that the event (x) is of a certain type (eso:translocation), that it involves an entity playing the role of a moving thing (:enzo ferrari), an entity playing the role of goal (:rome), an entity playing the role of source (:napels) and that it occurred at a certain time (:time eventx). from these representations, we derive the statements that express the pre, post and during event situations. for this purpose, we defined five core classes in eso: 1) event: this class is the root of the taxonomy of event types. any event detected in a text will be an instance of some class of this taxonomy; 2) dynamicevent: this is a subclass of event for which dynamic changes are defined; 3) staticevent: this is another subclass of event for "static" event types which capture more stable circumstances; 4) situation: the individuals of this class are actual pre, post and during situations that will be instantiated starting from the event instances detected in the text; 5) situationrule: the individuals of this class enable to encode the rules for instantiating pre/post/during situations when a certain type of event is detected. further, eso includes mapping properties to match eso roles to framenet roles, and properties to match eso classes to framenet frames and sumo classes. the mappings to framenet are necessary to translate the annotations provided by the srl module using the predicate matrix to our ontology. this is then exploited by the reasoning module to instantiate situations from events. ## predicate matrix the predicatematrix (pm) @xcite ) is an automatic extension of semlink @xcite that merges several models of predicates such as verb-net @xcite , framenet @xcite , propbank @xcite and word-net @xcite . the pm also contains for each predicate features of the ontologies integrated in the multilingual central repository @xcite like sumo @xcite , top ontology @xcite or word-net domains @xcite . the mappings between such knowledge bases allow to take advantage from their individual strengths. for example, the coverage of propbank or the semantic relations among events and participants of framenet. the semantic interoperability offered by the pm allows to translate the output of a srl analysis to ## experiment on the wikinews corpus the wikinews corpus consists of 120 manually annotated news articles selected from wikinews foot_4 and is used within newsreader as an evaluation corpus. 7 the evaluation of the mate tool that is in short, we followed the same procedure that is also used for the automotive corpus. first, all 120 wikinews articles were processed by the news-reader pipeline @xcite using the predicate matrix and eso; next, a module called naf2sem merged identical events across documents and translated all events into sem-rdf and finally, all events were loaded into the knowledge-store @xcite and further enriched by the eso reasoner that infers all eso assertions, based on the class and role labels. in the knowledgestore, the data can be queried via sparql queries or simple look-ups. in next, we derived some basic statistics from the knowledgestore that contains all events derived from the corpus. in number of events inspected 52 (10.5%) number events insp. with a pre/post situation 43 number events insp. with a during situation 9 correct class label 37 (71.1%) correct pre and post situation(s) 18 (41.8%) correct during situation(s) 6 (66.6%) correct eso events 21 (50%) for nine events we found that the sense of the predicate was misinterpreted, for eight events multiple and conflicting eso classes were assigned due to some unavoidable level of ambiguity in the predicate matrix. in five cases, we found that the semantic role labeler picked up the wrong role; for ten events dbpedia spotlight assigned a wrong label for a named entity. these errors also resulted in 6 role duplications where subject and object of an assertion are identical while they should not. for one event, it caused conflicting assertions. ## discussion and future work in this paper, we have presented the new release of the event and implied situation ontology (eso) and the predicatematrix (pm). both resources augment semantic role labeling techniques and are applied to a very large document collection to capture implications of events for a selected set of concepts, roles and properties. through the word-net backbone of eso and pm, we were able to derive a formal model for event implications with a large coverage in english. since wordnets in many languages are connected to wordnet, this model has also been projected to other languages in the newsreader project: spanish, dutch and bulgarian. eso thus has shown to be used as a interoperable framework on reasoning over changes and their implications across different languages. this allows us to compare the content of text across languages, regardless of the way this content is expressed. from the experiment on the wikinews corpus, we conclude that eso performs reasonably well on this dataset with 50% of correct eso events with a pre/post or during situation. the ontology is not built in order to define all events in text which is shown in the coverage of all events found (5,443), and the eso events (2,508) of which 496 have either both a pre and post situation or a during situation. the errors in the eso events with assertions are mainly caused by an unavoidable degree of errors in the processing pipeline as was reported in the error analysis. the observation that not all eso events come with assertions is likely due to the fact that a sentence does not always contain all roles necessary for an assertion rule to fire. a more in-depth analysis of the annotated texts will provide an answer for this. we are currently processing about 2.1 million news articles on the automotive industry, where the eso mapping are inserted in the srl layers. the output is converted to rdf, after which we apply reasoning to derive new statements as was shown in the experiment. the output will be evaluated through inspecting samples, against benchmark data that will be developed on the wikinews corpus and through end-user tasks on the data sets. also, we planned additional experiments on the usability of the eso assertions for tracking actual chains of property changes through time. finally, the wikinews corpus has been translated to spanish, dutch and italian. the processing of the translated text through newsreader pipelines in these languages, where these pipelines exploit the same eso model and a language-specific pm, will allow us to do a cross-language comparison of the inferred properties.