FrameNet Meets the Semantic Web: Lexical Semantics for the Web

Abstract

Abstract. This paper describes FrameNet [9,1,3], an online lexical resource for English based on the principles of frame semantics [5,7,2]. We provide a data category specification for frame semantics and FrameNet annotations in an RDF-based language. More specifically, we provide an RDF markup for lexical units, defined as a relation between a lemma and a semantic frame, and frame-to-frame relations, namely Inheritance and Subframes. The paper includes simple examples of FrameNet annotated sentences in an XML/RDF format that references the project-specific data category specification. Frame Semantics and the FrameNet ProjectFrameNet's goal is to provide, for a significant portion of the vocabulary of contemporary English, a body of semantically and syntactically annotated sentences from which reliable information can be reported on the valences or combinatorial possibilities of each item included. A semantic frame is a script-like structure of inferences, which are linked to the meanings of linguistic units (lexical items). Each frame identifies a set of frame elements (FEs), which are frame-specific semantic roles (participants, props, phases of a state of affairs). Our description of each lexical item identifies the frames which underlie a given meaning and the ways in which the FEs are realized in structures headed by the word. The FrameNet database documents the range of semantic and syntactic combinatory possibilities (valences) of each word in each of its senses, through manual annotation of example sentences and automatic summarization of the resulting annotations. FrameNet I focused on governors, meaning that for the most part, annotation was done in respect to verbs; in FrameNet II, we have been annotating in respect to governed words as well. The FrameNet database is available in XML, and can be displayed and queried via the web and other interfaces. FrameNet data has also been translated into the DAML+OIL extension to XML and the Resource Description Framework (RDF). This paper will explain the theory behind FrameNet, briefly discuss the annotation process, and then describe how the FrameNet data can be represented in RDF, using DAML+OIL, so that researchers on the semantic web can use the data.

Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction

“…2009) and even English is lacking domain-specific frame-based resources. Possible inroads are cross-lingual semantic annotation transfer (Padó and Lapata 2009;Hartmann, Eckle-Kohler, and Gurevych 2016) or linking FrameNet to other lexical-semantic or ontological resources (Narayanan et al 2003;Tonelli and Pighin 2009;Laparra and Rigau 2010;Gurevych et al 2012, inter alia). But while the arguably simpler task of PropBankbased Semantic Role Labeling has been successfully addressed by unsupervised approaches (Lang and Lapata 2010;Titov and Klementiev 2011), fully unsupervised framebased semantic annotation exhibits far more challenges, starting with the preliminary step of automatically inducing a set of semantic frame definitions that would drive a subsequent text annotation.…”

lemonUby – A large, interlinked, syntactically-rich lexical resource for ontologies

“…Yet, representing lexical resources in RDF does not per se make them semantically interoperable. Consider, for instance, existing conversions of WordNet and FrameNet [26,22], where a simple mapping to RDF is provided, and augmented with OWL semantics so that reasoning could be applied to the structure of the resource. However, the formats chosen for the RDF versions of WordNet and FrameNet are specific to the underlying data models of WordNet and FrameNet.…”

From information to knowledge

“…For example, sentences that refer to purchases could be mapped into purchase-specific frames with roles like seller, buyer, goods, amount, etc., regardless of whether the sentence is active or passive and whether the verb is "buy", "acquire", etc. Obviously this is a very difficult task, especially because there are still only small-scale training corpora available (see, e.g., PropBank [82] and FrameNet [96]). …”