Temporal prepositions trigger various temporal relations over events and times. In this chapter, I categorize such temporal relators into five types: (i) anchoring (at, in), (ii) ordering (before, after),(iii) metric (for, in), (iv) bounding (from -- till), and (v) orienting (time interval + before, after). These temporal relators are analyzed with respect to the tripartite temporal configurations <E,R,T>, where E is a set of eventualities, R is a set of temporal relators, and T is a set of associated temporal structures, which subsume metric structures. Temporal relators combine with temporal expressions to form temporal adjuncts, either simple or complex. Complex temporal adjuncts introduce time intervals as nonconsuming tag in annotation, while relating eventualities to temporal structures. Each temporal relator r in R combines with a temporal structure t in T as its argument to form a temporal adjunct, while relating an eventuality e in E of various aspectual types such as state, process, or transition to an appropriate temporal structure t in T. This chapter clarifies such temporal relations by annotating and interpreting event and temporal base structures and their relations.

In this chapter, I explain how TimeML, a specification language for the annotation of event-associated temporal expressions in language, was normalized as an ISO international standard, known as ISO-TimeML, with some modifications. ISO’s working group developed TimeML into an ISO standard on event-associated temporal annotation by making four modifications of TimeML: (i) abstract specification of the annotation scheme, (ii) adoption of standoff annotation, (iii) merging of two tags, <EVENT/> and <MAKEINSTNACE/>, to a single tag <EVENT/>, and (iv) treating duration (e.g., two hours) as measurement. Following Bunt’s (2010) proposal for the construction of semantic annotation schemes and his subsequent work, I then formalize ISO-TimeML by presenting a metamodel for its design, an abstract syntax for formulating its specification language in set-theoretic terms, and an XML-based concrete syntax. I also analyze base structures as consisting of two substructures, anchoring and content structures, into the annotation structures of the normalized TimeML.

This chapter formulates an annotation-based semantics (ABS) for the annotation and interpretation of temporal and spatial information in language. It consists of two modules, one for representation and another for interpretation. The representation module consists of a type-theoretic first-order logic with a small set of merge operators. The theory of types is based on the extended list of basic types, which treats eventualities and points of time and space as basic types besides the two basic types e for entities and t for truth-values. These types extend the Neo-Davidsonian semantics to all types of objects including paths and vectors (trajectories) triggered by motions. The merge operators in ABS allow the compositional process of combining the semantic representation of base structures into that of the link structures that combine them without depending on complex lambda operations. ABS adopts shallow semantics to represent complex structures of eventuality or quantificaiton with simple logical predicates defined as part of admissible interpretation models.

The task of semantics for annotation is two-fold. First, semantics validates the construction of a syntax for the generation of well-formed annotation structures. Second, semantics provides a formalism for interpreting those annotation structures that are generated by the syntax. In this chapter, my main concern is to present a general view of what kind of semantics is needed to interpret annotation structures and to lay a general ground for constructing an interpretation scheme for temporal and spatial annotation. This semantics follows the ordinary steps of doing model-theoretic formal semantics such as Montague semantics. It goes through an intermediate step of representing semantic content or denotations in logical forms and then interprets them with respect to a model with truth definitions.

Data can be segmented into minimal units. Such a process is called base segmentation. In this chapter, I discuss three types of base segmentation of language data, depending on its three media types: phoneme segmentation, image segmentation, and text segmentation. They can be grouped into larger units. Base segmented text, for instance, undergo tokenization, annotated segmentation such as word segmentation, and chunking with POS-tagging. The semantic annotation of language data, whether written, spoken, or visualized, requires the target data to be segmented and preferably annotated with appropriate morpho-syntactic information.

This chapter shows how ISO-Space evolved from MITRE’s SpatialML. Both present a specification language for the annotation of spatial information on geographical names and landmarks and also directional information involving orientations. Unlike SpatialML, ISO-Space extended its scope to motions and motion-triggered dynamic paths. ISO-Space also generalized distance measures to measures of other types and dimensions so that spatial annotation can be integrated with other semantic annotation schemes such as temporal annotation (e.g., ISO-TimeML). I also discuss how spatial relators, called signals, are enriched with fine-grained specifications, especially related to directional or orientational configurations involving frames of reference. SpatialML is a compact and very simple annotation scheme and is made easily mappable to other geospatial annotation schemes. In contrast, ISO-Space is more expressive and complex than SpatialML, meeting semantic needs of interpreting complex spatial language and computational needs of application envisioned in the coming years.

In this chapter, I discuss how the abstract syntax for a semantic annotation scheme is modeled on a formal grammar of language. I design a semantic annotation scheme as consisting of three components: a (nonempty) set of annotation structures, a syntax, and a semantics. The metatheoretic syntax formally defines, or generates, annotation structures each of which consists of base structures and link structures. The semantics interprets annotation structures, while validating the formulation of the syntax. I also discuss how the specification of attribute-value assignments determine the well-formedness of annotation structures and its substructures, anchoring and content (feature) structures. This chapter focuses on the formulation of an abstract syntax.

Viewing ontology as a science of things, this chapter treats times as real objects in the world. Such a view of ontology of times, called temporal ontology, conforms to Neo-Davidsonian semantics and to the type-theoretic semantics, which treats time points as one of the basic types that include individual objects, events, and spatial points. It is thus designed to provide a sound basis for the development of a semantics for the annotation and interpretation of event-based temporal information in language. In this chapter, I first introduce OWL-Time ontology which classifies temporal entities into instances and intervals. I then introduce an interval temporal calculus with 13 base relations over time points and intervals. I also discuss how eventualities are temporalized to be treated as denoting time intervals. Eventualities are then temporally related to times. To apply the notions of time points and intervals to the interpretation of tenses and aspects of language, especially the progessive aspect and the present perfective aspect, I define the notion of neighborhood and apply it to the definition of the present perfect as denoting the neighborhood of the present moment.

This chapter is about semantic annotation, discussed from formal and computational perspectives. Annotation is viewed as a scholarly technique or methodology. This chapter explains what annotation was in general and is now, how annotation has gradually developed and become applicable to the automatic building of larger data in language, and what applications semantic annotation aims at and what principles govern the modeling of semantic annotation schemes. There are two governing principles discussed: the partiality and situatedness of information to be annotated. I also mention the use of machine learning techniques for the automatic annotation of language data, which is represented either in a textual form or in a graphic form.

This chapter introduces a concrete syntax. It is ideally isomorphic to an abstract syntax that specifies a semantic annotation scheme, while providing a format for representing annotation structures. This format can represent them either in a serialized way from left to right, or in graphic images or tabular forms with linking arrows. Representation formats may vary, depending on kinds of the use of annotation. Human readers, for instance, prefer tabular formats especially for illustrations or demonstrations. For the purposes of merging, comparing, or exchanging various types of annotations or different annotations of the same type, graphs are considered useful. In this chapter, I introduce a graphic annotation format, called GrAF, for linguistic annotation. For the construction of larger corpora, however, there are practical computing reasons to prefer a serialization of annotations. For the serialized representation of annotation structures, this chapter mainly discusses two formats: (i) XML and (ii) pFormat, a predicate-logic-like representation format, which represents annotation structures in a strictly serial (linear) manner by avoiding embedded structures.

In Chapter 9, I introduce eXTimeML, an extended variant of ISO-TimeML, with three extensions. (i) Temporal measure expressions are annotated as part of generalized measure: e.g., 30 hours. (ii) Quantified temporal expressions are annotated as part of generalized quantification: e.g., every day. (iii) Adjectives and adverbs are annotated as modifiers of nouns and verbs, respectively: e.g., daily, never. Temporal measures and temporal quantifiers are treated as part of generalized measures and quantifiers. I then illustrate how the representation language of ABS applies to each of these extensions in eXTimeML by deriving appropriate (logical) semantic forms from the well-formed annotation structures of temporal measures, quantifiers, and modifiers. Semantic forms are then interpreted with respect to admissible models, constrained by the formal definitions of logical predicates such as twice or three thousand.