Why Ontological Semantics Is neither Ontological nor Semantics




This criticism focuses on Sergei Nirenburg and Victor Raskin’s Ontological Semantics. An approach in automated speech processing, summarily presented in their corresponding book Ontological Semantics (2004).


In the philosophy of language and linguistics the idea of ‘bringing ontology back to semantics’ is linked to Situation Semantics, the approach introduced by Jon Barwise, John Perry, Keith Devlin and their co-authors in the 1980s. ‘ontology’ in this approach meant on the one hand a theory of the kinds of entities that reality is composed of, and on the other hand modelling semantics and information flow using these entities, the models also containing a meta-theory how they relate and refer to parts of reality. ‘ontology’ here is used in the traditional (philosophical) sense.


In the artificial intelligence community, however, ‘ontology’ is unfortunately often used in quite another sense. (This leads – as one may witness occasionally at conferences – to talking past each other.) ‘Ontology’ here, and so in Nirenburg and Raskin’s Ontological Semantics, is introduced as a canonical description of the world. This alludes to the first sense of ‘ontology’ mentioned above, but involves no claims about linking models framed in the model language to reality or even withholds commitment as to the real existence of the kinds of entities used in the model language. The ‘world’ that is modelled is a more or the less comprehensive representation (true or not), and is not reality. Ontology in the AI sense, therefore, can freely make use of fictional entities and kinds of entities supposedly not belonging to the ultimate furniture of the universe. Ontology in Ontological Semantics, in fact, turns out to be a conceptual structure, which is outlined in a meta-language. Ontological modelling means to use a conceptual scheme of an explicitly defined format: ‘it belongs in epistemology’ (p.135).


Ontological Semantics is employed to derive a canonical interpretation given some input text. As it should be used by automata this means that such an artificial system comprises sub-systems responsible for (syntactic) parsing and analysis of text structure and other sub-systems deriving an interpretation by making use of a lexicon and a stored knowledge repository. The input text is ultimately translated into a canonical output text, which expresses the proper interpretation of the input text.

In ontological semantics, …, sentential meaning is defined as an expression, text-meaning representation (TMR), obtained through the application of the set of rules for syntactic analysis of the source text, for linking syntactic dependencies into ontological dependencies, and for establishing the meaning of source-text lexical units.



The aim of Ontological Semantics is to enable this treatment of text not just for toy text-book examples, but for ordinary – say newspaper – texts. Nirenburg and Raskin see the scope of envisioned application as one of the outstanding features of Ontological Semantics.


Nirenburg and Raskin introduce the reader both to the methodology and the development of Ontological Semantics, a research programme of about twenty years.

The first part of the book provides an overview and comment on current linguistics and its methodology and places Ontological Semantics within the field. Nirenburg and Raskin see themselves as contributing to the methodology of linguistics and the proper methodology of semantics. They see the explicitness of its methodology as the other outstanding feature of Ontological Semantics.

The second part of the book provides basic insights into the workings of Ontological Semantics. Nirenburg and Rasking outline their conception of ontology and their theories of the lexicon and the use of the fact repository. The structure of the ontology resembles an inheritance hierarchy as one may find in object oriented programming. Examples of processing are presented, more are advertised as web-resources of the research group. Although somewhat repetitive this overview allows some fascinating insights into Nirenburg and Raskin’s approach to canonical text interpretation by artificial systems.


Whether Ontological Semantics is ‘semantics’ is quite another matter. Nirenburg and Raskin claim to deliver a lexicalist account of meaning, so that the system uses a lexicon, which provides for each lexical item an entry with that entries definition and syntactic as well as semantic features. 

A theory of meaning, however, demands far more than a system deriving some interpretation of a text. Ontological Semantics ultimately is a version of inferential role semantics: a lexical item is claimed to be defined by its interrelations to other lexical items. If that was so, one should not use as labels of lexical entries words of ordinary language (like ‘pay’), which may trick the casual reader into importing their linguistic knowledge, but new labels (like ‘pon’). As Nirenburg and Raskin note, the technical system does not make a distinction between labels we know and new labels, but once we – as readers – make use of new meaningless labels we recognize that the examples given by Nirenburg and Raskin are far from a comprehensive model of inferential roles that are distinct enough for lexical items of natural languages.

Their lexical entries, further on, have a specific slot for ‘definition’, which again is said for the human user of the system only. Now, on the one hand these definitions use other words, the entries of which use other words – a circle to be explained; on the other hand these definitions show again the insufficieny of definitions in semantics – as sometimes complained in the linguistics literature. For example, ‘say’ is taken as ‘inform’ (p.169), ‘pay’ is defined as ‘to compensate somebody for goods or services rendered’ (p.199). This does neither account for uses like ‘pay with one’s life’ nor does it distinguish ‘pay’ from ‘remunerate’, ‘reimburse’, ‘pay off’… A theory of interpretation, which derives a canonical reading of a text in a context, does not need to do these two things, a theory of meaning should. The definitional slots in the lexical entries play no role in the processing, thus one may downplay this problem. An even more serious problem, however, is the status of the meta-representations. The elements of the ontology (vis. the concepts like AGENT or ANIMATE) are connected by relations and structured in some format. These relations and structures (‘metaontological predicates’, p.224) are part of a meta-language in which the ‘ontology’ is expressed. If the ‘ontology’ is the conceptual system, where do we have to place this meta-language? For a technical system employing such a further layer of analysis or description may be allowed, for a theory which aspires to account for natural language processing in humans as well, which claims to be part of our account of cognition, this will not do.

There cannot be, on pains of vicious regress, a conceptual layer beneath/beyond the conceptual system.


So, Ontological Semantics should not be seen as either ontology or semantics in their respective strict (traditional) sense, but as an approach of Canonical Interpretation making use of a handcrafted conceptual scheme. That does not make it nor its aspirations, nor Nirenburg and Raskin’s book less interesting.


Manuel Bremer, 2008.