# Non-Axiomatic Logic Modeling of English Texts for Knowledge Discovery and Commonsense Reasoning
## 7 Discussion  
Modeling a natural language with a formal language is always going to be an incomplete task. Part of the problem stems from the fact that logic formulas (in any symbolic logic) are purely declarative, while natural languages can express a variety of sentences besides declarative. It has been extensively argued that the lack of contextual or background knowledge in an artificial agent prevents any chance of communication or reasoning with unified semantics [45]. However, it seems quite evident that if artificial intelligence is going to have any chance of near-human behavior, it cannot depend on non-symbolic generative models for catching the true meaning of natural language expressions.  
In this paper, we have shown a methodological return to the symbolic way, using linguistic analysis tools that identify the grammatical structure in a natural language sentence and reveal the various types of relations among words (dependencies, semantic role relations, etc.). Such an analysis provides sufficient evidence to identify concepts, instances of those concepts, properties and relations expressed by natural language sentences, which can then be translated into NAL formulas that not only preserve the original relations but also insert them in a multi-valued, non-monotonic, and higher-order logic with enough flexibility to later perform inference tasks [46; 47].  
Our approach has been to search for named entities and grammatical labels in order to define the _terms_ (logical terms or concepts) on which the logical formulas will be based. Next, we used a commonsense ontology (WordNet) to construct a minimal background context for the selected terms. Finally, we took the table of universal dependencies as the landmark reference, maximizing a consistent covering of commonly used expressions and idioms, as well as their translation into NAL formulas. We would like to highlight the following linguistic/logic merits of our proposal:  
* A nominal subject sentence (Examples, Nominal subject, cases (a) and (b)) gets translated to the exact same NAL formula, regardless of it being in an active or passive voice.
* A double object construction (Example, Indirect object, case (a)) and a prepositional construction (Example, Oblique nominal, case (c)) also got translated to the same NAL pre-defined relation formula.
* A careful use of NAL product terms allows the pre-defined relations to endow their related terms with a more semantic role than their syntactic analysis would suggest. For example, the two sentences _[PERSON] teaches Logic_ and _[PERSON] teaches the students_ have exactly the same dependency parsing but are translated to slightly different NAL formulas. The first one is translated into \((\mathbf{\times},\{\text{[PERSON]}\},logic,\#\text{To-whomever})\xrightarrow{}\mathbf{\to}\text{ teach}\), while the second one is translated into \((\mathbf{\times},\{\text{[PERSON]}\},\#\text{Whatever},\{\text{the-students}\}) \xrightarrow{}\text{ teach}\).
* Adjectives are not always translated into NAL properties. When appropriate, they can also be translated into relations, as can be seen in Examples, Table 15, Adjectival modifier, case (b), which is consistent with [48; 49].  
These characteristics, along with the broad coverage of universal dependencies, make our method more extensive and precise than the one implemented in [22] but still complying with the specifications in [21].