Question: How to express the statements in RDF?
Evidence: RDF BIBREF18 uses a graph-based data model for representing knowledge. Statements in RDF are expressed as so-called triples of the form (subject, predicate, object). RDF subjects and predicates are IRI and objects are either IRI or literals. RDF literals always have a datatype that defines its possible values. A predicate denotes a property and can also be seen as a binary relation taking subject and object as arguments. For example, the following triple expresses that Albert Einstein was born in Ulm:
The lexicalization of RDF triples must be able to deal with resources, classes, properties and literals.
Classes and resources The lexicalization of classes and resources is carried out as follows: Given a URI $u$ we ask for the English label of $u$ using a SPARQL query. If such a label does not exist, we use either the fragment of $u$ (the string after #) if it exists, else the string after the last occurrence of /. Finally this NL representation is realized as a noun phrase, and in the case of classes is also pluralized. As an example, :Person is realized as people (its label).
Properties The lexicalization of properties relies on the insight that most property labels are either nouns or verbs. While the mapping of a particular property p can be unambiguous, some property labels are not as easy to categorize. For examples, the label crosses can either be the plural form of the noun cross or the third person singular present form of the verb to cross. To automatically determine which realization to use, we relied on the insight that the first and last word of a property label are often the key to determining the type of the property: properties whose label begins with a verb (resp. noun or gerund) are most to be realized as verbs (resp. nouns). We devised a set of rules to capture this behavior, which we omit due to space restrictions. In some cases (such as crosses) none of the rules applied. In these cases, we compare the probability of $P(p|\texttt {noun})$ and $P(p|\texttt {verb})$ by measuring
where $\text{\em synset}(p)$ is the set of all synsets of $p$, $\text{\em synset}(p|X)$ is the set of all synsets of $p$ that are of the syntactic class $X \in \lbrace \texttt {noun},\texttt {verb}\rbrace $ and $f(t)$ is the frequency of use of $p$ in the sense of the synset $t$ according to WordNet. For
we choose to realize p as a noun; else we realized it as a verb. For $\theta =1$, for example, dbo:crosses is realized as a verb.
Literals Literals in an RDF graph usually consist of a lexical form LF and a datatype IRI DT, represented as "LF"⌃⌃<DT>. Optionally, if the datatype is rdf:langString, a non-empty language tag is specified and the literal is denoted as language-tagged string. The realization of language-tagged strings is done by using simply the lexical form, while omitting the language tag. For example, "Albert Einstein"@en is realized as Albert Einstein. For other types of literals, we further differentiate between built-in and user-defined datatypes. For the former, we also use the lexical form, e.g. "123"⌃⌃xsd:int $\Rightarrow $ 123, while the latter are processed by using the literal value with its representation of the datatype IRI, e.g., "123"⌃⌃dt:squareKilometre as 123 square kilometres.
The realization $\rho $ of a triple (s p o) depends mostly on the verbalization of the predicate p. If p can be realized as a noun phrase, then a possessive clause can be used to express the semantics of (s p o), more formally
$\rho $(s p o) $\Rightarrow $ poss($\rho $(p),$\rho $(s))$\,\wedge \,$subj(BE,$\rho $(p))$\,\\ \wedge \,$dobj(BE,$\rho $(o))
For example, if $\rho (\texttt {p})$ is a relational noun like birth place e.g. in the triple (:Albert_Einstein :birthPlace :Ulm), then the verbalization is Albert Einstein's birth place is Ulm. Note that BE stands for the verb “to be”. In case p's realization is a verb, then the triple can be verbalized as follows:
$\rho $(s p o) $\Rightarrow $ subj($\rho $(p),$\rho $(s))$\,\wedge \,$dobj($\rho $(p),$\rho $(o))
For example, in (:Albert_Einstein :influenced :Nathan_Rosen) $\rho (\texttt {p})$ is the verb influenced, thus, the verbalization is Albert Einstein influenced Nathan Rosen.
The same procedure of generating a single triple can be applied for the generation of each triple in a set of triples. However, the NL output would contain redundant information and consequently sound very artificial. Thus, the goal is to transform the generated description to sound more natural. To this end, we focus on two types of transformation rules (cf. BIBREF21): ordering and clustering and grouping. In the following, we describe the transformation rules we employ in more detail. Note that clustering and ordering (SECREF18) is applied before grouping (SECREF26).
We process the input trees in descending order with respect to the frequency of the variables they contain, starting with the projection variables and only after that turning to other variables. As an example, consider the following triples about two of the most known people in the world:
The five triples are verbalized as given in UNKREF20–UNKREF24. Clustering and ordering first take all sentences containing the subject :Albert_Einstein, i.e. UNKREF21 –UNKREF23, which are ordered such that copulative sentences (such as Albert Einstein is a scientist) come before other sentences, and then takes all sentences containing the remaining subject :William_Shakespeare in UNKREF20 and UNKREF24 resulting in a sequence of sentences as in UNKREF25.
William Shakespeare is a writer.
Albert Einstein's birth place is Ulm.
Albert Einstein's death place is Princeton.
Albert Einstein is a scientist.
William Shakespeare's death date is 23 April 1616.
Albert Einstein is a scientist. Albert Einstein's birth place is Ulm. Albert Einstein's death place is Princeton. William Shakespeare's is a writer. William Shakespeare's death date is 23 April 1616.
dalianishovy describe grouping as a process “collecting clauses with common elements and then collapsing the common elements”. The common elements are usually subject noun phrases and verb phrases (verbs together with object noun phrases), leading to subject grouping and object grouping. To maximize the grouping effects, we collapse common prefixes and suffixes of sentences, irrespective of whether they are full subject noun phrases or complete verb phrases. In the following we use $X_1,X_2,$...$X_N$ as variables for the root nodes of the input sentences and $Y$ as variable for the root node of the output sentence. Furthermore, we abbreviate a subject $\texttt {subj}(X_i,s_i)$ as $\texttt {s}_i$, an object $\texttt {dobj}(X_i,o_i)$ as $\texttt {o}_i$, and a verb $\texttt {root}(\text{\em ROOT}_i,v_i)$ as $\texttt {v}_i$.
Subject grouping collapses the predicates (i.e. verb and object) of two sentences if their subjects are the same, as specified in UNKREF27 (abbreviations as above).
$\rho (\texttt {s}_1)=\rho (\texttt {s}_2)\wedge \texttt {cc}(\texttt {v}_1,\text{\em coord})$
$\Rightarrow \texttt {root}(Y,\texttt {coord}(\texttt {v}_1,\texttt {v}_2))\wedge \, \texttt {subj}(\texttt {v}_1,\texttt {s}_1)\wedge \texttt {dobj}(\texttt {v}_1,\texttt {o}_1)\wedge \texttt {subj}(\texttt {v}_2,\texttt {s}_1)\wedge \texttt {dobj}(\texttt {v}_1,\texttt {o}_2) $
An example are the sentences given in UNKREF28, which share the subject Albert Einstein and thus can be collaped into a single sentence.
Albert Einstein is a scientist and Albert Einstein is known for general relativity.
$\Rightarrow $ Albert Einstein is a scientist and known for general relativity.
Object grouping collapses the subjects of two sentences if the realizations of the verbs and objects of the sentences are the same, where the $\text{\em coord}\in \lbrace \text{\sf and},\text{\sf or}\rbrace $ is the coordination combining the input sentences $X_1$ and $X_2$, and $\texttt {coord}\in \lbrace \texttt {conj},\texttt {disj}\rbrace $ is the corresponding coordination combining the subjects.
$\rho (\texttt {o}_1)=\rho (\texttt {o}_2)\wedge \rho (\texttt {v}_1)=\rho (\texttt {v}_2)\wedge \texttt {cc}(\texttt {v}_1,\text{\em coord})$
$\Rightarrow \texttt {root}(Y,\texttt {PLURAL}(\texttt {v}_1))\wedge \, \texttt {subj}(\texttt {v}_1,\texttt {coord}(\texttt {s}_1,\texttt {s}_2))\wedge \texttt {dobj}(\texttt {v}_1,\texttt {o}_1) $
For example, the sentences in UNKREF30 share their verb and object, thus they can be collapsed into a single sentence. Note that to this end the singular auxiliary was needs to be transformed into its plural form were.
Benjamin Franklin was born in Boston. Leonard Nimoy was born in Boston. $\Rightarrow $ Benjamin Franklin and Leonard Nimoy were born in Boston.
Statements in RDF are expressed as so-called triples of the form (subject, predicate, object).
Answer: Statements in RDF are expressed as so-called triples of the form (subject, predicate, object).