Making Sense of schema.org with W ord N et (2019)

Abstract: The schema.org initiative was designed to introduce machine readable metadata into the World Wide Web. This paper investigates conceptual biases in the schema through a mapping exercise between schema.org types and WordNet synsets. We create a mapping ontology which establishes the relationship between schema metadata types and the corresponding everyday concepts. This in turn can be used to enhance metadata annotation to include a more complete description of knowledge on the Web of data.

## introduction schema.org is an initiative to introduce machine readable metadata into html web pages. it was launched on june 2, 2011, under the auspices of a consortium consisting of google, bing, and yahoo!. the schema.org web site initially described the project as one that "provides a collection of schemas, i.e., html tags, that webmasters can use to markup their pages in ways recognized by major search providers . . . . making it easier for people to find the right web pages." (schema.org web site, 2011). the incentive for using the schema was that web sites that contained markup would appear with informative details in search results which in turn enables people to judge the relevance of the site more accurately. this could lead to higher user engagement and higher search ranking, which is the ultimate incentive for web masters. the initial release contained 297 classes and 187 relations, but by 2016 had grown to 638 classes and 965 relations @xcite . it is important to note, however, that the expansion of the schema consists entirely in adding subclasses and properties to the core classes through the al-lowed extension mechanism foot_0 . from the outset the immediate sub classes of thing were stiulated as action, creativework, event, intangible, organization, person, place and product. these high level conceptual divisions with their implicit ontological commitments are not, and never were open to discussion. @xcite explain that the primary driving force behind the design of the schema, and ultimately the reason for its success, was its simplicity. previous efforts to introduce large scale metadata failed, in part because each standard was too narrow in terms of domain coverage. the result was too many standards for too few applications. on the other hand the schema offered a single, unified and broad vocabulary that could be used across several verticals and promised a benefit for perhaps the most important driving force, search rankings. as a part of this simplicity, the schema taxonomy and classes were intended more as an "organisational tool to help browse the vocabulary" than a definitive ontology of world @xcite . in other words, the schema was designed as an intuitive set of metadata classes that could be used to describe the majority of items people would search for on the web. together these factors ensured that the schema has enjoyed a significant amount of success. @xcite report that in a sample of 10 billion web pages, 31.3% of the pages had schema.org markup, a growth of 22% from a year earlier. the markup is used by many different data consumers for various tasks involving enhanced search results (rich snippets), populating the google knowledge graph, exchange of transaction details in email, support for automatic formatting of recipes, reviews, etc., and advanced search features in apple's siri. the fifteen most popular implemented classes were website, searchac-tion, webpage, product,imageobject, person, offer, blogposting, organization, article, postal-address, blog, localbusiness, aggregaterating, wpfooter. many of these refer to elements of the web page itself rather than the content. the top fifteen content bearing classes were product, im-ageobject, person, offer, organization, postaladdress, localbusiness, aggregaterating, creative-work, review, place, rating, event, geocoordinates, and thing. these are sun types of product, creativework, person, intangible, organization, place, and event. although the coverage was intended to be broad, it is clear that the use of the schema covers its range of types well, but that the types favour a particular view of web content, in the interests of the search providers. the motivation for this paper was to try and characterize the conceptual biases of the schema top level categories, by mapping the types to their corresponding meanings in wordnet. to the extent that we believe wordnet captures the ontological commitments inherent in human language, it should provide insights about where the two conceptualisations diverge. the further aim, however, is to use the mappings to enrich the valuable human provided metadata towards the aim of providing general but rich meaning annotations to a large portion of web content. it is important to note that we are not advocating wordnet as a gold standard for ontologies and knowledge representation. on the contrary, we agree with @xcite who argues that wordnet contains modeling decisions which differentiate it from formal ontologies. as an example, there are cases where synsets have overlapping hyponyms whereas ontologies have disjoint subclasses. consider the first noun sense of mistake: {mistake, error, fault} which includes the following hyponyms (among others): {slip, slipup, miscue, parapraxis}, {oversight, lapse}, {faux pas, gaffe, solecism, slip, gaucherie}, and {fail-ure}. a single act can be both a slip and a faux pas. the first implies the act was inadvertent, and the second that it possibly had a social component such as a mistake in etiquette. a lapse is also a slip, but it involves some sort of forgetfulness or inattention on top of the mere slip. a lapse can also be a faux pas, of course. if the faux pas is sufficiently severe, it can become a complete failure. these hyponyms contain more information that that they are a kind-of mistake, they also con-tain information about likely causes and implications, and these can be overlapping. nevertheless, our interest is that people do consider these as kinds of mistake in everyday discourse. for the same reason we think it is beside the point to try and restrucutre wordnet by some formal methodology such as dolce @xcite . we are interested here in intuitive relations, not formal ones. ## the wordnet mappings the mapping involved two stages. first the schema.org types were aligned with wordnet synsets, while retaining the structure of the schema. this stage can be seen as adding information to the schema, namely, the corresponding wordnet synsets. then, a new hierarchy of concepts was constructed from the synsets involved in the mapping. that is, by promoting the mapped synsets to be the central classes, we could get a better idea what sorts of concepts are in the schema, in relation to the wordnet taxonomy. in order to distinguish between the concepts in the two taxonomies, wordnet names will be prefixed with wn: and the schema with the prefix schema:. in addition when necessary the word-net name will be qualified with part of speech and sense tag, as in wn:dog#n#1. to summarize, we constructed two artefacts at the end of the process: ## finding correct mappings there are a number of potential pitfalls in defining appropriate mappings between the two taxonomies. one of the most important is to avoid introducing unwanted inferences from the semantics of the mapping axioms. a prevalent example of this is the use of owl:sameas to represent equivalence between individuals, or classes in owl-full. owl:sameas asserts full equivalence between the individuals such that all of their properties are automatically shared, even though most commonly this is not the desired consequence @xcite . to avoid this problem we used the weaker owl:equivalentclass axiom, which does not imply complete equality. what is required instead is the weaker condition that every instance of one class must also be an instance of the other. even with a weaker semantics we found that equivalent classes could not always be found. one reason is that schema.org includes concepts which involve various sorts of compounding of simple concepts, and wordnet contains only common, lexicalized compounds. for example land-marksorhistoricalbuildings is a compound concept that includes any kind of general landmark as well as the specific concept of buildings with historical significance. there is no such lexical entry in english. most likely there is no such compound in any language, because the concept is un-natural, mixing different levels of generalization. it is analogous to a concept for toys or teddy bears. there are also more acceptable compounds like schema:civicstructure which is "a public structure such as a town hall or concert hall". this is of course a perfectly acceptable compound, which happens not to be in wordnet. in every case that an acceptable wordnet compound could not be found, we decided to make the schema.org concept a subclass of one or more wordnet synsets that captured part of the compound. for the above example of schema:civicstructure, the obvious superclass is wn:structure#n#1. sometimes the compound nature of the schema terms is hidden. for example the terms that are subclasses of schema:localbusiness are a mixed group of explicit compounds (e.g., schema:movingcompany, schema:icecreamshop) and implicit compounds (e.g., schema:electrician, schema:locksmith, schema:housepainter). that is, schema:electrician is really meant to be something like "electricianbusiness" and not just "electrician". the compound schema:housepainter is even more complicated because it has an exact match in wn:house_painter#n#1, but in fact schema:housepainter is really meant to be a housepainterbusiness, so the exact match is illusory. the important modelling decision is whether or not to reintroduce the hidden compound in mapping to wordnet. that is, should schema:electrician be regarded in its ordinary word sense as "a person who is an electrician", or should it be modelled as an "electrician business"? in other words, these concepts could simply be declared as subclasses of wn:place_of_business to maintain the intended interpretation in the schema. the most flexible solution was to declare an equivalent class relation between schema:electrician and the person interpretation in wordnet, wn:electrician#n#1. this choice captures the notion that electricians are people. however it is also possible to infer that wn:electrician is a wn:place_of_business, as shown in figure 5 . there is a small set of schema.org types for which we did not establish mappings. one group involved technical compounds describing the structure of web pages with terms like schema:aboutpage and schema:checkoutpage. these are all subtypes of schema:webpage, for which we did define a mapping. the second group was the primitive data types, schema:datatype which are not part of the main taxonomy subsumed by schema:thing. ## using the wordnet mappings the practical motivation for mapping the schema to wordnet was to enrich the metadata that can be assigned to concepts in a web page. we have already seen this in examples such as beach. a secondary motivation was to make it easier for web masters to find the schema types without knowing anything about its structure. we have already developed a prototype of a tool in which the user can highlight any word in text, nominate its corresponding synset, and the application will attempt to guess the correct schema type. consider the following example scenario. there is a geological landmark called the jenolan caves in the blue mountains, australia. suppose a web master wanted to mark up the web site for jenolan caves. a quick search will reveal that there is no matching type in the schema for caves. using the wordnet mappings it is possible for the designer to find the most appropriate types, without any knowledge of the schema. the synset wn:cave is a wn:geological_formation, which in turn maps to schema:landform. however, the mapping ontology can also suggest additional useful classifications. the coordinate terms of wn:cave contain some terms which are defined in the schema, including our old friend beach. recall that wn:beach is mapped to schema:civicstructure through schema:beach (see figure 6 ). thus jenolan caves could be marked with both schema types, and the properties of the facilities at the premises could be specified. of course the annotation effort does not have to stop there. since the wordnet synset is available, it can also be included in the markup, which in turn enables the markup to be used with a huge number of mappings to other resources foot_2 . while this process is currently being performed through our prototype tool where users specify the disambiguated sense @xcite , this does not necessarily have to be performed manually. with sufficiently accurate disambiguation methods, any web page could be automatically annotated with schema and wordnet metadata. this would be useful for any downstream task including the construction of knowledge graphs, as previously mentioned. the jenolan caves example requires the ability to declare multiple types. the original syntax for the schema, microdata is not able to express multiple types. the recommendation therefore is to use rdf-a foot_3 or json-ld foot_4 which are inherently built to express multiple types from any vocabulary. ## conclusion we proposed a method for evaluating the conceptual bias of schema.org by comparing the type terms against their usage in everyday language as stipulated in wordnet. the observation is that schema.org favours the markup of web sites promoting goods, services, and locations fulfilling some human centred need. this then results in the observed data that the majority of web sites which contain schema.org, are about products and goods and services. if search rankings favour sites with markup, and if most markup is about goods and services, then search results will come to favour goods and services. anecdotally, this could be one factor for why it is sometimes easier to find where to buy something rather than information about the thing itself. the bias diminishes the potential for providing a rich source of general semantic metadata on the web, for use in diverse use cases. we argued that the schema needs types that de-scribe a more neutral view of the world, for example artefacts, to describe things independently of the roles they can play. a metadata specification should be able to annotate a chicken as a kind of bird as well as a kind of food. our suggestion to include wordnet mappings into the markup effort is one way to sneak more general markup into the annotation process. the requirement is that multiple types must be a standard feature of the annotation, with different types describing different aspects of the item. a car is an artefact designed for locomotion, but can also acquire its role as a product if it is put up for sale. this addition would not compromise people who want to advertise their products. in fact, it would give them more freedom to express physical properties of their products like size, construction material, origin, and so on. in summary, we used wordnet as a standard representation of everyday word use, to provide clarity to the types proposed in schema.org. we proposed a method to help people mark up web sites that do not fit neatly into the service oriented world view, by enabling them to annotate their contribution to world knowledge as broadly as possible. this is clearly of benefit to all users who see the web as a vehicle for disseminating informative structured data as freely as possible.