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Introduction to mapping curation with SSSOM

Mappings between entities from ontologies, terminologies and databases are created for many reasons (data integration, knowledge graphs) and maintained in many different ways (automated matching, manual curation). In the following tutorial, we will learn how to curate semantic mappings manually using SSSOM. Knowledge about manual mapping curation is important even in scenarios where most, if not all, of the mapping curation is performed automatically - the basic principles are still the same.

Pre-requisites

We expect the reader of this tutorial to have a basic understanding of the following:

  • What are ontology classes? What is a database?
  • What is an (ontology) mapping?
  • Why do we need to map across ontologies and between databases and ontologies?

We do provide a few materials in the Background section below that touch on the above concepts, but a detailed discussion is out of scope.

Table of contents

Background

As a reminder, a SSSOM mapping comprises three major components:

  1. The mapping itself, that is, a triple <subject, predicate, object> that reflects a correspondence of a subject entity, for example a class in an ontology, to an object entity, for example an identifier in some database, via a semantic mapping predicate, such as skos:exactMatch.
  2. A mapping justification, the process or activity that led us to consider the mapping to be correct or reasonable (typical examples: labels match exactly; two classes are logically equivalent; a domain expert determined that two terms reflect the same real world concept).
  3. Provenance metadata, including information about author and mapping_tool.

In the following, we will give pointers to some useful background materials before we describe how SSSOM mappings are created.

Ontology alignment/matching

Ontology alignment is the process of determining correspondences between ontological concepts. The usage of "alignment", "matching" and "mapping" is fuzzy in practice. From the perspective of SSSOM, alignment usually involves determining all (or a more or less complete set of) correspondences between ontological concepts of two or more source ontologies. The most important resource on the subject is "Ontology Matching" by Jérôme Euzenat and Pavel Shvaiko. If you are interested in really diving into the subject, there is no avoiding this book!

This 25 minute course unit by the OpenHPI gives a nice overview over the area, which is relevant to all mapping activities:

Another useful overview is this one by the Knowledge and Data VU Amsterdam. Especially after minute 12, we learn a bit about the differences of OWL and SKOS.

A 10 minute deep-dive into Jerome Euzenat classification of ontology matching techniques can be seen here:

A 10 minute overview of Clinical Terminology Mappings by Michael Kahn at CU Anschutz can be seen in the following. This video is highly optional and reveals a very non-semantic webby perspective on the subject. It is focussed on OMOP mappings, one of the many terminology standards in the medical domain.

What are we mapping?

In SSSOM we are concerned with mapping information entities, i.e. representations of a real world entities. Examples of such entities are:

  • Classes, Individuals and Properties in an ontology.
  • Entities in Databases, such as a specific person in a "Person" table of a relational database.
  • A specific value in the slot of a data model, for example the "UNIVERSITY" constant in the highest-degree enumeration for a demographics survey data model.
  • A specific code from a code system or terminology such as ICD10CM.

Information entities represent real world objects such as diseases (e.g. Alzheimer's, Diabetes), kinds of vegetables (Asparagus, Broccoli), concrete instances of vegetables (a specific broccoli that was sold in your local supermarket yesterday).

What kind of entities can we not map with SSSOM?

Some of the limitations of SSSOM are discussed in our paper. A selection of the most important things that cannot be mapped at the moment:

  • Compound/complex entities, i.e. entities that are defined by more than one term. For example, we cannot currently map "Raw apple" (subject) to "Apple" and "Raw" (two objects).
  • Anything that is not an entity, e.g. unit conversion rules (1000mg maps to 1g * 1000) or functions.
  • Highly contextual entities like "PERSON:1" as they enter the hospital.

As a rule of thumb, we can map any entity for which (1) we can provide a single identifier and (2) whose identifier establishes its context (i.e. no further information is needed to understand the meaning of the identifier).

Note that literal values are a special case - SSSOM is not designed for mapping literals to entity identifiers, but there are some discussions on how to do this anyways here.

CURIEs, URIs and databases

A mapping involves three entities:

  1. A subject (the entity which is mapped to some other entity)
  2. An object (the entity the subject is mapped to)
  3. A semantic mapping predicate, such as "skos:exactMatch" which defines how the subject entity is mapped to the object entity.

All three must be referred to by an identifier in CURIE syntax (Compact URI) when using the SSSOM table format or JSON, or an IRI (Internationalized Resource Identifier) when you are using the RDF representation of SSSOM. This is necessary to ensure that entities are globally unique and mapping sets are fully interoperable across an organisation and beyond. While these concepts are common practice in the Semantic Web world, they may be less well understood in the database world. In fact, they can be quite awkward: - Your database my use p9787869 to identify a specific person in a "Person" table of a relational database. - Your data model for a demographics survey uses, among others, the UNIVERSITY constant in the highest-degree enumeration.

To be compliant with SSSOM, such values must be "curified". While this process sounds daunting at first, it is essential: Both the p9787869 identifier and the UNIVERSITY constant may be used in different contexts (different databases or data models) to refer to entirely different entities! While there is no 100% reliable guide for "curification", we usually recommend the following steps:

  • Choose a globally unique URI prefix which can unambiguously define the context of your entity. For example (1) http://embl.org/ebi/person/p9787869 to refer to the person in your Person table and (2) http://embl.org/demographics-survey-datamodel/demographics.highest_education#UNIVERSITY. In an ideal world, these can be de-referenced (i.e. you can look them up in a web-browser), but the important thing is that they are globally unique (and persistent), so that they cannot be confused with, for example, the UNIVERSITY code in another data model.
  • We select a reasonable prefix for the code, for example (1) embl.ebi.person and (2) demographics-survey-datamodel.demographics.highest_education. Note these do not need to be globally unique anymore. Indeed, you could, if you wanted to, use (much) shorter prefixes. (NOTE: some people disagree with this and strive for globally unique prefixes. In the biomedical domain, for example, we try to coordinate prefixes at http://bioregistry.io/. This is not however, necessary when using SSSOM).
  • We record the prefixes and their URI prefixes (sometimes called URI expansions) in the curie_map of our SSSOM file:
curie_map:
  embl.ebi.person: "http://embl.org/ebi/person/"
  demographics-survey-datamodel.demographics.highest_education: "http://embl.org/demographics-survey-datamodel/demographics.highest_education#"
  • Now we can refer to our entities in the SSSOM mapping table like this: (1) embl.ebi.person:p9787869 and (2) demographics-survey-datamodel.demographics.highest_education:UNIVERSITY.

This may strike some users as verbose - but the concept of unique identifiers for all information entities is at the heart of SSSOM. There is an initial cost to carefully defining namespaces for the various vocabularies and contexts (data model enums, value sets), but the ability to unambiguously refer to an entity will pay of as the organisation grows and data needs to be integrated from a wide variety of sources.

Tangent: See here for an example how FHIR, a standard for health care data exchange, published by HL7, deals with this: Rather than using a lot of prefixes, FHIR chooses to have one small namespace for fhir, and then having the path to the data model element all the way to its value as the local identifier.

How to create an SSSOM mapping set from scratch

SSSOM mapping sets can be created as part of automated processes, like ontology matchers, or manually by ontology curators. While there is overlap, it makes sense to look at both cases separately. To remind yourself why you should build SSSOM mapping sets in the first place, please refer to the FAQ.

Manually curating mapping sets

To gradually improve terminological mapping practices we are proposing a 5-star system for mappings. For the sake of this tutorial, we will focus on producing a solid 3-Star mapping set with the following metadata:

Core mapping metadata:

  • subject_id: The ID of the subject of the mapping
  • predicate_id: The ID of the predicate of the mapping
  • object_id: The ID of the object of the mapping

Mapping justification metadata:

  • mapping_justification: the process or activity that led us to believe the mapping to be correct or reasonable.

Basic provenance metadata:

  • mapping_date: The date the mapping was asserted. This is different from the date the mapping was published or compiled in a SSSOM file.
  • author_id: Identifies the persons or groups responsible for asserting the mappings. Recommended to be a (pipe-separated) list of ORCIDs or otherwise identifying URLs, but any identifying string (such as name and affiliation) is permissible.
  • mapping_set_description: A description of the mapping set, providing context and motivation.
  • license: An identifier for a license description.
  • mapping_set_id: A unique identifier of the mapping set.
  • mapping_set_version: The version of a mapping set.
  • subject_source: URI of source the subject.
  • subject_source_version: The version of the source of the subject.
  • object_source: URI of source the subject.
  • object_source_version: The version of the source of the object.
  • confidence: the level of certainty you have for the mapping to be true (based on the process used to confirm or generate it).

Some convenience metadata

  • subject_label: The human readable label of the subject.
  • object_label: The human readable label of the object.

The tutorial scenario

You are charged with aligning your organisations (KEWL FOODIE INC) internal database about food and nutrition with Food Ontology (FOODON). In your database, you have a table with food items:

ID LABEL
F001 apple
F002 gala
F003 pink
F004 braeburn

As a first pass, you are tasked to map the food items (kinds of apples) in your database to classes in the FOODON ontology.

Getting the tools together

To complete this tutorial, we need the following tools:

  1. A table editor. In this tutorial we will use Google Sheets. Manually curating mappings is often done in a collaborative fashion. We like Google Sheets because it allows multiple people to edit the same mapping set at once.
  2. OPTIONAL: The SSSOM toolkit installed (requires python 3.9+).

Creating a first draft of the mappings

First create a google sheet with the following columns:

subject_id subject_label predicate_id object_id object_label mapping_justification mapping_date author_id subject_source subject_source_version object_source object_source_version confidence

As we are mapping database identifiers, our first step is curiefy our database identifiers. Read up in detail on why this is done here.

We chose to use the following URI prefix for our food database: http://kewl-foodie.com/foods/, with the KF_FOODS: prefix (for now, we just document this information in the side, but later, we will add this to our mapping table).

Next, we will add all the entities we hope to align to the mapping table above (we removed some columns here for readability, we will get back to these later):

subject_id subject_label predicate_id object_id object_label confidence
KF_FOOD:F001 apple
KF_FOOD:F002 gala
KF_FOOD:F003 pink
KF_FOOD:F004 braeburn

While not necessary from a computational perspective, we recommend to document the labels of both the subject and the object to make the mapping table easier to process for human curators.

The next step is now to try and identify suitable terms from FOODON to map to. In the biomedical domain, most curators will search OLS or Ontobee, but some more technically advanced users may choose to use SPARQL over ontobee or another endpoint:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT * WHERE {
  ?sub rdfs:label ?obj .
  FILTER(regex(str(?obj), "apple"))
  FILTER(STRSTARTS(str(?sub),"http://purl.obolibrary.org/obo/FOODON_"))
}

A detailed discussion on mapping predicates can be found here.

Mapping "apple", attempt 1

Our first attempt is to try and map KF_FOOD:F001 (apple). At the time of writing, a search for the string "apple" just across the labels in FOODON reveals more than 300 results. There are no exact matches for the search string "apple", i.e. there is no entity in FOODON that has the label "apple" exactly. Rather than sifting through the large set of results, we move on to try to map a more specific element first. As FOODON is an ontology, having a mapping to a more specific element (e.g. gala) may help us to find an appropriate mapping for the more general concept (e.g. apple), which should be hierarchically related to the more specific term.

Mapping "gala"

Indeed, a search for "gala" reveals one single result: Gala apple (whole). How do we know if this is a good mapping for our own database entity gala? This is a very difficult question, and there is no perfect answer. It is important to remember that mappings should not be judged in terms of "correct" or "wrong", but in terms of "fit for purpose", or, in the case of SSSOM, "fit for most purposes". The following thoughts should cross the curators mind:

  • There does not seem to be another FOODON class concerned with "Gala".
  • From the description, "A pome fruit of a Gala apple tree cultivar." it seems like we are indeed talking about a kind of apple. (The picture in the OLS Term information box also helps.)
  • A quick email to our product team at KEWL FOODIE INC confirms that indeed, our gala database entity and FOODON's Gala apple (whole) class seem to refer to the same entity. As apples in our database are usually considered "whole", we do not concern ourselves further with the that slightly ambiguous part of the label. (Can I map my apple snack pack which has the "whole" apple cut in slices to FOODON:00003348?)

We add the new mapping to our mapping table. Due to our domain expertise and consultation with the product team of our company, we are very confident (1.0 or 100%) that the mapping between KF_FOOD:F002 and FOODON:00003348 is exact (for exact matches, we use skos:exactMatch as per SSSOM convention).

subject_id subject_label predicate_id object_id object_label confidence
KF_FOOD:F001 apple
KF_FOOD:F002 gala skos:exactMatch FOODON:00003348 Gala apple (whole) 1
KF_FOOD:F003 pink
KF_FOOD:F004 braeburn
Mapping "apple", attempt 2

Given our mapping of Gala apple (whole) we take a better look at the class hierarchy around. We notice three things:

  • There is indeed a class called "apple (whole)" which seems to fit our purpose. This also seems to be consistent with our choice of "Gala apple (whole)".
  • What is, however, annoying is that there is also a "apple (whole or parts)" class. KEWL FOODS INC definitely has plans to introduce products involving sliced Gala apples!
  • FOODON does not have a concept of a sliced Gala apple.

Again, our judgement as curators is asked here. There is no "correct" or "wrong". To keep things consistent, we decide to map to the "whole" apple, but we take a mental note that this might change in the future. We also take a physical note to document this design decision as a comment.

subject_id subject_label predicate_id object_id object_label confidence comment
KF_FOOD:F001 apple skos:exactMatch FOODON:00002473 apple (whole) 0.95 We could map to FOODON:03310788 instead to cover sliced apples, but only "whole" apple types exist.
KF_FOOD:F002 gala skos:exactMatch FOODON:00003348 Gala apple (whole) 1
KF_FOOD:F003 pink
KF_FOOD:F004 braeburn
Mapping "pink"

In the same hierarchy as apple (whole), we find Pink apple (whole). This is seems like an excellent match, consistent with our previous design decisions. However two observations leave us uncertain:

  • The Pink apple (whole) class has no definition (at the time of writing this tutorial at least) and no pictures, so we cannot be 100% certain that our notion of "pink" is the same as Foodon. A search on Wikipedia reveals different names, like "Pink Pearl" and "Pink Lady", which makes us a bit uncertain.
  • In contrast to "Gala apple (whole)", "Pink apple (whole)" has a further subclass, "Pink apple (whole, raw)". What does that mean? All data in our KEWL FOODS INC database pertains to raw apple, so is this now a better match? Raw as opposed to what? Cooked?

Again, there is no great recipe to solve this dilemma. We chose our default recipe:

  1. prefer consistent mapping rules over occasionally increased precision (not always a good idea)
  2. document design decision
subject_id subject_label predicate_id object_id object_label confidence comment
KF_FOOD:F001 apple skos:exactMatch FOODON:00002473 apple (whole) 0.95 We could map to FOODON:03310788 instead to cover sliced apples, but only "whole" apple types exist.
KF_FOOD:F002 gala skos:exactMatch FOODON:00003348 Gala apple (whole) 1
KF_FOOD:F003 pink skos:exactMatch FOODON:00004186 Pink apple (whole) 0.9 We could map to FOODON:00004187 instead which more specifically refers to "raw" Pink apples. Decided against to be consistent with other mapping choices.
KF_FOOD:F004 braeburn
Mapping "braeburn"

We now turn our attention to the last database entity: KF_FOOD:F004 (braeburn).

Unfortunately, our search for braeburn, brae-burn yields no results in Foodon. We search Wikipedia and Google for potential synonyms of Braeburn that might have been missed by the FOODON developers, but are unsuccessful. In the end, we give up and decide that there is no matching concept for KF_FOOD:F004 (braeburn) in FOODON. Now we have to make a choice and how to reflect that in our mapping set:

  • We can document directly the fact that there is no skos:exactMatch in our SSSOM table.
  • We can map KF_FOOD:F004 (braeburn) to a more general concept, i.e. apple (whole).
  • We can do both.

For our data integration efforts, it is generally useful to know if no exact match could be found. Here, again, we have two options:

  • we can convey this information by omission. By not including a mapping in the dataset, it does not exist. The downside is that we do not know further down the line if (a) we have looked and there really was no suitable code and (b) we have not looked.
  • we can convey this information by using a special code sssom:NoMapping. (NOTE as of 2 May 2022, the final decision on how this is represented has not been made. Follow this discussion).

In our case, we have plans to extend our manual mapping efforts with automated ones. We want to use manual non-mapping assertions to filter out false positive mappings with our automated approaches, so we decide to go with the second option and make the non-mapping explicit.

The second question is whether to include a less precise mapping. This depends heavily on the target use case. As a rule of thumb, if the target use case requires precise 1:1 mappings (for example, data transformation use cases often do), we do not include any broad mappings. If our use case is data aggregation, broad matches can still be very useful: At least, we will be able to use the hierarchical structure of FOODON to retrieve all kinds of apples in our FOOD database! We are interested in data aggregation, so we decide to include the mapping.

subject_id subject_label predicate_id object_id object_label confidence comment
KF_FOOD:F001 apple skos:exactMatch FOODON:00002473 apple (whole) 0.95 We could map to FOODON:03310788 instead to cover sliced apples, but only "whole" apple types exist.
KF_FOOD:F002 gala skos:exactMatch FOODON:00003348 Gala apple (whole) 1
KF_FOOD:F003 pink skos:exactMatch FOODON:00004186 Pink apple (whole) 0.9 We could map to FOODON:00004187 instead which more specifically refers to "raw" Pink apples. Decided against to be consistent with other mapping choices.
KF_FOOD:F004 braeburn skos:exactMatch sssom:NoMapping 1
KF_FOOD:F004 braeburn skos:broadMatch FOODON:00002473 apple (whole) 1

Adding rich metadata

We are done curating the basic mappings. Next, we will add some richer metadata for the mapping set. For this tutorial we will add the metadata introduce here.

Mapping justification metadata:

mapping_justification: the process or activity that led us to believe the mapping to be correct or reasonable.

This is the most important piece of metadata and a pivotal concept for SSSOM curation in general. Let us think about all the various ways that can lead us to believe a mapping to be correct.

The most crude thing would be to document is: "a Human determined this mapping". We do that by documenting the mapping justification sempav:HumanCuration. This justification is a vague placeholder, but it instills some confidence in the mapping consumer (the user) that someone with at least some domain expertise determined the mapping to be ok. We will discuss mapping_justifications in more detailed in a later tutorial on automated matching, where we have many more fine-grained distinctions, like "the justification for asserting this mapping is that the label of the subject matches to an exact synonym of the object after applying 'stemming' during preprocessing". Nevertheless, modelling human curation better is one of the future goals of SSSOM. The key is to document "curation rules", which contain the conditions and assumptions made by the (human) mapping author when asserting the mapping. In the absence of a formal element (at least at the time of this writing, May 2022), you should try and document such curation rules in the comment field.

Basic provenance metadata:

mapping_date: The date the mapping was asserted.

Why is this important? Time of an assertion is essential provenance. It allows us to prefer assertions (mapping decisions) that were done later, but it also gives us a hint how old a mapping is, in particular if the source versions are not, or cannot, be documented. It is a very easy element to document, and we should try to do that at all times.

author_id: Identifies the persons or groups responsible for asserting the mappings.

The author is a crucial bit of metadata, in particular in conjunction with the mapping justification human curation. A mapping consumer can look up the author of a mapping through their unique identifier (e.g. an ORCiD, which we use in the biomedical domain, but might be anything, including a unique database identifier). Again, we prefer PURLs here, that resolve to some useful information when you look them up.

mapping_set_id: A unique identifier of the mapping set. This is a pivotal concept in FAIR data and data management in general: every unit of data that is shared around within an organisation (or the whole world) should have a unique identifier. As per Semantic Web conventions, we recommend using persistent URLs, or PURLs, to identify your mappings set. For example: http://purl.obolibrary.org/obo/mondo.owl is a unique identifier to an ontology and http://purl.obolibrary.org/obo/mondo/mapping/mondo.sssom.tsv refers to the "Mondo disease mappings".

mapping_set_version: The version of a mapping set. Versioning is absolutely crucial for mapping sets, much the same way as it is for ontologies. We recommend to use semantic versioning or simple ISO Date versioning, like "2022-05-01". The latter is recommended by some organisations like the OBO foundry (it is easier to see how new a mapping set is, and it is easier to sort as a string), but semantic versioning is much more widely used. We use date based versioning in the tutorial.

mapping_set_description: A description of the mapping set, providing context and motivation. This is another underrated piece of metadata that allows humans to understand and build trust towards a mapping set. A good description of a mapping set

  • describes the scope and content of a mapping set
  • describes the purpose for the creation of the mapping set
  • is reasonably short, but not too short (3-4 sentences)

license: An identifier for a license description. One of the most serious impediments to re-use on the web is the absence of clear and standardised licenses. We recommend the creative commons licenses for open data, either CC-0 (public domain, no license) or CC-BY 4.0. (Some people prefer CC-BY 4.0, because it ensures that attribution is taken more seriously.) Even when using a proprietary license, it is good to be transparent here, so that an "accidentally leaked" data file is not mistakenly assumed to be "open".

subject_source: URI of source the subject. This is one of the most important pieces of metadata: an unambiguous reference to a source. It is notoriously hard to standardise source references (see past debate). We recommend to use the standard URIs used in your own domain, for example OBO (obo:mondo) or Wikidata (wikidata:Q7876491).

subject_source_version: The version of the source of the subject. In order to interpret a mapping, it is not enough to know the source. Sources changes all the time, whether they are database and/or ontology: classes are obsoleted, database records are deleted. What counts for an exact mapping may change through the evolution of a source. Always document the source version, if you can. This can be very difficult for database systems that do not have a real notion of versioning.

object_source: URI of source the object. See subject_source.

object_source_version: The version of the source of the object. See subject_source_version.

Mapping vs Mapping set metadata - where should it go?

SSSOM distinguishes between mapping and mapping_set metadata, i.e. metadata that pertains to each individual mapping and metadata that pertains to the whole mapping set. To understand which is which, you can browse the specification.

Mapping metadata is usually captured in the rows of the SSSOM mapping table. We have done this a lot so far during this tutorial: documenting our confidence in our mapping decision, and specifying the source of our subject id. However, in SSSOM we have the option to document some mapping metadata on the level of the mapping_set, which means that the metadata item applies to all mappings in the mapping set. We will capture subject and object_source this way, see a bit further below. We capture mapping level metadata in the usual way using our table:

subject_id subject_label predicate_id object_id object_label confidence comment mapping_justification mapping_date author_id subject_source_version object_source_version
KF_FOOD:F001 apple skos:exactMatch FOODON:00002473 apple (whole) 0.95 We could map to FOODON:03310788 instead to cover sliced apples, but only "whole" apple types exist. sempav:HumanCuration 2022-05-02 orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl
KF_FOOD:F002 gala skos:exactMatch FOODON:00003348 Gala apple (whole) 1 sempav:HumanCuration 2022-05-02 orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl
KF_FOOD:F003 pink skos:exactMatch FOODON:00004186 Pink apple (whole) 0.9 We could map to FOODON:00004187 instead which more specifically refers to "raw" Pink apples. Decided against to be consistent with other mapping choices. sempav:HumanCuration 2022-05-02 orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl
KF_FOOD:F004 braeburn skos:exactMatch sssom:NoMapping 1 sempav:HumanCuration 2022-05-02 orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl
KF_FOOD:F004 braeburn skos:broadMatch FOODON:00002473 apple (whole) 1 sempav:HumanCuration 2022-05-02 orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl

Mapping set metadata. In this tutorial, only mapping_set_id, mapping_set_version, license and mapping_set_description are purely mapping_set metadata. Everything else is considered mapping metadata. Mapping set metadata is captured in YAML format. For this tutorial, we will capture the following:

mapping_set_id: https://w3id.org/sssom/tutorial/example1.sssom.tsv
license: https://creativecommons.org/licenses/by/4.0/
mapping_set_version: "2022-06-01"
mapping_set_description: "Manually curated alignment of KEWL FOODIE INC internal food and nutrition database with Food Ontology (FOODON). Intended to be used for ontological analysis and grouping of KEWL FOODIE INC related data."
object_source: wikidata:Q55118395
subject_source: KF_FOOD:DB
curie_map:
  KF_FOOD: https://kewl-foodie.inc/food/
  wikidata: http://www.wikidata.org/entity/
  FOODON: http://purl.obolibrary.org/obo/FOODON_
  sempav: https://w3id.org/semapv/
  skos: "http://www.w3.org/2004/02/skos/core#"
  sssom: https://w3id.org/sssom/

Despite object_source and subject_source being mapping metadata, we decided to capture them at mapping set level, as they are not likely to change throughout versions of the mapping set. Note that while the object_source resolves to an actual page on the web (FOODON), KF_FOOD:DB does not. SSSOM requires a source to correspond to an IRI (see ongoing debate). This helps to ensure that it is unambiguously clear what the source was. Imagine someone documenting the string INTERNAL_DB or just DB - even in large organisations, but certainly on the web, this can cause clashes.

The curie_map (better known as "prefix map") is another key concept in SSSOM (and most Semantic Web standards). It maps prefixes to URI expansions. This serves three main purposes.

  1. Unambiguously identify the namespace of a prefix. The prefix FOODON:, all by itself, can be used by many different sources. http://purl.obolibrary.org/obo/FOODON_ uniquely identifies the namespace of FOODON. This is important when merging different mapping sets together.
  2. Expanding and resolving identifiers. Some identifier schemes like the one in the OBO Foundry, Wikidata and many others, resolve identifiers to a page on the web. This allows people (and sometimes machines) to look up additional information about an entity on the web. For example, when we expand FOODON:00002473 to http://purl.obolibrary.org/obo/FOODON_00002473, we can look this URI up in a browser.
  3. Providing a recipe for creating RDF resources from CURIEs. RDF requires an entity to be represented by a full URI, e.g. http://purl.obolibrary.org/obo/FOODON_00002473. In this case, you can think of the curie_map in essence as a set of RDF prefix declarations. This is only important if your use case requires serialisation into RDF.

This concludes the manual curation tutorial. Next, we will process the two mapping sets using "SSSOM python toolkit" (aka sssom-py).

Automated processing 1: Creating an embedded SSSOM file

Important note May 8 2022*: The SSSOM toolkit have not yet been updated to the most recent changes of the SSSOM data model. If you get an error ValueError: match_type must be supplied, you have to update your local installation.

Embedded vs external mode for SSSOM metadata

One problem with table formats like TSV or CSV, in contrast to more flexible tree shaped formats like JSON or XML, is that it is notoriously hard to include metadata about the whole table (for example, mapping set metadata) in them. There are essentially three options:

  1. All metadata is stored as values in columns. While this is definitely possible, it is not ideal for a few reasons:
    1. It is highly redundant. If we have to store the mapping_set_id, for example, as a value in a mapping table with 1000 mappings, it is repeated 1000 times.
    2. It is less immediately clear whether a piece of metadata pertains to the mapping_set or a mapping (you have to study the specification to understand that author_id pertains to an individual mapping rather than the whole mapping set).
  2. Metadata about the mapping set is stored within the TSV file header. Basically, we introduce a number of rows at the top of the TSV file that we reserve for metadata. The disadvantage is that many parsers for such flat files do not know how to deal with a header like this.
  3. We keep metadata about tables and mapping sets separate, i.e. we keep one TSV file that contains the data and one YAML file that contains the mapping set metadata. This is often a good option, but keeping the two separate may cause a problem: in environments where the data is shared around (emailed, copied) the connection can get lost.

In SSSOM, we opted for option 2 as the default, which we call "embedded mode" (the metadata is embedded). Most commands in the SSSOM toolkit expect SSSOM files to be in embedded mode. However, we support option 3 (external mode) indirectly by providing operations to simply merge the two before other processing steps.

Converting an SSSOM file from from external to embedded mode

If you do not have the SSSOM toolkit installed, do so now.

Download the food mappings created before. If you feel confident with your own mappings, feel free to use these instead.

Now you let's use SSSOM toolkit to merge these two:

sssom parse example1.sssom.tsv -m example1.sssom.yml -o foodieinc-food.sssom.tsv

If you open foodieinc-food.sssom.tsv, you will see:

# comment: We could map to FOODON:00004187 instead which more specifically refers to
#   "raw" Pink apples. Decided against to be consistent with other mapping choices.
# curie_map:
#   FOODON: http://purl.obolibrary.org/obo/FOODON_
#   KF_FOOD: https://kewl-foodie.inc/food/
#   skos: http://www.w3.org/2004/02/skos/core#
#   sssom: https://w3id.org/sssom/
# license: https://creativecommons.org/licenses/by/4.0/
# mapping_date: '2022-05-02'
# mapping_set_description: Manually curated alignment of KEWL FOODIE INC internal food
#   and nutrition database with Food Ontology (FOODON). Intended to be used for ontological
#   analysis and grouping of KEWL FOODIE INC related data.
# mapping_set_id: https://w3id.org/sssom/tutorial/example1.sssom.tsv
# mapping_set_version: '2022-06-01'
# object_source: wikidata:Q55118395
# object_source_version: http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl
# subject_source: KF_FOOD:DB
subject_id  subject_label   predicate_id    object_id   object_label    mapping_justification   author_id   object_source_version   mapping_date    confidence  comment
KF_FOOD:F001    apple   skos:exactMatch FOODON:00002473 apple (whole)   semapv:ManualMappingCuration    orcid:0000-0002-7356-1779   http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl    2022-05-02  0.95    "We could map to FOODON:03310788 instead to cover sliced apples, but only ""whole"" apple types exist."
KF_FOOD:F002    gala    skos:exactMatch FOODON:00003348 Gala apple (whole)  semapv:ManualMappingCuration    orcid:0000-0002-7356-1779   http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl    2022-05-02  1.0 
KF_FOOD:F003    pink    skos:exactMatch FOODON:00004186 Pink apple (whole)  semapv:ManualMappingCuration    orcid:0000-0002-7356-1779   http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl    2022-05-02  0.9 "We could map to FOODON:00004187 instead which more specifically refers to ""raw"" Pink apples. Decided against to be consistent with other mapping choices."
KF_FOOD:F004    braeburn    skos:exactMatch sssom:NoMapping     semapv:ManualMappingCuration    orcid:0000-0002-7356-1779   http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl    2022-05-02  1.0 
KF_FOOD:F004    braeburn    skos:broadMatch FOODON:00002473 apple (whole)   semapv:ManualMappingCuration    orcid:0000-0002-7356-1779   http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl    2022-05-02  1.0 

Converting an SSSOM file to JSON

We will now convert the embedded SSSOM file we created before into JSON:

sssom convert foodieinc-food.sssom.tsv --output-format json -o foodieinc-food.sssom.json

While the JSON format is not yet stable, it is close to completion.

Diff between two versions

The last part of this tutorial concerns one of the main motivations of using a controlled metadata model for mappings: versioning. One key concern for data management, and mapping management in particular, is to be able under understand the evolution of mappings over time. While this command is not stable yet, we can use it to understand the difference between two mappings sets: sssom diff. Let us try to look at the difference between an old version of our foodie-inc mapping set and our new one:

sssom diff foodieinc-food.sssom.tsv ../embedded/foodie-inc-2022-05-01.sssom.tsv -o diff.sssom.tsv

The outcome gives us the following information:

subject_id subject_label predicate_id object_id object_label mapping_justification author_id object_source_version mapping_date confidence comment
KF_FOOD:F003 pink skos:exactMatch FOODON:00004186 Pink apple (whole) semapv:ManualMappingCuration orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl 2022-05-02 0.9 UNIQUE_1
KF_FOOD:F003 pink skos:exactMatch FOODON:00004187 Pink apple (whole, raw) semapv:ManualMappingCuration orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl 2022-05-02 0.9 UNIQUE_2
KF_FOOD:F002 gala skos:exactMatch FOODON:00003348 Gala apple (whole) semapv:ManualMappingCuration orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl 2022-05-02 1.0 COMMON_TO_BOTH
KF_FOOD:F004 braeburn skos:broadMatch FOODON:00002473 apple (whole) semapv:ManualMappingCuration orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl 2022-05-02 1.0 COMMON_TO_BOTH
KF_FOOD:F001 apple skos:exactMatch FOODON:00002473 apple (whole) semapv:ManualMappingCuration orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl 2022-05-02 0.95 COMMON_TO_BOTH
KF_FOOD:F004 braeburn skos:exactMatch sssom:NoMapping semapv:ManualMappingCuration orcid:0000-0002-7356-1779 http://purl.obolibrary.org/obo/foodon/releases/2022-02-01/foodon.owl 2022-05-02 1.0 COMMON_TO_BOTH

This can be used to understand that the first mapping is only present in the new mapping set, while the second mapping was present in the old mapping set - all the other ones are in common between the two.