semantic web 0 (0) 1 1 ios press applying and …cial theory of relativity by albert einstein in...
TRANSCRIPT
Semantic Web 0 (0) 1 1IOS Press
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
Applying and Developing Semantic WebTechnologies for Exploiting a Corpus inHistory of Science: the Case Study of theHenri Poincaré CorrespondenceOlivier Bruneau a,*, Nicolas Lasolle a,b, Jean Lieber b, Emmanuel Nauer b, Siyana Pavlova a,b, andLaurent Rollet a
a AHP-PReST, Université de Lorraine, CNRS, Université de Strasbourg, FranceE-mails: [email protected], [email protected], [email protected],[email protected] LORIA, Université de Lorraine, CNRS, Inria, FranceE-mails: [email protected], [email protected], [email protected],[email protected]
Abstract. The Henri Poincaré correspondence is a corpus of letters sent and received by this mathematician. The edition of thiscorrespondence is a long-term project begun during the 1990s. Since 1999, a website is devoted to publish online this correspon-dence with digitized letters. In 2017, it has been decided to reforge this website using Omeka S. This content management systemoffers useful services but some user needs have led to the development of an RDFS infrastructure associated to it. Approximateand explained searches are managed thanks to SPARQL query transformations. A prototype for efficient RDF annotation of thiscorpus (and similar corpora) has been designed and implemented. This article deals with these three research issues and howthey are addressed.
Keywords: history of science, Digital Humanities, Henri Poincaré, scientific correspondence, RDF(S), approximate andexplained search, SPARQL query transformation, corpus annotation
1. Introduction
Jules Henri Poincaré is a great name of the historyof science. Born in Nancy (France) on April 29, 1854,he died in Paris on July 17, 1912.
His name is associated with discoveries or worksof primary importance. He is responsible for, amongother things, the discovery of Fuchsian functions inmathematics, an essential contribution to the resolu-tion of the Three-Body problem in celestial mechan-ics, for which he won the Oscar II Prize, King of Swe-den’s mathematical competition, in 1889, as well as the
*Corresponding author. E-mail: [email protected].
introduction in topology of the concepts of homotopyand homology. His theoretical research on new me-chanics after 1900 prepared the discovery of the spe-cial theory of relativity by Albert Einstein in 1905.
Mathematician, physicist, astronomer, research ad-ministrator, Henri Poincaré was also very active in thefield of philosophy as a regular contributor to the Re-vue de métaphysique et de morale. His philosophicalbooks, such as La science et l’hypothèse [Science andHypothesis] [1] contributed to the birth of French phi-losophy of sciences and gave him a great reputationin France and at international level. Although not veryengaged on the political scene, he nevertheless played
1570-0844/0-1900/$35.00 c© 0 – IOS Press and the authors. All rights reserved
2 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
a significant role in the Dreyfus Affair through severalmathematical expertises.
Henri Poincaré was elected in 1887 at the Académiedes sciences of Paris, at the age of 33. Throughout hislife he was also a member of the Bureau des longitudes(1893), of the Académie Française (1908) and of nu-merous foreign learned societies and academies.
In 1992, the laboratory of history of science and phi-losophy Archives Henri Poincaré was created to studyHenri Poincaré’s manuscripts and to organize the pub-lication of his scientific and private correspondence.For more than 25 years, this long-term project has pro-duced four volumes of letters. The first one is devotedto the letters exchanged between Henri Poincaré andthe Swedish mathematician Gösta Mittag-Leffler [2].The second one concerns the correspondence withphysicists, chemists and engineers [3]. The third vol-ume gathers the correspondence with astronomers and,in particular, geodesists [4]. The fourth one deals withthe Henri Poincaré’s youth correspondence [5]. Twoother volumes are in preparation. The first one will bedevoted to the letters from or of mathematicians. Thesecond will contain Henri Poincaré’s administrative,academic and private correspondence.
The corpus consists of around 2100 letters, 1126sent by Henri Poincaré and 956 received by him. Someother letters have been discovered recently. About 50%of these letters form a correspondence with scientists.Original letters come from 63 different archive centersand libraries from 14 countries. All known letters aredigitized1 and around 60% of them are in plain text(in LATEX and XML versions). Lots of letters containmathematical and physical formulae. The correspon-dence is available on Henri Poincaré website.2 The let-ters of this website are indexed with Dublin Core ex-tended metadata.3 This enables to query the corpus by
1Due to copyright laws, some images of letters are not availableonline, though transcripts are.
2 http://henripoincare.fr.3Different projects exist that are devoted to scientific corre-
spondences for example the CKCC project (http://ckcc.huygens.knaw.nl [6]) or Mapping the Republic of Letters project (http://republicofletters.stanford.edu). More generally, several projects inSemantic Web are dedicated to history. A recent paper [7] proposes astate of the art on this issue. For instance, SemanticHPST is a projectin which Semantic Web principles are applied to the history and phi-losophy of science and technology [8]. A newly founded Europeanconsortium called Data for History (http://dataforhistory.org/) aimsat uniting such projects.
e.g.
Q1 =“Give me the letters sent by HenriPoincaré in 1885”
Q2 =“Give me the letters received by EugénieLaunois between 1882 and 1894”
Plain text search engine exists for the letters that arealready transcribed4 but the proposed set of results canbe incomplete or incorrect. For instance, if the queryis to find letters sent or received by Henri Poincaréwith the topic “lacunary functions”, the result is onlyone letter though there are more than 10 letters refer-ring partially to this topic. The problem is that, in theseletters, the term “lacunaire” does not explicitly occur,hence the incompleteness of this search from a seman-tic viewpoint. In other letters, “espaces lacunaires” canbe found but it refers to titles of Henri Poincaré’s pa-pers, hence its incorrectness.
This article is organized as follows. Section 2presents the Henri Poincaré correspondence and how itwas edited, annotated and published on the web of doc-uments. It has appeared that principles and technolo-gies of the Semantic Web should prove useful for theexploitation of this corpus, in particular, the RDF(S)technology that is briefly recalled in Section 3, withsome examples related to the corpus. The remainder ofthe paper presents three main works related to Seman-tic Web for the Henri Poincaré correspondence. Sec-tion 4 explains how the RDF(S) infrastructure is addedon the Henri Poincaré correspondence website, whichinvolves some translation mechanisms. This makespossible the interrogation of this corpus by SPARQLqueries. The need for more flexible querying is mo-tivated in Section 5, together with a way of handlingthis flexibility. Finally, the prototype of a tool for effi-cient editing of RDF triples for the purpose of index-ing the Henri Poincaré correspondence is described inSection 6.
2. Editing and annotating the correspondence ofHenri Poincaré
From 1999 to 2015, the Henri Poincaré Papers web-site which was created and maintained by Scott Walterto highlight the Henri Poincaré correspondence knewseveral developments. The last version was a web ap-
4The search-engine Solr is installed in the platform.
O. Bruneau et al. / Semantic Web for History of Science 3
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
plication associated with the search engine Sphinx.One could find, when available, the digitized letters,a transcript, critical apparatus and Dublin Core meta-data. This website was harvested by OAI-PMH.5
In 2017, the Archives Henri Poincaré decided to re-forge the website. In order to better structure the siteand to benefit from semantic annotation, this new plat-form has been based on the content management sys-tem (CMS) Omeka S.6 Developed by the Roy Rosen-zweig Center for History and New Media, this CMShas been created for publishing and promoting culturalheritage collections.
Omeka S allows semantic annotations. In thebackoffice, several vocabularies are already avail-able: Dublin Core Terms, Friend of a Friend, andBibliographic Ontology. Adding other vocabularies ispossible, like the Archives Henri Poincaré Ontology.7
A search engine based on the properties is availablebut it is not very user friendly.
All data of the previous website (digitized letters,transcripts, metadata) are available in the platform.
An environment has been created in this CMS inwhich letters can be visualized (as an image or in plaintext) with a critical apparatus essentially coming fromthe printed edition of this correspondence and an in-dexation (Figure 1).
For all letters, there are two types of indexing. Thefirst one is a physical description (type of letter –telegram, autograph letter, minutes, etc. –, number ofpages, location of the letter within the archive, sender,recipient, date and place of expedition, etc.). Somepieces of information are missing. For example, thereare letters for which the exact sending date is unknown,but for which the year and the month are known fromthe context and associated to the letter.
The second type of indexing relates to the contentof the letters; all relevant information can be indexed(see Figure 2 for an example). These data are relevantfrom the viewpoint of historians. For instance, peopleor publication quoted, mathematical theories or formu-lae, philosophical concepts are taken into account.
5OAI-PHM is a protocol developed by the Open Archives Ini-tiative for harvesting metadata description of archives. It is mainlybased on the Dublin Core model. See http://www.openarchives.org/pmh/.
6https://omeka.org/s/.7For now, this ontology is rather basic. It still requires an epis-
temological investigation which will rely on other ontologies indigital humanities such as the CIDOC-CRM ontology (http://www.cidoc-crm.org).
3. Preliminaries on RDFS and SPARQL
This section makes a presentation of RDF, RDFSand SPARQL that is simplified for the needs of this ar-ticle, and exemplified in the domain of the correspon-dence of Henri Poincaré.
The atoms of RDF are resources and literals. A re-source is either anonymous or named. An anonymousresource (aka blank node) is an existentially quantifiedvariable; by convention its identifier starts with a ques-tion mark (e.g., ?x or ?firstName). A named resourceis identified by a name without a question mark; it is aconstant of any type: instance (e.g., henriPoincaré),class (e.g., Mathematician), etc. A property is a re-source denoting a binary relation (e.g., sentBy is aproperty relating a letter to the person who sent it). Aliteral is a constant of a predefined datatype, such asinteger, float or date. The term “value” is used inthis paper for “resource or literal”.
An RDF triple is a triple τ = 〈s p o〉 where sis a resource (the subject of τ), p is a property (thepredicate of τ) and o is either a resource or a lit-eral (the object of τ). For example, the RDF triple〈letter22 sender henriPoincaré〉 states thatletter22 was sent by Henri Poincaré.
An RDF graph represents a set of RDF triples: if〈s p o〉 belongs to an RDF graph G then s and o arenodes of G, and p labels the edge (s, o) of G. The setof named resources of an RDF graph G is denoted byRes(G).
RDFS is a logic whose language is RDF and infer-ence relation ` is defined by a set of inference rules,that are based on some given resources: rdf:type,rdfs:subclassof, rdfs:subpropertyof,rdfs:domain and rdfs:range, respectively abbrevi-ated in a, subc, subp, domain and range. The infer-ence rules considered in this paper are
〈x a C〉 〈C subc D〉〈x a D〉 r1, 〈C subc D〉 〈D subc E〉
〈C subc E〉 r2,〈p subc q〉 〈q subc r〉
〈p subc r〉 r3, 〈x p y〉 〈p subp q〉〈x q y〉 r4,
〈x p y〉 〈p domain D〉〈x a D〉 r5 and 〈x p y〉 〈p range R〉
〈y a R〉 r6.
The (RDFS) deductive closure of an RDF graph G is
G` = {τ : RDF triple | G ` τ}
An RDF graph G can be partitioned intoG = O ∪ D where O is an ontology (containing allthe triples whose predicates are subc, subp, domainor range) and D gathers the data (that are about the
4 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
Baltimore, May 18/851
Dear M. Poincaré:I have received your last letter and in reply may saythat I will take charge of the American Mathematicalwork for the “Répertoire”.I wish you would let me know as soon as you conve-niently can when you wish me to send my first install-ment of the “fiches”, and I will endeavor to do so.2
Let me trouble you again about a matter I mentionedin my last letter: I have not received Nos V+IV of the“Bulletin de la Société Mathématique” for volume 12,if it is not too much trouble for you will you kindly seethat they are forwarded to me.3 I have found a few ty-pographical errors in your memoir which I shall pub-lish in a table of errata in the next number of the Jour-nal. Can you not send me something more for publi-cation in the Journal?4 I will see that it is done bet-ter than the last, which was my fist attempt at read-ing French proof sheets. Please remember me kindlyto Misters Hermite and Picard and believe me to beFaithfully yours
Thomas Craig
(a) A digitized letter. (b) Transcription of the letter (a).
Semantic annotation of the letter〈letterCraig10 sentBy thomasCraig〉〈letterCraig10 sentTo henriPoincaré〉〈letterCraig10 hasDate 1885-05-18〉〈letterCraig10 hasPlaceOfExpedition
baltimore〉〈letterCraig10 hasSubject répertoire〉〈letterCraig10 quoteName charlesHermite〉〈letterCraig10 quoteName émilePicard〉
Semantic annotation of the critical apparatus〈letterCraig10 refersTo letterCraig8〉〈letterCraig10 refersTo letterCraig9〉
1. This letter was written with a letterhead paper ofthe Atheneum club – Franklin & Charles Sts.2. Each referenced article is associated with an in-dex card. When there are enough index cards forthe same index entry, a directory card that containsabout 10 references can be written. See the previousletter.3. See letter 8.4. See letter 9.
(c) Metadata associated with the letter (a) in a RDF syntax. (d) Critical apparatus of the letter (a)translated from French.
Fig. 1. Example of transcription and metadata from a letter.
O. Bruneau et al. / Semantic Web for History of Science 5
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
〈letter3 quote orpheeAuxEnfers〉〈letter2 quoteTextOf albertEinstein〉〈letter4 earliestPossibleWritingDate
1881-02-05〉
Fig. 2. Examples of indexation by RDF triples.
individuals). The RDFS graph Gex of Figure 3 is usedas an example in the remainder of the paper.
SPARQL is a query language for RDF. In this ar-ticle, the SPARQL interrogation is assumed to bemodulo RDFS entailment, meaning that interrogatingan RDF graph G by a SPARQL query Q gives thesame result as interrogating G` with Q. Moreover, theSPARQL queries considered have the following syn-tax: SELECT vars WHERE
{body
}, where vars is a se-
quence of anonymous resources and body is a se-quence of statements separated by “.”, a statement be-ing either an RDF triple written without 〈 and 〉 (i.e.,s p o instead of 〈s p o〉) or a filter statement of theform FILTER (condition), where condition is a Booleanexpression using Boolean operators, equality, and in-equality relations on numbers and dates. Moreover,without loss of generality, it is assumed that only onefilter statement occurs in the body of a SPARQL query.For example, the following is a query for letters sentby Henri Poincaré to a scientist before 1898:
SELECT ?`WHERE
?` sentBy henriPoincaré .?` sentTo ?x .?x a Scientist .?` sendingDate ?dFILTER (?d < 1898-01-01)
More generally, the execution of a SPARQL query
Q on an RDF graph G consists in finding matchings be-tween the body of the query to G and results as a set ofbindings, a binding being an assignment of each vari-able by a resource or a literal, this assignment havingto satisfy the constraint given in the filter statement.The result of this execution, denoted by exec (Q,G),is the set of bindings restricted to the variables givenafter the SELECT keyword. For example:
if Q =
∣∣∣∣ SELECT ?p ?oWHERE
{letter22 ?p ?o
}then exec (Q,Gex)
=
?p ?oa Letter
sentBy henriPoincarésentTo felixKlein
correspondent henriPoincarécorrespondent felixKlein
topic fuchsianFunc
4. Adding an RDFS infrastructure on an Omeka Ssite
The Henri Poincaré website is hosted and managedusing a Huma-Num service. Huma-Num is a Frenchinfrastructure dedicated to Digital Humanities [9]. Asystem, branded “Huma-Num Box”, has been devel-oped to address several issues related to the treatmentof data for Humanities (scalability, volume, accessi-bility, security, etc.). Using such a service enhancesthe development of standards when dealing with Dig-ital Humanities. The Omeka S instance (vocabularies,content, website modules, etc.) has been installed on ashared server.
Omeka S comes with a search engine called Solr,8
that lets users query the database to get informationabout transcribed letters. Although it can be very pow-erful, this search tool is limited in this situation anddoes not take advantage of the Semantic Web tech-nologies. That is why the need to install a SPARQLendpoint has emerged. Omeka S data is stored us-ing a MySQL dedicated database which cannot be di-rectly interrogated through SPARQL queries. A spe-cific RDFS base had to be installed. The Turtle syntaxis a textual RDF syntax [10] which is easy to read andwas chosen for this reason. In addition to the serverused to manage Omeka S installation, a virtual ma-chine has been configured to create and expose thisknowledge database. An automatic script retrieves datafrom Omeka S to update this RDFS base on a dailybasis.9 If an ontology is modified, it will also be up-
8Solr is an open source search platform built on Apache Lucene.9However, although Omeka S allows export to different data for-
mats, Turtle is not part of it. The script exports Omeka S data inJSON-LD format, and then converts it to Turtle before updating theRDFS base.
6 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
Gex = Oex ∪ Dex
Oex = {〈Mathematician subc Scientist〉, 〈Scientist subc Person〉,
〈Person subc Agent〉, 〈Institution subc Agent〉,
〈sentBy subp correspondent〉, 〈sentTo subp correspondent〉,
〈correspondent domain Letter〉, 〈correspondent range Agent〉,
〈topic domain Letter〉, 〈topic range Topic〉}
Meaning that mathematicians are scientists, that scientists are persons, that persons and institutions areagents, that sender and recipient of a letter are correspondents of this letter, that if x has for correspondenty then x is a letter and y is an agent, and that if x has for topic y then x is a letter and y is a topic.
Dex = {〈henriPoincaré a Mathematician〉, 〈felixKlein a Mathematician〉,
〈göstaMittagLeffler a Mathematician〉, 〈letter22 sentBy henriPoincaré〉,
〈letter22 sentTo felixKlein〉, 〈letter22 topic fuchsianFunc〉,
〈letter33 sentBy henriPoincaré〉, 〈letter33 sentTo göstaMittagLeffler〉,
〈letter33 writtenDate 1881-07-26〉, 〈letter33 topic complexAnalysis〉}
Meaning that Henri Poincaré, Felix Klein and Gösta Mittag-Leffler are mathematicians, that letter22is sent by Henri Poincaré to Felix Klein, that a topic of letter22 is Fuchsian functions, that letter33is sent by Henri Poincaré to Gösta Mittag-Leffler, and was written the 26th of July, 1881, and that a topicof letter33 is complex analysis.
G`ex ⊇ {〈Mathematician subc Person〉, 〈henriPoincaré a Scientist〉,
〈felixKlein a Scientist〉, 〈letter22 a Letter〉, 〈fuchsianFunc a Topic〉}
The deduced triples which are given there mean that mathematicians are persons, that Henri Poincaréand Felix Klein are scientists, and that letter22 is a letter and that Fuchsian functions “is” a topic.
Fig. 3. An RDF graph Gex and a part of its deductive closure.
dated. At the time of writing this paper, the RDFS baseis composed of more than 200 000 triples.
On the website, a user can choose between using thebasic input search and creating more complex querieswith SPARQL. Three SPARQL query editing modesare proposed:
Classical mode The user can directly write SPARQLqueries, within a text area, to access the RDFSdatabase It allows creating complex queries bytaking advantage of the expressiveness of theSPARQL language. But this requires a good un-derstanding of SPARQL syntax which is not wellsuited for historians of science and people that arenot familiar with the Semantic Web technologies.
Form-based mode A form containing a set of in-puts is proposed to the user to help him/her build-ing the query. This is a mode suitable for all users,as it does not require any specific knowledge.
Graphical mode A graphical interface is presentedto let the user construct a graph correspondingto an inference graph. This mode can be a goodcompromise because it is not too difficult to ap-prehend, but also keeps a certain expressivenesswhen formulating queries. The interface has beencreated using D3.js library, which is adapted formanipulating documents based on data [11].
It is important to mention that the different blocksshaping the website are invisible for users. They caneasily retrieve data they are interested in by choos-
O. Bruneau et al. / Semantic Web for History of Science 7
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
ing the appropriate search engine. In practice, for thedaily use, historians and experts of the domain tendto use the form-based and graphical modes. Figure 4illustrates the website architecture presented above.For the historians, Omeka S grant access to a spe-cific back interface to visualize and update data (col-lections, vocabularies and content). As the data trans-fer from Omeka S to the RDFS base is automatic, theydo not need to manage this particular task.
Consider a user who wants to express the followinginformal query using SPARQL:
Q =“Give me the letters sent
or received by Paul Appellbetween 1890 and 1905”
He/she can use any of the three modes to express theSPARQL query and to find the corresponding letters.Figure 5 shows the edition of this query in the threemodes.
5. Approximate and explained search in the HenriPoincaré correspondence
To be exploitable, the Henri Poincaré correspon-dence has to be queried. SPARQL querying, with ad-equate user interfaces, serves this purpose. However,more flexible searches can be useful, which is ex-plained in Section 5.1. The way flexible searches aremanaged is based on SPARQL query transformations,as explained in Section 5.2. These transformations arebased on rules, and a tool for expressing and man-aging these rules is briefly presented in Section 5.3.This work is implemented and works well, but can beimproved: some future improvements are described inSection 5.4.
5.1. Motivating approximate and explained searches
Two reasons why flexible search is useful in the con-text of the correspondence of Henri Poincaré are pre-sented below and exemplified.
Taking into account vagueness. Consider the follow-ing informal query:
Q =“Give me the letters sent by Henri
Poincaré to Felix Klein at theend of the 19th century”
(1)
The time period “end of the 19th century” is vague, asmany notions used by human beings. For example, theyears 1876 and 1903 might be considered to belong tothis time period.10 By contrast, there is a large concen-sus on the year interval [1890, 1900] to be a part of thistime period. Therefore, if two letters `1 and `2 haveHenri Poincaré as sender and Felix Klein as recipient,`1 written in 1892 and `2, in 1876, then `1 undoubtedlyis an answer toQ but `2 could be considered also as ananswer to Q.
One way to handle these kinds of informal queries,where boundaries of notions are imprecise, consists inusing the tools of fuzzy set theory: a fuzzy query witha kernel corresponding to the years [1890, 1900] seemsappropriate. However, for the examples presented be-low, fuzzy set theory seems to be inappropriate or, atleast, incomplete.
Finding related results. Now, consider a historian ofscience querying the corpus about the query Q of theletters sent by Felix Klein to Henri Poincaré that areabout complex analysis. Such a query can be easilyformulated in SPARQL:
Q =
SELECT ?`WHERE
?` a Letter .?` sentBy felixKlein .?` sentTo henriPoincaré .?` topic complexAnalysis
(2)
The execution of this query gives a set of letters thatmatch exactly the formulated query Q. Now, imaginethat the historian wants to go further, to get more lettersrelated to the query Q. This may occur in particular inthe following situations:
– The set of results is empty or, at least, too small(for the historian accessing the corpus): the his-torian may desire that the system provides moreletters associated to explanations pointing out themismatch between these letters and the query,something like “This is not a letter about complexanalysis but it is about analysis.”
– The corpus is incomplete: there exist letters sentor received by Henri Poincaré that do not existanymore and also, there probably exist such let-
10Some historians of science state that the end of the 19th cen-tury is the year 1905 during which Albert Einstein published threeof his most important articles, while some historians of geopoliticsconsider that the 20th century starts in 1914.
8 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
Huma-Num Infrastructure
Henri Poincaré website
Omeka S installation
MySQLdatabase
RDFSbase
Server
Vocabu -- laries
Daily update
Basic input search
Website user
Classicalmode
Form-basedmode
Graphicalmode
Solr SPARQL
Content
SPARQLendpoint
Administrator
Virtual machine
SPARQL installation
Fig. 4. The architecture of the Henri Poincaré website.
ters that are not yet discovered. However, theremay be a letter `reply of the corpus sent by HenriPoincaré to Felix Klein that is a reply to a letterexactly matching Q but that is not in the corpus.Therefore `reply should be interesting for the his-torian whose initial query was Q despite the factthat `reply 6∈ exec (Q,GHP).
5.2. SPARQL query transformations
Query transformations can be useful for taking intoaccount vagueness and for finding results that are re-lated to the query but do not match it exactly, as ex-plained in this section.
Consider first the informal query Q of equation (1).Let a and b be two integers with a 6 b. Let Qb
a be thequery for the letters sent by Henri Poincaré to Felix
Klein between year a and year b:
Qba =
SELECT ?`WHERE
?` a Letter .?` sentBy felixKlein .?` sentTo henriPoincaré .?` sentInYear ?yFILTER (?y >= a AND ?y <= b)
Let Lb
a be the set of letters resulting from the exe-cution of the query Qb
a on the RDFS graph GHP of theHenri Poincaré correspondence (Lb
a = {` | (?`, `) ∈exec
(Qb
a,GHP)}). A letter `0 ∈ L1900
1890 is, under astrong concensus, an answer to the informal query Q.A letter `1 ∈ L1900
1885 \ L19001890 can be accepted as an
answer to Q, but it is more debatable. For a letter`1 ∈ L1900
1880 \ L19001885, this is even more debatable. Thus,
O. Bruneau et al. / Semantic Web for History of Science 9
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
(a) Classical mode (SPARQL query syntax).
(b) Form-based mode.
(c) Graphical mode.
Fig. 5. SPARQL query editing modes available on Henri Poincaré website (http://henripoincare.fr/s/correspondance/page/sparql).
10 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
Q19001890
(0)
Q19001885
(1)Q19051890
(1)
Q19001880
(2)Q19051885
(2)Q19051885
(2)Q19101890
(2)
rpast rfuture
rpast rfuture rpast rfuture
Fig. 6. A search tree (truncated at depth 2) for an informal queryrelated to the end of the 19th century (penalties are below the queries,assuming cost(rpast) = cost(rfuture) = 1).
the idea is to generate sets of letters, starting fromL19001890 and enlarging progressively the interval by steps
of 5 years to get results that could be considered asanswers to Q but with less and less relevance.
To implement this idea, the notion of SPARQLquery transformation rules is considered. Let rpast andrfuture be two such rules that are both applicable toa query Qb
a and such that their application to thisquery respectively gives Qb
a−5 and Qb+5a . For example,
Q19001890
rpast−−→ Q19001885. A search tree can be defined us-ing SPARQL queries as states and these two rules togenerate successor states. Figure 6 presents this searchtree. To each rule r is associated a transformation costcost(r) > 0 that is assumed to be additive: this costis used to associate to a query generated by rules apenalty such that if Q has a penalty of π and Q r−→ Q′,then a penalty π′ = π+ cost(r) is associated to Q′.
Thus, the informal queryQ is modeled by an elasticquery
(Q19001885, {rpast, rfuture}
). More generally, an elastic
query is an ordered pair (Q,R) where Q is a SPARQLquery and R is a finite set of SPARQL query transfor-mation rules. The result of an elastic query is a streamof letters ordered by increasing penalty and resultingfrom a search in the tree associated to (Q,R): the statesof this tree are SPARQL queries with penalties (Q, π),the root of this search tree is (Q, 0) a successor of atree node (Q, π) is a node (Q′, π′) such that there isa r ∈ R that can be applied on Q with Q r−→ Q′ andπ′ = π+ cost(r).
For the example of the query Q of equation (2), theelastic query (Q,R) can be considered, with R a set ofquery transformation rules such as the ones defined in-formally below:
(rGenPred) Substitution of the predicate p of a tripleby a superproperty q of p (i.e.,O ` 〈p subp q〉).
(rGenObjInst) Substitution of the object o of a triple〈s p o〉 when it is an instance of a class C bya variable instance of this class (i.e., replace thistriple by the triples 〈s p ?x〉 and 〈?x a C〉).
(rexchangeSenderRecipient) If the body of a query Qhas two triples of the form 〈s sentBy o1〉 and〈s sentTo o2〉 then this rule can be applied onit and its application consists in replacing thesetriples with 〈s sentTo o1〉 and 〈s sentBy o2〉.
(rsubstByColleague) If the body of a query Q has atriple 〈s p o〉 such that O ` 〈o worksWith c〉then the rule substitutes this triple by 〈s p c〉.E.g., this transforms a query from Felix Klein bya query from a colleague of this mathematician.
It is noteworthy that the first two rules are generaliza-tion rules that can be applied to other domains.11 Sev-eral such rules can be defined (generalization of classesin subject or object, removal of a triple, etc.) The twoother rules are not generalization rules and are rule de-fined for the application domain of a correspondencecorpus.
5.3. SQTRL
SQTRL (SPARQL Query Transformation Rule Lan-guage) is a language associated with a tool designedfor handling SPARQL query transformations. This toolhas been used in various application contexts [12].
An SQTRL transformation rule r is characterized bythe following fields:
– name(r): an identifier of the rule by a string;– context(r): a set of RDFS triples;– left(r): a set of RDFS triples;– right(r): a set of RDFS triples;– cost(r): a positive float;– explanation(r): a text describing the transfor-
mation that may contain blank nodes occurring inthe fields context, left and right.
The RDFS triples of such a rule appear in SPARQLsyntax.
Given an RDFS graph G, a SPARQL query Q, andan SQTRL rule r, r is applicable on Q (given G)if context(r) can be bound with the graph G andleft(r) can be bound with the body of Q, providedthat these bindings are consistent: if ?x occurs in bothcontext(r) and left(r), only the values x such that
11A generalization rule is a rule r such that if Q r−→ Q′ then, forany RDFS graph G, exec(Q,G) ⊆ exec(Q′,G).
O. Bruneau et al. / Semantic Web for History of Science 11
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
(?x, x) appears in both bindings are kept. If so, the ap-plication of r gives queries Q′ such that left(r) is sub-stituted by right(r), where blank nodes are substi-tuted by their values in the bindings.
For example, consider first the query transformationrule rGenPred, presented in Section 5.2 and correspond-ing to the generalization of a predicate p by a super-property q of p. This rule can be described as follows(r = rGenPred):
name(r) = "Generalize a property "+ "in predicate position"
context(r) = ?p subp ?qleft(r) = ?s ?p ?oright(r) = ?s ?q ?ocost(r) = 1.0explanation(r) = "Generalize ?p in ?q"
The rule rGenPred is a generalization rule that can beapplied to many application contexts. As pointed outin Section 5.2, it is possible to add domain-dependentrules. For example, the rule r = rexchangeSenderRecipientcan be represented as follows:
name(r) = "Exchange sender and recipient"context(r) = (empty context)left(r) = ?s sentBy ?o1 . ?s sentTo ?o2right(r) = ?s sentTo ?o1 . ?s sentBy ?o2cost(r) = 1.0explanation(r) = "Exchange sender ?o1 "
+ "and recipient ?o2"
An XML syntax has been chosen to properly de-fine a transformation rule. As an example, Figure 7illustrates the XML syntax associated with the rulerGenObjInst corresponding to the generalization of anobject instance.
The SQTRL tool internally reuses the Corese en-gine [13]. Such an engine enables to query SemanticWeb data, stored as RDF(S) files.
5.4. Future work on approximate and explainedsearch
The current version of SQTRL has some limitationsthat require to be overcome. This section lists some ofthem and points out future studies for addressing them.
The first limitation is related to the costs associatedto a rule: such a cost is a constant, whereas it is some-times more relevant to have costs that depend on thebindings of anonymous resources occurring in the rule(in the fields context, left and right) at rule ap-plication time. For instance, the cost of generalizing a
class C into a class D may depend on the “generaliza-tion leap” from C to D.
The second limitation is linked with the unnecessaryapplications of some rules given the rules already ap-plied in the same branch of the search tree. For exam-ple, it is not necessary to apply sequentially twice therule rexchangeSenderRecipient on a query Q, since it leadsback to Q. Another example is linked with the rulesrpast and rfuture: it is unnecessary to apply both in thesame branch since the set of results they add are al-ready generated by queries with a lower or equal costin the search tree. Therefore, in Figure 6, the two nodeslabelled by Q19051885 are both useless (the letters answer-ing this query are in the union of the answers to queriesQ19001885 and Q19051890 that are generated at a higher level inthe tree). The objective of a future work is thus to avoidsuch unnecessary composition of rules, which wouldhave a positive impact both on the computing time and(more importantly) on the user load, that would haveto examine fewer generated results appearing severaltimes with equivalent (but not necessarily equal) ex-planations.
The third limitation is related to the filter statementsof the SPARQL queries. Currently, only filter state-ments representing intervals (e.g., rpast and rfuture) arehandled by SQTRL rules. More complex filter state-ments (involving, e.g., negations or disjunctions) arenot considered currently. Taking them into account is acomplex research direction, since complex filter state-ments are propositionally closed which involves thata purely syntactic handling is not sufficient if querytransformation rules are considered at a semanticallevel (i.e., if the execution of two queries Q and Q′ givethe same results on any RDFS graph then a SQTRLrule should be applicable in the same manner on Q orQ′).
The fourth limitation is more domain-dependentsince it is related to the notion of time in history. Forexample, the rules rpast and rfuture enlarge time intervalsby steps of 5 years. By contrast, a historian often con-siders the time scale according to milestones (e.g., theyear 1914 for political history and the year 1905 forhistory of physics). Therefore, using such milestonesin time interval transformation rules, as well as theirsignificance in the context of the search, is a challeng-ing future work. For this purpose, ontologies for his-tory12 should be useful.
12In particular data for history, http://ontome.dataforhistory.org/.
12 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
<rule name="Generalize an instance in object position by any instance of one of its classes"><context>?o a ?C</context><left>?s ?p ?o</left><right>?s ?p ?x . ?x a ?C</right><cost>1.0</cost><explanation>Generalize ?o in any instance of ?C </explanation>
</rule>
Fig. 7. An example of SQTRL rule in XML syntax.
6. A tool for efficient editing indexing triples
Currently, the Henri Poincaré correspondence is in-dexed in a satisfying way by RDFS files. However,new properties may emerge from research in historythat would require additional annotation work and,more importantly, new correspondence corpora existthat are not yet annotated and that are of interest fornearby colleagues in history of science. This justifiesthe development of an editing tool of RDF triples forthe purpose of corpus annotation. The RDF graph Gis partitioned in an ontology O that is supposed to begiven and in a set of triples D that has to be edited bythis tool.
The development of this tool is an ongoing workthat is described below in four parts: the edition tool,the use of deductive inferences in RDFS for improv-ing the efficiency of this tool, the use of hypotheti-cal inferences on RDFS for the same purpose usingcase-based reasoning principles, and a first evaluationof this work. Finally, some research directions on thisissue are presented.
6.1. RDFWebEditor4Humanities: an editor forindexing corpora
The indexing work done by historians of science forthe Henri Poincaré correspondence was mainly doneusing the user interface of Omeka S: the RDF filesare generated by translating the information edited viaOmeka S (in a SQL server) to RDF, as described be-fore, in Figure 4. It was decided to implement an RDFtriple editor prototype for the new annotation tool inorder to benefit from the RDF(S) infrastructure.
Figure 8 presents a screenshot of the developed in-terface. An autocomplete mechanism has been imple-mented which assists users in writing triples values.Applying such a filter is particularly relevant whenworking with a base containing a large number oftriples. The set of potential values for a field are rankedaccording to the alphabetical order.
The editor also displays the context of the edition.For the edition of a correspondence, the context is aletter and the already edited triples about this letter.If the current letter is letter44, the context isthe set of triples 〈letter44 p o〉 where p and oresult from the execution of the querySELECT ?p ?o WHERE
{letter44 ?p ?o
}.
When editing a state for which only one of the threevalues is missing, the tool avoids giving to the usera value that forms a triple already known. For exam-ple, when the subject s and the predicate p are al-ready edited, the values o resulting from the querySELECT ?o WHERE
{s p ?o
}are not proposed.
Figure 8 example displays the context of the letterwhich is currently being edited. This letter is identifiedas item no9866 of the Henri Poincaré database and itscontext contains four triples.
6.2. Indexing support using RDFS deduction
Consider an expert indexing the Henri Poincaré cor-respondence: D is the set of triples already editedat current time. At a given time, an editing ques-tion is raised, that is composed of (1) a triple with1, 2 or 3 missing values, denoted by anonymous re-sources, and (2) a mark on the field that the edi-tor wants to fill, denoted by framing the highlightedanonymous resource. For example, the editing ques-tion 〈letter44 ?p ?o〉 corresponds to the statewhen the editor has already edited the subject of thetriple but neither its predicate nor its object, and wantsto edit now its predicate. The editing assistance pro-posed here aims essentially at re-ranking the set of po-tential values for the highlighted field, the objective be-ing that the rank of the value chosen by the expert is,on the average, lower than the rank given by the alpha-betical order. For this purpose, some features about themissing values are deduced from G = O ∪ D and themore features a potential value has, the higher its rankin the list will be. In the following, given a value c thatis in the set of the candidate values for the highlightedfield, c.#f denotes the number of features of c.
O. Bruneau et al. / Semantic Web for History of Science 13
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
Fig. 8. A screenshot of RDFWebEditor4Humanities.
Fig. 9. A screenshot of RDFWebEditor4Humanities using RDFS deduction to rank the set of potential values.
14 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
For example, consider the editing question〈letter44 sentTo ?o 〉 and G = Gex from Figure 3.Once ?o is replaced by a value o, it can be deducedthat o is an instance of Agent:
G ∪ {〈letter44 sentTo o〉} ` 〈o a Agent〉
The set of candidate values for ?o is Res(G). For sucha candidate value c, two situations may occur:
S+ Either G ` 〈c a Agent〉;S? Or G 6` 〈c a Agent〉: it cannot be deduced fromG that c is an instance of Agent.
Let C+ (resp., C?) bet the set of c ∈ Res(G) in situationS+ (resp., S?). If c1 ∈ C+ and c2 ∈ C?, all other thingsbeing equal, then c1.#f = c2.#f+ 1.
This editing question is of the type 〈s p ?o 〉.There are 12 editing question types: cf. first columnin Figure 10. For an editing question of the type〈s p ?o 〉, the knowledge about the ranges of p can beused (there may be several ranges for a property, eachof them providing an additional constraint on the valuefor ?o). Let R be such a range (i.e., G ` 〈p range R〉).If R is a datatype then the value for ?o has to be a lit-eral of this datatype. Otherwise, the value for ?o is ei-ther new or has to be selected in Res(G). Therefore,for each c ∈ Res(G), if G ∪ {〈s p c〉} ` 〈c a R〉then c.#f is incremented. This can be computed byexecuting the query SELECT ?c WHERE
{?c a R
}on
G: if (?c, c) belongs to the result, c.#f is incremented.This is called rangePred in Figure 10. For editingquestions of the type 〈?s p o〉, the idea is similar,except that domain is used instead of range (calleddomainPred).
Consider an editing question of the type 〈s ?p o〉.First, the value to be edited for ?p has to be a prop-erty, so a candidate value that is a property (writtenG ` 〈c a rdf:Property〉) has its feature countf.#f incremented (this is called predProperty inFigure 10). The value of s can also be used: if s isan instance of a class D then a candidate value c hav-ing D as domain (i.e., G ` 〈c domain D〉) has itsc.#f incremented (called subjectInDomain). Thiscan be computed thanks to the execution of the querySELECT ?c WHERE
{s a ?D . ?c domain ?D
}on
G. In a symmetrical way, the value of o can beused (with range instead of domain): this is calledobjectInRange in Figure 10.
For the editing questions of the type 〈 ?s ?p o〉,the feature count c.#f for the subject is increasedeach time c is in the domain of a property whose
range contains o, which corresponds to the query
SELECT ?cWHERE
?c a ?D .?p domain ?D .?p range ?R .o a ?R
. For the editing
questions of the type 〈s ?p ?o 〉, a similar ideaapplies (with exchange of domain and range). Forboth types of editing questions, this is denoted bysubjImRel(subject-image relationship) in Figure 10.
For editing questions of the type 〈s ?p ?o〉 (resp.,
〈?s ?p o〉), the edition of a value for ?p is similar asfor 〈s ?p o〉, except that range (resp., domain) is notused. For 〈?s ?p ?o〉, this also applies, but withoutthe use of domain and range (the fact that the valueof ?p should be a property is used).
For the other possibilities no re-ranking is providedin the current version of the tool.
Figure 9 illustrates the use of RDFS deduction inthe edition tool. The editing question is the sameas the one presented in Figure 8. This is a ques-tion of the type 〈s ?p o〉, where s is a Letter(letter9866) and o is a Person (thomasCraig).Imagine that the user wants to define thomasCraigas the sender of the letter9866. The question is〈letter9866 ?p thomasCraig〉. The graph con-tains the triples:
〈sentBy domain Letter〉,〈sentTo domain Letter〉,〈sentBy range Person〉,〈sentTo range Person〉,〈letter9866 a Letter〉, and〈thomasCraig a Person〉
As the class Letter is domain of the propertiessentBy and sentTo, the counts associated with thetwo properties are incremented. Moreover, the classPerson is range of the two properties which also in-creases the counts by one. The property sentBy is nowthe first proposition among the values proposed by thetool while it was in third position when using the basiceditor. This version of the tool provides a better rank-ing in this situation. It is important to point out thatapplying RDFS deduction does not add any sensitivedelay to the use of the tool.
6.3. Indexing support using case-based reasoning
The work presented in this section is freely inspiredfrom the research on the UTILIS system that assists
O. Bruneau et al. / Semantic Web for History of Science 15
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
editingquestion type
domain-Pred
range-Pred
pred-Property
subject-InDomain
object-InRange
subjIm-Rel
〈 ?s p o〉 ×〈s ?p o〉 × × ×〈s p ?o 〉 ×〈 ?s ?p o〉 ×〈?s ?p o〉 × ×〈 ?s p ?o〉 ×〈?s p ?o 〉 ×〈s ?p ?o〉 ×〈s ?p ?o 〉 ×〈 ?s ?p ?o〉〈?s ?p ?o〉 ×〈?s ?p ?o 〉
Fig. 10. Editing question types and associated features of candidate values.
users in RDFS graph updating [14] and on the case-based reasoning methodology.
Case-based reasoning (CBR [15]) aims at solvingproblems with the help of a case base, where a caseis a representation of a problem-solving episode. Thetarget problem is the problem currently under resolu-tion. A source case is an element of the case base.The reasoning process is usually composed of severalsteps: (1) retrieval chooses one or several source casesjudged similar to the target problem, (2) adaptationmodifies, if necessary, the retrieved case, (3) storageadds the newly formed case, possibly after a correc-tion from the user. In this ongoing work, only retrievalis considered (adaptation is currently a mere copy andstorage is quite straightforward).
In this application framework, a problem is an edit-ing problem, defined by an editing question (as in theprevious section), and the context of the problem (asdefined in Section 6.1). For example, consider the fol-lowing target problem for the running example devel-oped below:
tgt =
question: 〈letter44 topic ?o 〉
ctxt: 〈letter44 sentBy felixKlein〉,〈letter44 sentTo henriPoincaré〉
A solution is a plausible value for the highlightedfield in the editing question of the problem. The set ofedited data, D, is used as a case base. It can be noticedthat D is not a set of problem-solution pairs, but ap-
pears as a whole in which such pairs can be “cut out”.Such situations occur for other CBR systems, as de-picted already in [15].
The retrieval step aims at extracting cases from D.This gives candidate solutions that are proposed forthe edition and ranked according to the similarity tothe target problem (the more similar the cases are tothe target problem, the higher in the list they are pro-posed). For example, considerD = Dex from Figure 3.Both letter instances letter22 and letter33 havetopics associated to them:
Gex ` 〈letter22 topic fuchsianFunc〉
Gex ` 〈letter33 topic complexAnalysis〉
Both topics can be proposed as candidate values, butwhich one should be proposed before the other? Theanswer is based on the similarity between the lettersof D and the letter of the target problem: if letter22is more similar to letter44 than letter33 then thevalue fuchsianFunc is proposed before the valuecomplexAnalysis.
So, the question is how to assess the similarity be-tween a letter and the target problem. The answer pro-posed for the UTILIS system cited above is basedon query relaxation. For RDFWebEditor4Humanities,this idea is reused, and query relaxationis replaced with SQTRL query transformation(cf. Section 5). More precisely, the target problem tgtis transformed into a SPARQL queryQtgt = SELECT var WHERE
{body
}, where var is the
variable highlighted in the editing question (var = ?o
16 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
in the running example) and body is constituted bythe triple of the editing question and the triples ofthe context, with the substitution of the current let-ter (letter44 in the example) by a variable ?`. Thisgives, for the running example:
Qtgt =
SELECT ?oWHERE
?` topic ?o .?` sentBy felixKlein .?` sentTo henriPoincaré
Then retrieval amounts to an approximate search, as
described in Section 5, taking Qtgt as an initial queryand a set of SQTRL rules. More precisely, the searchtree of root Qtgt is developed with a given maximaltransformation cost and then, for each Q of this treeassociated with a transformation cost tc, the executionof Q on the edited graph gives a (possibly empty) set ofvalues v (in the example, values for ?o). These valuesv are then ranked according to the increasing value ofthe transformation cost tc, which provides the list ofpotential values for the expert that indexes the letters.
With the running example, consider only the SQTRLrules rGenPred and rGenObjInst, defined in Section 5.2,with a cost of 1 for both. In the search tree of root Qtgt,the query Q1 is generated at depth 2 (corresponding to2 applications of rGenPred on Qtgt):
Q1 =
SELECT ?oWHERE
?` topic ?o .?` correspondent felixKlein .?` correspondent henriPoincaré
The execution of Q1 on Dex gives
{(?o, fuchsianFunc)}. At depth 3, the query Q2 isgenerated (corresponding to 1 application ofrGenObjInst on Q1):
Q2 =
SELECT ?oWHERE
?` topic ?o .?` correspondent ?x .?x a Mathematician .?` correspondent henriPoincaré
The execution of Q2 on Dex gives
{(?o, fuchsianFunc), (?o, complexAnalysis)}.Finally, the transformation cost for obtaining
fuchsianFunc is tc = min{2, 3} = 2 and the one forcomplexAnalysis is tc = 3: the former is proposed
before the latter in the list proposed during the editingprocess.
This work and its combination with the work of Sec-tion 6.2 are still in their early stages, so it is not evalu-ated yet and thus, not considered in the next section.
6.4. A first evaluation
A first evaluation has been carried out that is fullyautomated, though it is acknowledged that a completeevaluation of an editing tool has to involve human edi-tors.
For this evaluation, the baseline is the editor tool us-ing only alphabetical order to rank the values (cf. Sec-tion 6.1) and it is compared to the editing prototype us-ing RDFS entailment (cf. Section 6.2). The autocom-plete mechanism has been disabled for the automaticevaluation.
Given an editing question eq, the measure used inthis evaluation is the rank rank(eq) of the value cho-sen by the editor in the list of the values proposed bythe tool: the lower rank(eq) is, the better the tool is.
The test set is built on the basis of the currentRDF graph GHP = OHP ∪ DHP that indexes the HenriPoincaré correspondence. More precisely, the ontol-ogyOHP (that imports other ontologies, such as FOAF)is given and the edition of the data in DHP is simulatedas follows:
– Initially, D = ∅, Ranks is the empty multiset.– For each triple 〈s p o〉 ∈ DHP considered in a
random order,
• For each of the 3 fields subject, predicate andobject, considered in a random order,
∗ Let eq be the editing question correspondingto this field;
∗ Let rank(eq) be the rank of the edited value(i.e., s, p or o) in the list of the proposed val-ues given by the evaluated editing tool usingG = OHP ∪ D;
∗ Add rank(eq) to Ranks;
• Add 〈s p o〉 to D.
At the end D = DHP and Ranks is the multiset ofthe ranks. The average and standard deviation of theelements of Ranks are computed.
The results are presented in Figure 11.Although the evaluation has been carried out on a
subset of the Henri Poincaré Correspondence, this wasconstructed in order to be representative of the whole
O. Bruneau et al. / Semantic Web for History of Science 17
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
baseline using deduction(cf. §6.1) (cf. §6.2)
average 12.5 9.3standard deviation 11.4 8.1
Fig. 11. The average and the standard deviation of the val-ues rank(eq), for editing questions eq simulated from the HenriPoincaré correspondence RDF graph, for two versions of the editor.
correspondence. What emerges of these results is thatthe use of RDFS deduction brings a better generalranking and thus could effectively assist historians dur-ing the indexing work.
6.5. Future work on assisted indexing
It is noteworthy that the tool presented here is still aprototype needing some technical and ergonomic im-provements that are necessary before carrying out thecomplete evaluation involving human users.
The CBR approach presented in Section 6.3 is notfully implemented (and not evaluated) and this consti-tutes the first future work.
The combination of the deductive and CBR ap-proaches for editing also has to be investigated. Inparticular, the following hypothesis is made: the edit-ing improvements of these methods are largely inde-pendent one from the other, so a well-designed com-bination of them should give interesting results. Onepromising way to do such a combination is to applyone of these methods and then, to decide between theties using the other method. More sophisticated com-bination techniques can also be investigated.
Another future work is related to the deductive ap-proach and can be illustrated by the example of theediting question 〈letter44 ?p ?o〉 introduced inSection 6.2. Two types of situations were distinguishedfor a candidate value c for ?o: S+ and S?. A third typeof situations can be considered, provided that the rep-resentation language is extended to class complement(as in OWL DL and many of its fragments):
S− G ` 〈c a ¬Agent〉.
where ¬Agent represents the entities that are notagents, G is the graph in its current state and c is a can-didate value for ?o. For example, if G entails that theclasses Letter and Agent are disjoint, then any c suchthat G ` 〈c a Letter〉 is in situation S−. For sucha c, it is certain that it is not a potential value for ?oand then it can be removed from the list of candidatevalues proposed to the expert. More generally using amore expressive logic than RDFS that contains some
sort of negation would enable to remove elements fromthe list of candidates. The drawback of this extensionis the computation time involved by using this moreexpressive logic, which is a particularly sensitive issuewhen dealing with a user interface. Thus, an option tobe investigated would be to make offline deductions inthis more expressive than RDFS logic and to use thesededuced data online in order to point out elements thatcannot be correct values for a given editing question.
A last potential future work would be to integratethis work in an existing RDF editing tool such as Ne-ologism [16], Protégé [17] and Vitro [18].
7. Conclusion and Future Work
The Henri Poincaré correspondence is a corpusgathering letters received and sent by this famous sci-entist. This article has presented the application ofSemantic Web technologies on this corpus. Beforethese technologies were used on this corpus, letterswere digitized, transcribed in plain text and associatedto metadata, within the content management systemOmeka S.
Then, a translation script of these pieces of infor-mation into RDFS has been implemented, which hasmade possible the access to the corpus via an RDFendpoint using the vocabularies of standard ontologiesand also the ontology developed for the purpose of thiscorpus. Therefore, the SPARQL querying became pos-sible, and three user interfaces were developed for thispurpose: a classical interface with the whole SPARQLquerying, a form-based interface more suited to thehabits of the users and an interface using a graphicalview.
Now, SPARQL querying is sometimes insufficient,when approximate searches are needed. This is thecase when vague notions have to be taken into accountin the query (e.g., “the end of the 19th century”) orwhen results that are somehow related to the initialquery can be of interest for the user. For this purpose,a mechanism based on the notion of elastic query hasbeen designed. The result of the execution of an elas-tic query is a stream ordered by increasing cost, eachresult being associated with explanations of the mis-match between the SPARQL query and this result.
Currently, the indexing of the corpus is done viaOmeka S. It is planned to do it directly in RDFS and anediting tool is currently under study. A prototype hasbeen developed in which the user edits each element ofa triple by selecting a resource in a list (or creating a
18 O. Bruneau et al. / Semantic Web for History of Science
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
new resource or a literal). This prototype has been im-proved by the use of RDFS entailment in order to pro-pose most promising values first. Another (potential)improvement of this prototype using case-based rea-soning principles has been presented and is an ongoingwork.
These contributions have different degrees of matu-rity and have been presented from the more mature one(that is currently in use) to the less mature one (that isan ongoing work). Future works have been planned forapproximate and explained search and for assisted in-dexing of triples (cf. Sections 5.4 and 6.5) and are notrecalled here. Therefore, the first ongoing work con-sists in putting all these contributions in practice so thatthey are all as routine uses.
Another future work will aim at dealing with uncer-tainty and imprecision of some information about theletters, and, more specifically, about the dates. Whenthe date is written in the preamble of a letter, it is rea-sonable to consider it as its writing date, but in manyletters this information is missing. However, an impre-cise date can be given, e.g., because it relates to anevent (a historical event or a relation to another let-ter that is well-dated). In such situations, the precisedate is often difficult to know, thus an imprecise date(e.g., giving only the month and the year) is associ-ated to the letter. Sometimes, a precise yet hypothet-ical date can be inferred by historians, e.g., when theletter refers to something that is interpreted by a his-torical event. Dealing with imprecise and/or uncertaindates is a challenging issue that requires both a carefulontology modeling and inference mechanisms.
In this paper, the representation formalism for theontologies and for the data does not go beyond RDFS.However, in order to carry out some of the future stud-ies described in this conclusion as well as in other sec-tions of the paper, the use of larger fragments of OWLis likely to be required. For instance, in Section 6.5, itwas shown how the use of negation can improve theeditor using entailment. It was also noted in this samesection that the issue of computing time increasing po-tentially involved by a more expressive formalism canbe harmful to the practical use of such a system. Someoffline deductions may be used to alleviate the onlinecomputing burden.
Although this work has been carried out for theHenri Poincaré correspondence, it is planned to be ap-plied to other correspondences of known scientists.More widely, it should be reusable for another historyof science corpora.
Acknowledgements
This work was supported partly by the french PIAproject “Lorraine Université d’Excellence”, referenceANR-15-IDEX-04-LUE. It was also supported by theCPER LCHN (Contrat de Plan État-Région Lorrain“Langues, Connaissances et Humanités Numériques”)that financed engineer Ismaël Bada who participated tothis project.
References
[1] H. Poincaré, La Science et l’Hypothèse, Ernest Flammarion,Paris, 1902.
[2] P. Nabonnand (ed.), La correspondance entre Henri Poincaréet Gösta Mittag-Leffler, Birkhäuser, Basel, 1998.
[3] S. Walter, E. Bolmont and A. Coré (eds), La correspondanceentre Henri Poincaré et les physiciens, chimistes et ingénieurs,Birkhäuser, Basel, 2007.
[4] S. Walter, R. Krömer and M. Schiavon (eds), La corre-spondance entre Henri Poincaré avec les astronomes et lesgéodésiens, Birkhäuser, Basel, 2014.
[5] L. Rollet (ed.), La correspondance de jeunesse d’HenriPoincaré: Les années de formation. De l’École polytechniqueà l’École des Mines (1873-1878), Publications of the HenriPoincaré Archives, Springer International Publishing, Basel,2017. ISBN 9783319559599.
[6] P. Wittek and W. Ravenek, Supporting the Exploration of aCorpus of 17th-Century Scholarly Correspondences by TopicModeling, in: Supporting Digital Humanities 2011: Answer-ing the unaskable, B. Maegaard, ed., Copenhagen, Denmark,2011.
[7] A. Meroño-Peñuela, A. Ashkpour, M. van Erp, K. Mandemak-ers, L. Breure, A. Scharnhorst, S. Schlobach and F. van Harme-len, Semantic technologies for historical research: A survey,Semantic Web Journal (2015), 1–27. doi:10.3233/SW-140158.http://iospress.metapress.com/content/A842V11135QK5055.
[8] O. Bruneau, S. Garlatti, M. Guedj, S. Laubé and J. Lieber,SemanticHPST: Applying Semantic Web Principles and Tech-nologies to the History and Philosophy of Science and Tech-nology, in: The Semantic Web: ESWC 2015 Satellite Events,F. Gandon, C. Guéret, S. Villata, J. Breslin, C. Faron-Zuckerand A. Zimmermann, eds, Lecture Notes in Computer Science,Vol. 9341, Springer International Publishing, 2015, pp. 416–427.
[9] N. Larrousse and J. Marchand, A Techno-Human Mesh forHumanities in France: Dealing with preservation complex-ity, in: DH 2019, Utrecht, Netherlands, 2019. https://hal.archives-ouvertes.fr/hal-02153016.
[10] G. Carothers and E. Prud’hommeaux, RDF 1.1 Turtle, 2014.http://www.w3.org/TR/2014/REC-turtle-20140225/.
[11] M. Bostock, D3.js - Data-Driven Documents, 2012. http://d3js.org/.
[12] O. Bruneau, E. Gaillard, N. Lasolle, J. Lieber, E. Nauer andJ. Reynaud, A SPARQL Query Transformation Rule Language— Application to Retrieval and Adaptation in Case-Based Rea-soning, in: Case-Based Reasoning Research and Development.
O. Bruneau et al. / Semantic Web for History of Science 19
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
ICCBR 2017, D. Aha and J. Lieber, eds, Lecture Notes in Com-puter Science, Springer, 2017, pp. 76–91.
[13] O. Corby, R. Dieng-Kuntz and C. Faron Zucker, Queryingthe Semantic Web with Corese Search Engine, in: EuropeanConference on Artificial Intelligence, Valence, Spain, 2004.https://hal.inria.fr/hal-01531219.
[14] A. Hermann, S. Ferré and M. Ducassé, An interactive guid-ance process supporting consistent updates of RDFS graphs,in: International Conference on Knowledge Engineering andKnowledge Management, Springer, 2012, pp. 185–199.
[15] C.K. Riesbeck and R.C. Schank, Inside Case-Based Reason-ing, Lawrence Erlbaum Associates, Inc., Hillsdale, New Jersey,
1989.
[16] C. Basca, S. Corlosquet, R. Cyganiak, S. Fernández and
T. Schandl, Neologism: Easy vocabulary publishing (2008).
[17] W.E. Grosso, H. Eriksson, R.W. Fergerson, J.H. Gennari,
S.W. Tu and M.A. Musen, Knowledge modeling at the millen-
nium (the design and evolution of Protégé-2000) (1999).
[18] B. Lowe, B. Caruso, N. Cappadona, M. Worthington,
S. Mitchell and J. Corson-Rikert, The Vitro Integrated Ontol-
ogy Editor and Semantic Web Application., in: ICBO, 2011.