Predicate constraints of general-purpose knowledge bases (KBs) like Wikidata, DBpedia and Freebase are often limited to subproperty, domain and range constraints. In this demo we showcase CounQER, a system that illustrates the alignment of counting predicates, like staffSize, and enumerating predicates, like workInstitution^{-1} . In the demonstration session, attendees can inspect these alignments, and will learn about the importance of these alignments for KB question answering and curation. CounQER is available at https://counqer.mpi-inf.mpg.de/spo

Ghosh, S.

Razniewski, S.

Weikum, G.

English

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CounQER: A System for Discovering andLinking Count Information in Knowledge BasesShrestha Ghosh1 , Simon Razniewski1, and Gerhard Weikum1Max Planck Institute for Informatics, Saarland Informatics Campus, 66123Saarbruecken, Germany{ghoshs, srazniew, weikum}@mpi-inf.mpg.deAbstract. Predicate constraints of general-purpose knowledge bases (KBs)like Wikidata, DBpedia and Freebase are often limited to subproperty,domain and range constraints. In this demo we showcase CounQER, asystem that illustrates the alignment of counting predicates, like staffSize,and enumerating predicates, like workInstitution−1. In the demonstra-tion session, attendees can inspect these alignments, and will learn aboutthe importance of these alignments for KB question answering and cu-ration. CounQER is available at https://counqer.mpi-inf.mpg.de/spo.Keywords: Knowledge bases · semantics · count information.1 IntroductionMotivation and problem. Detecting inter-predicate relations in KnowledgeBases (KBs) beyond inheritance can lead to a better semantic understandingthat can be leveraged for important tasks such as KB curation and questionanswering (QA). In this work we focus on set predicates and their alignment. Setpredicates describe the relation between an entity and a set of entities throughtwo variants - i) counting predicates which relate an entity to a count (of a set ofother entities) and, ii) enumerating predicates which relate an entity to multipleentities.Consider a list of counting predicates, {numberOfChildren, staffSize},which take only integer count values as objects and a list of enumerating pred-icates, {child, employer−1, workInstitution−1}, which take only entity val-ues. Identifying set predicates pairs across the two variants, aligned by theirsemantic relatedness, such as {numberOfChildren ↔ child}, {staffSize ↔employer−1}, {staffSize ↔ workInstitution−1}, has two major benefits.1. KB curation - We can discover incompleteness and/or inconsistencies in KBsthrough alignments [7,9]. For instance, if the value of numberOfChildren ex-ceeds the count of child entities of a subject, then the child statements forthat subject may be incomplete. Alternately, if the value of numberOfChildrenis less than the count of child entities, there may be inconsistent enumera-tions. An empty instantiation is also an indication of incompleteness.arXiv:2005.03529v1  [cs.IR]  7 May 20202 S. Ghosh et al.Fig. 1: The interface for SPO queries showing results on an example query.2. QA enhancement - Set predicate alignments can aid in KB query result de-bugging and enrichment [1,2]. Even in an event of empty result, for instance,when an entity has no numberOfChildren predicate instantiations, but, haschild predicate instances, we can enumerate the object entities of childinstead.Set predicate alignments highlight the variation in predicate usage for thesame concept. For instance, the staffSize of an entity has related resultson employees through employer−1 as well as workInstitution−1.Approach. CounQER (short for “Counting Quantifiers and Entity-valued PR-edicates”) uses a two-step approach. First it identifies the counting and enu-merating predicates with supervised classification and then aligns set predicatepairs, one from each variant, according to ranking methods and statistical andlexical metrics. For further details refer to [3]. The classification and alignmentsteps are executed offline. We use the obtained results in our demonstrator forcount-related SPO queries on three KBs1 - Wikidata-truthy and two variants ofDBpedia based on mapped and raw extractions.Fig. 1 shows the interface with results on an example query on the DBpedia-raw KB. The query is on the events where the entity, Leander Paes, wins gold(dbp: gold−1). The main result (set predicate in blue) is succeeded by relatedresults on ranked and aligned set predicates (in orange). Enumerations expandon hovering and we show up to 1000 enumerations. A user can check the actualquery fired for each row by following the link to the SPARQL query endpoint.Also, KB-specific predicate statistics show up while hovering over the predicate1 https://tinyurl.com/wikidata-truthy, https://tinyurl.com/dbpedia-mappings,https://tinyurl.com/dbpedia-rawCounQER: A System for Discovering and Linking Count Information in KBs 3Fig. 2: Interface for viewing KB-specific alignments.buttons. On clicking a set predicate from the related results, a new query is firedon the same subject and the new clicked predicate.The complete ranked list of set predicate alignments for the three KBs as wellas Freebase can be viewed as in Fig. 2. Here too, we provide links to the SPARQLendpoint showing the subjects that have populated facts for the alignments.Related Work. Schema and ontology alignment is a classic problem in dataintegration, which in the semantic web is tackled by focusing on the dynamics ofentity relations across ontologies [8]. Subset and equivalence relation alignment isone of the popular approaches to ontology alignment [4]. The problem of aligningenumerations with counts, which we address is atypical since most approachesdo not target completeness and correctness of KBs [6]. Even though between5% to 10% of questions in popular TREC QA datasets deal with counts [5], QAsystems like AQQU [1] and QAnswer [2] only perform ad-hoc count aggregationfunction to deal with typical count questions, which start with “How many..?”.2 System DescriptionSPO query. The SPO query function provides two input fields, Entity and SetPredicate, and a KB selection button. The first field provides real-time entitysuggestions from the selected KB, based on the input prefix, to the user tochoose from. Next, the user selects a set predicate from the set predicate inputfield. The predicate choices are KB-specific and ordered by i) whether they arepopulated and have alignments, ii) they are populated but without alignments,and iii) they are unpopulated.Upon execution, the input parameters are sent to our server, where we de-termine the variant of the user-selected set predicate - counting or enumerat-ing. Then from the KB-specific alignments containing the queried predicate, weshortlist the top-five highest scoring pairs to obtain related set predicate facts.4 S. Ghosh et al.Fig. 3: Query results on the number of employees in Microsoft (DBpedia-mapped).If there are no alignments we do not generate any related query. The serverthen fires the main query to the SPARQL endpoint of the corresponding KBfollowed by the SPARQL queries for the aligned set predicates, if present. Oncethese results are obtained, the server returns the results along with KB-specificpredicate statistics, i.e., the average value that the counting predicates take andthe average number of entities per subject that the enumerating predicates take.Alignments. CounQER provides an option of viewing all alignments acrossthe four KBs along with their alignment scores. A user can go through the listordered by the alignment score or, use the search bar to filter matching setpredicates and view their corresponding alignments. Each alignment has a linkto SPARQL query API where the user can view the list of subjects for whichthe predicate pair co-occur.The main features of the interface are as follows.1. Predicate suggestions - Set predicates are ordered based on whether they arepopulated for the selected entity and whether alignments exist for them.2. Empty results - If the main query returns an empty result, but, the predicatehas populated alignments, CounQER shows the related results. Conversely, ifthe set predicate in the main query is populated and alignments exist for thispredicate, we show the related results regardless of them being empty, thushighlighting potential incompleteness in the KB, w.r.t the queried entity.3. Links to SPARQL queries - Every row in the results contains a link to theSPARQL endpoint, which a user can follow to check the actual query thatwas fired and also view enumerations of size more than 1000. Alignmenttables also link to the SPARQL endpoint with queries which list subjects forwhich the set predicate pair co-occur.We also show some manually added ideal alignments, i.e., the alignmentswhich are present in the investigated KBs but missed by the automated Coun-QER methodology. These alignments are also present in the table with a fictitiousscore between [0.9-1].CounQER: A System for Discovering and Linking Count Information in KBs 5Fig. 4: Query results on the number of children of Charlie Chaplin (Wikidata).3 Demonstration ExperienceScenario 1 - QA. In a query about the number of employees at Microsoft,CounQER finds the main result from the queried KB, DBpedia-mapped, tobe 114, 000 employees. In addition, CounQER returns instantiated facts on in-teresting enumerating predicates, such as, employer−1 and occupation−1 (seeFig. 3).Scenario 2 - KB curation. Consider the example in Fig. 4, where the usersearches for the number of children of the British comic actor, Charlie Chap-lin. The alignment results reveal inconsistent information in Wikidata-truthy.While the value for number of children is 6, there are 9 statements for theenumerating predicate child.Next, we investigate the winning titles of Roger Federer in DBpedia-raw(Fig. 5). Even though a query on the golds won by Federer returns no mainresults, unlike the query on the golds won by Leader Paes in Fig. 1, the countingpredicates doublestitles (2nd) and singlestitles (3rd) give the number ofdoubles and singles titles won by Federer.4 ConclusionWe demonstrate how set predicate alignments highlight redundancies in the KBschema, enhance question answering by providing supporting counts and/or enu-merations and help in KB curation. Utilizing KB alignments to automaticallyflag inconsistent SPO facts for resolution and highlight SPO facts needing com-pletions is a possible future work. Analysing multi-hop alignments and extendingKB alignments towards open information extraction is also worth exploring.6 S. Ghosh et al.Fig. 5: Query results on golds won by Roger Federer (DBpedia-raw).References1. Bast, H., Haussmann, E.: More accurate question answering on Freebase. In: Pro-ceedings of the 24th ACM International Conference on Information and KnowledgeManagement, CIKM (2015)2. Diefenbach, D., Migliatti, P.H., Qawasmeh, O., Lully, V., Singh, K., Maret, P.:QAnswer: A question answering prototype bridging the gap between a considerablepart of the LOD cloud and end-users. In: The World Wide Web Conference, WWW(2019)3. Ghosh, S., Razniewski, S., Weikum, G.: Uncovering hidden semantics of set infor-mation in knowledge bases. arXiv:2003.03155 (Under revision at Journal of WebSemantics 2020)4. Koutraki, M., Preda, N., Vodislav, D.: Online relation alignment for linked datasets.In: The Semantic Web - 14th International Conference, ESWC (2017)5. Mirza, P., Razniewski, S., Darari, F., Weikum, G.: Enriching knowledge bases withcounting quantifiers. In: The Semantic Web - 17th International Semantic WebConference, ISWC (2018)6. Paulheim, H.: Knowledge graph refinement: A survey of approaches and evaluationmethods. Semantic Web (2017)7. Razniewski, S., Suchanek, F., Nutt, W.: But what do we actually know? In: Pro-ceedings of the 5th Workshop on Automated Knowledge Base Construction AKBC(2016)8. Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future. IEEETransactions on Knowledge and Data Engineering (2013)9. Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., Auer, S.: Qualityassessment for linked data: A survey. Semantic Web (2016)

{CounQER}: {A} System for Discovering and Linking Count Information in Knowledge Bases

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{CounQER}: {A} System for Discovering and Linking Count Information in Knowledge Bases

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