OnToMap - Semantic Community Maps for Knowledge SharingLiliana Ardissono, Maurizio

LucenteforteUniversita di Torino

Dipartimento di InformaticaTorino, Italy 10149

liliana.ardissono@unito.it,maurizio.lucenteforte@unito.it

Noemi Mauro, Adriano SavocaUniversita di Torino

Dipartimento di InformaticaTorino, Italy 10149

noemi.mauro@unito.it,adriano.savoca@di.unito.it

Angiole�a Voghera, Luigi LaRiccia

DIST Politecnico di TorinoTorino, Italy 10125

angiole�a.voghera@polito.it,luigi.lariccia@polito.it

ABSTRACTWe present the information retrieval model adopted in the On-ToMap Participatory GIS. �e model addresses the limitations ofkeyword-based and category-based search by semantically inter-preting the information needs speci�ed in free-text search queries.�e model is based on an ontological representation of linguisticand encyclopaedic knowledge, which makes it possible to exploitterms and synonyms occurring in the de�nitions of concepts to�exibly match the user’s and system’s terminologies. �is featureenables users to query the application using their own vocabulary.

CCS CONCEPTS•Information systems → Geographic information systems;Ontologies; Search interfaces; Presentation of retrieval results;

KEYWORDSParticipatory GIS, Information search, Ontologies, Linked DataACM Reference format:Liliana Ardissono, Maurizio Lucenteforte, Noemi Mauro, Adriano Savoca,and Angiole�a Voghera, Luigi La Riccia. 2017. OnToMap - Semantic Com-munity Maps for Knowledge Sharing. In Proceedings of HT ’17, Prague,Czech Republic, July 04-07, 2017, 2 pages.DOI: h�p://dx.doi.org/10.1145/3078714.3078747

1 INTRODUCTIONWeb-GIS are increasingly used to support geographical informationsharing, but they challenge information retrieval by imposing a�xed terminology for the speci�cation of information the user isinterested in. Starting from the GeoSpatial Semantic Web vision[2], we investigate the usefulness of semantics for enhancing notonly data management and integration, but also multi-faceted infor-mation search and data visualization. Speci�cally, we propose anontology-based model that integrates linguistic and encyclopaedicknowledge to enhance information search. �is model is applied inthe OnToMap Participatory GIS, described in the following.

�is work is partially funded by project MIMOSA (MultIModalOntology-driven query system for the heterogeneous data of aSmArtcity, “Proge�o di Ateneo Torino call2014 L2 157”, 2015-17).

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor pro�t or commercial advantage and that copies bear this notice and the full citationon the �rst page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).HT ’17, Prague, Czech Republic© 2017 Copyright held by the owner/author(s). 978-1-4503-4708-2/17/07. . .$15.00DOI: h�p://dx.doi.org/10.1145/3078714.3078747

2 SEARCH QUERY INTERPRETATIONOnToMap [3] supports the management of interactive geographicmaps for information sharing and participatory decision-making.�e development of this application started with project “Mappedi Comunita 3.0” (h�ps://ontomap.ontomap.eu), that investigatedthe possibility of a new representation of community maps, usingdigital media and a semantic representation of spatial knowledge.OnToMap supports both the consultation of spatial data and thecreation of public and private maps, which re�ect individual infor-mation needs and can be enriched with crowdsourced content tohelp project design and group collaboration.

�e application o�ers two information search modes, and di�er-ent granularity levels in the speci�cation of the relevant data.

• �e former mode enables the user to enter free-text searchqueries; see [1]. Starting from the limitations of keyword-based and category-based search, that are o�ered by mostWeb GIS and location-based social networks, we investi-gated the potential of integrating semantic and linguisticknowledge for improving information retrieval. For thispurpose, we de�ned an ontological representation of ge-ographical information that allows the categorization ofheterogeneous data, de�ning semantic relations amongconcepts, and is enriched with linguistic and encyclopaedicknowledge to de�ne the meaning of concepts and the termsthat can be used to refer to them. �e ontology has a cen-tral role in supporting multi-faceted information retrievalbecause it o�ers a bridge between the domain conceptual-ization adopted by the system and the user’s vocabulary:starting from the words occurring in the queries, and apply-ing word sense disambiguation and synonym recognition,the system can identify the relevant concepts and providethe corresponding results, le�ing the user free to expressher/himself in a natural way.

Search queries are interpreted in three steps:(1) Recognition of geographical constraints and identi�-

cation of the bounding box for data retrieval.(2) Semantic concept identi�cation, by matching a se-

mantically expanded query to the domain ontology inorder to identify the referenced concepts. �is enablesthe retrieval of a set of information items belonging tothe general topics of the search query; e.g., hospitals.

(3) Filtering of results to take the quali�ers speci�ed inthe query into account; e.g., pediatric hospitals. �isis done by projecting the retrieved data on the items

HT ’17, July 04-07, 2017, Prague, Czech Republic L. Ardissono et al.

Figure 1: Search results for “nosocomi (hospitals) in Torino” and visualization of the data concerning a speci�c hospital.

Figure 2: Generalist concept graph.

having in their own description a�ributes that coin-cide, or are semantically similar to those quali�ers(considering synonyms for �exibility).

E.g., the query in Figure 1 (“nosocomi a Torino”) is aimed at�nding the hospitals in Torino, but it is expressed using adi�erent name from the ontology concept (“Ospedali”). �esystem has recognized the term as a synonym of ”ospedale”and visualizes the results in the map. If the query includedany quali�ers (e.g., pediatric hospitals), the applicationwould �lter the instances of “Ospedali” by matching thequali�ers to their descriptions. �e result would be a singleinstance, “Ospedale Infantile Regina Margherita”, havingrecognized that “Infantile” is a synonym of pediatric.

Semantic query expansion is also used to suggest otherrelated concepts, guiding her/him towards types of infor-mation that can satisfy her/his needs in a more compre-hensive way (not shown in the �gure).

• �e graph-based information search mode enables the userto explore concepts and relations in a direct way, by brows-ing a graph that represents a thematic view on the domainconceptualization. For instance, Figure 2 shows a general-ist concept graph, which includes data categories relevantto non-expert users and displays subclass relations amongconcepts. �is interaction mode enables the user to ex-plore other possibly interesting concepts, by navigating

the semantic relations that link the graph concepts to therest of the ontology; e.g., the norms regulating the usageof infrastructures (“Infrastru�ure”) in the city.

�e semantic knowledge representation helps the explorationof the information space in several ways, thanks to the structuredrepresentation of Linked Data; see [4]. For instance, the user caninspect the details of a geographical object by clicking on its icon;see the sticky note in Figure 1. In that case, a table reporting themain information about the item is displayed in the right portionof the page. Moreover, by clicking on bu�on “Mostra/Nascondielementi correlati” (show/hide related items), the user can visualizeother information, related to the item in focus via semantic andgeographic relations. For instance, the right portion of Figure 1provides links to some o�cial documents on land usage relevantfor the area of the geographical item (“Riferimento normativo - …”),to a school (“Arduino”) and to a park adjacent to the hospital.

OnToMap was designed to manage participatory processes but ithas supported other activities, outperforming other Web-GIS thanksto the semantic interpretation of search queries (w.r.t. keyword-based search) and map management functions. E.g., we collectedpositive feedback by comparing it with OpenStreetMap (open-streetmap.org) in an experiment in which a group of secondarylevel students used both applications to organize a sport event.

REFERENCES[1] L. Ardissono, M. Lucenteforte, N. Mauro, A. Savoca, A. Voghera, and L. Lariccia.

2016. Exploration of Cultural Heritage Information via Textual Search �eries.In MobileHCI ’16 Proceedings Adjunct. ACM, 992–1001.

[2] K. Janowicz, S. Scheider, T. Pehle, and G. Ha. 2012. Geospatial Semantics andLinked Spatiotemporal Data – Past, Present, and Future. Semantic Web - Onlinked spatiotemporal data and geo-ontologies 3, 4 (2012), 321–332.

[3] A. Voghera, R. Crivello, L.Ardissono, M. Lucenteforte, A. Savoca, and L. Lariccia.2016. Production of spatial representations through collaborative mapping. Anexperiment. In Proc. of INPUT 2016. 356–361.

[4] W3C. 2017. Geospatial Semantic Web Community Group.h�ps://www.w3.org/community/geosemweb/.