Implementation and Evaluation of a Semantics-based UserInterface for Web Gazetteers

Krzysztof JanowiczInstitute for Geoinformatics

University of Munster,Germany

janowicz@uni-muenster.de

Mirco SchwarzInstitute for Geoinformatics

University of Munster,Germany

mirco.schwarz@uni-muenster.de

Marc WilkesInstitute for Geoinformatics

University of Munster,Germany

marc.wilkes@uni-muenster.de

ABSTRACTAs the Web moves towards structured data, new communi-cation paradigms and user interfaces become necessary tosupport users beyond simple keyword search. In contrastto applications for expert users, these interfaces should hidethe complexity of ontology-based systems but still offer theirreasoning capabilities. Using Web gazetteers as applicationarea, we discuss how subsumption and similarity reasoningcan be integrated into modern Web interfaces and explain themade design decisions. To evaluate our approach, the resultsfrom a first human participants test are presented.

Author KeywordsInformation Retrieval, Similarity, Geographic Feature Types

ACM Classification KeywordsH.5.2 Information Interfaces and Presentation: User Inter-faces—Graphical user interface; H.3.3 Information Storageand Retrieval: Information Search and Retrieval

INTRODUCTION AND MOTIVATIONThe major benefit of semantics-based information retrievalis the possibility to reach beyond simple keyword search,and hence to support the user in answering complex queries.While there are several promising approaches to semantics-based retrieval such as subsumption and similarity reason-ing, there is surprisingly little work on how to hide the ad-ditional complexity (e.g., the underlying ontology) from theuser. In addition, the results have to be presented in a waythat they are also comprehensible for non-experts. The ex-tended retrieval capabilities of the semantic Web should notbe slowed down by more complex user interfaces.

Based on our previous work [14, 13], we discuss the im-plementation and evaluation of a semantics-based user in-terface for Web gazetteers. It makes use of subsumptionand similarity reasoning to support the users in navigatingthrough geographic feature types. Our idea is to combine

Workshop on Visual Interfaces to the Social and the Semantic Web(VISSW2009), IUI2009, Feb 8 2009, Sanibel Island, Florida, USA.Copyright is held by the author/owner(s).

techniques developed in conjunction with the social Web2.0, such as search-while-you-type forms (also known as au-tocompletion) and tag clouds, with the reasoning capabilitiesof the semantic Web.

GAZETTEERS AS APPLICATION AREAGazetteers are place name directories. Each gazetteer recordconsists at least of a triple (N, F, T ) where N correspondsto one or more place names, F represents one or more geo-graphic footprints (i.e., locations), and T is the type (class)of the described feature (i.e., the representation of a realworld geographic entity). Hence, gazetteers support at leasttwo types of queries. First, they map between place namesand respective footprints: N → F ; and second, they map be-tween names and geographic feature types: N → T . Notethat a named geographic place is an abstract individual de-fined to refer to a physical region in space and categorizedaccording to commonly agreed characteristics. The placename is a handle to support communication. Hence, Placeis a social construct of interest for a particular communityduring a specific time span. A place may change its name,location, and type (e.g., from city to ruin) over time [10, 12].

Gazetteers are key components of all georeferenced infor-mation systems, including GIScience applications in manydiverse fields, Web-based mapping services such as GoogleMapsTM, and an increasing number of Web 2.0 applicationssuch as plazes.com. Gazetteers are crucial for geoparsingwhere references to geographic locations (by their names)are recognized in texts and converted to coordinate refer-ences [10]. Most gazetteers are either accessed via a Webpage or through an application programming interface (API).The geographic feature type is selected from a typing scheme,in most cases from a feature type thesaurus. The type of thedescribed geographic feature is of special importance. Forinstance, a search for places named Berlin might return morethan hundred places with a lot of different feature types vary-ing from capital (located in Germany), over administrativearea (a state in Germany), to stream (located in Colombia).

One of the major Web gazetteers is the Alexandria DigitalLibrary (ADL) Gazetteer1. The ADL Web interface depictedin figure 1 includes a map to narrow down the search to a par-ticular spatial extent, a form to enter the place name (or partsof it), as well as a scrolldown list with about 200 (or 1200

1http://www.alexandria.ucsb.edu/gazetteer/

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if one includes the non-preferred) geographic feature types.To learn about the definitions of these types, the user has toswitch to the ADL Feature Type Thesaurus (FTT) and readthe (often ambiguous and brief) plain text descriptions. Thethesaurus also contains information about super types andsome manually selected related types (with many types re-maining unrelated to others). Some gazetteers try to take upthe participantion idea of the Web 2.0 and allow for user de-fined places and types. In both cases, the navigation throughand understanding of the types is a major shortcoming [12].

The following example illustrates the navigation betweenfeature types in ADL. A user selects lakes from the featuretype scrolldown list using the ADL gazetteer Web interface.Lakes is a narrower term [2] of hydrographic features whichis a top term in the FTT hierarchy. As the user cannot finda particular geographic feature, she decides to change thetype to search for reservoirs. Unfortunately, reservoirs isnot a narrower term of hydrographic features, and thereforeshe looks up the textual definition of lakes in the thesaurus.Here, reservoirs is specified as related term [2] and she canclick on the link and read the definition of reservoirs. It isdefined as narrower term [2] of hydrographic structure. Thisin turn is a narrower term of manmade features which is an-other top term in the ADL FTT.

Figure 1. The Alexandria Digital Library Gazetteer Web interfaceshowing a search for reservoirs in Florida.

As discussed by Janowicz and Keßler [12], semantics-baseduser interfaces in conjunction with geographic feature typeontologies would support the user in browsing through andsearching in gazetteers. The alternative interface proposed inthis paper aims at hiding the feature type scrolldown list (seefigure 1) from the user. Instead, we propose a search-while-you-type form as well as a combination of horizontal andvertical search to improve the navigation between (related)feature types. These types will be determined based on theirformal specifications. Hence, users do not have to look uppotentially relevant types in the FTT by hand.

SEMANTICS-BASED INFORMATION RETRIEVALIn contrast to mere syntactic search, semantics-based infor-mation retrieval takes the underlying conceptualization (orattribution) into account to improve searching and brows-ing through structured data. Three approaches can be dis-tinguished: 1) those based on classical subsumption reason-ing, 2) those based on so-called non-standard inference tech-niques such as computing the least common subsumer (lcs)or most specific concept (mcs) [18, 17], and finally 3) thoseapproaches based on computing semantic similarity2 [5, 6,3, 14]. As depicted in figure 2, subsumption reasoning canbe applied to vertical search, while similarity works best forhorizontal search and should not be applied to compare suband super types.

Figure 2. Vertical and horizontal retrieval within an ontology.

Formally, the result set for a user’s query using subsumptionreasoning is defined as RS = {C | C v Cs}; where Cs isthe search (or query) concept specified by the user. As eachconcept returned to the user is a subsumee of the search con-cept, it meets the user’s search criteria. This makes selectingan appropriate search concept the major challenge for endusers. In fact, the search concept is an artificial constructand not necessarily the searched concept [15]. If it is toogeneric, i.e., at the top of the hierarchy, the user will get alarge part of the queried ontology back as (unsorted) resultset. In contrast, if the search concept is too specific, the re-sult set might be empty.

In case of similarity-based retrieval, the result set is definedas RS = {Ct | sim(Cs, Ct) > t}; where Ct is the compared-to target concept and t a threshold defined by the user or ap-plication [14, 15]. In contrast to subsumption reasoning, thesearch concept is the concept the user is really searching for(no matter if it is part of the queried ontology or not). Asthe concepts are compared for overlap (of their definitionsor extensions), similarity is more flexible than subsumptionreasoning, but it is not guaranteed that the results match all ofthe user’s search criteria. Note that the similarity estimationsbetween search and target concepts are asymmetric which isimportant for information retrieval. Usually, the results of a2We are focussing on measures which define similarity as degreeof conceptual overlap here. Other theories consider similarity as(inverse) distance within a multi-dimensional space, or as a set oftransformations [7] from the search to the target concept.

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similarity query are presented to the user as an ordered listwith descending similarity values.

Summing up, the benefits similarity offers during the re-trieval phase, i.e., to deliver a flexible degree of (conceptual)overlap to a searched concept, stands against shortcomingsduring the usage of the retrieved information, namely thatthe results not necessarily fit the user’s requirements.

To overcome these shortcomings, similarity theories such asSIM-DL combine subsumption and similarity reasoning. Ina first step, the context of discourse [11] is defined by intro-ducing a so-called context concept such that Cd = {Ct|Ct vCc}. Consequently, in the next step only such concepts arecompared for similarity which are subconcepts of Cc. Thisway, the user can specify some minimal characteristics alltarget concepts need to share. As depicted in figure 3, Cti

and Ctjare compared for similarity to Cs while Cx is not.

Note that for reasons of simplification, the figures 2 and 3show a single hierarchy, while similarity takes all role-fillerpairs (e.g., the fact that a river has a spring as its origin∃hasOrigin.Spring) into account to compute conceptualoverlap.

With regard to semantics-based user interfaces, subsumptionreasoning can be applied to include lakes in the result set ifthe user is searching for (the string) waterbody. Mcs and lcscan be used to implement a query-by-example in where theresult set is computed out of a set of prototypical individuals(e.g., waterbodies) selected by the user [26, 15]. Similaritymeasures can be implemented to propose alternative or sup-plementary results such as reservoirs if the user is searchingfor (the string) lake. However, to propose meaningful alter-natives the underlying similarity measure needs to be cog-nitively plausible, i.e., the rankings returned by such a mea-sure have to correlate with human similarity judgments forthe same set of compared individuals or concepts based onthe same definitions3. This has been shown for several mea-sures such as MDSM [19] and SIM-DL [14, 13]. As thesesimilarity measures compare DL concepts defined within on-tologies, the question what is similar to Lake depends on theformal definition (and does not necessarily match the mentalconceptualization of a particular human user).

SEMANTICS-BASED USER INTERFACESWhile there are specifications on how to display RDF triplesto the user [4, 20] and various semantic Web browsers suchas PowerMagpie4, there is little work on how to integrate thereasoning capabilities of the semantic Web within end userinterfaces for information retrieval. Schraefel and Karger[22] argue against the assumption that data on the semanticWeb should be displayed to end users as graphs simply be-cause graphs are used for the computational representation(as data model). As in contrast to HTML, XML (and there-fore RDF and OWL) separates design from content, the vi-sualization should depend on the task to solve instead of the3This is a weaker requirement than cognitive adequacy whichwould imply that the computational similarity theory reproducesthe process of human similarity reasoning.4http://powermagpie.open.ac.uk/

Figure 3. Combining subsumption and similarity reasoning for in-formation retrieval. Cs is the search concept, Cti and Ctj are thecompared-to target concepts, while Cx is not a subsumee of the con-text concept Cc, and hence is not compared for similarity to Cs.

content or the type of data. Van Kleek and colleagues [24]point to the obstacles users face when diving into the seman-tic Web and introduce an agenda on how to improve knowl-edge creation and access for end users. Van Ossenbruggen etal. argue why it is difficult to evaluate semantics-based userinterfaces [25]. Basically, one needs to distinguish betweenthe quality of the underlying data, the involved reasoningengine, and the user interface as such. In this work, we fo-cus on the user interface. Additional information on the un-derlying data and ontologies is discussed in [12], while thesimilarity reasoning is described in [14].

The Atom interface [21] realizes a circular layout to supportusers in navigating through semantic Web data. One exam-ple for a combination of Web 2.0 interaction and semanticWeb techniques is given by Heath and Motta [8], who de-scribe how non-experts can publish and consume RDF databy means of tags in a reviewing Website. Whereas theirwork deals with tagging data and proposing related tags, wepresent an approach to interact with data classified in a tax-onomy (of feature types) and focus on similarity and sub-sumption instead of relatedness. A promising approach tointegrate semantic autocompletion for geographic locationsis described by Hildebrand et al. [9]. They propose an inter-face which allows for searching geographic places by namewhere the suggestions are grouped by country or featuretype. Instead of focusing on a search by name, in this paper,we implement an additional feature type parameter, allowingfor a more restrictive search of places but also demanding foran intuitive feature type selection process. An approach toease concept selection is presented by Sinkkila et al. [23].They present an interface that combines semantic autocom-pletion with cross-ontological context navigation. WhereasSinkkila et al. chose to visualize the concept hierarchy aswell as related concepts, we suggest to hide the taxonomiccomplexity from the user.

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IMPLEMENTATIONIn order to demonstrate and evaluate how semantics-basedinformation retrieval can be integrated in Web interfaces forend users, we have implemented a prototypical interface forthe ADL gazetteer. The interface can access the recordsstored in the gazetteer via the ADL API. To implement thesearch-while-you-type form as well as geographic featuretype recommendations, the prototype makes use of AJAX(Asynchronous JavaScript and XML) which allows to up-date certain parts of the Web interface without the need toreload the whole page.

As depicted in figure 4, the gazetteer Web interface allowsfor specifying three search parameters: place name, geo-graphic feature type, and location of the searched place. Asargued above, while specifying the name and location of asearched place is straightforward, choosing the correct fea-ture type is more difficult, especially in combination with theother parameters. An example is shown in figure 4; note thatmost of the places categorized as reservoirs are named lakeor pond, hence they would not appear in the result list if theuser would search for geographic features of type Lake.

Figure 4. The semantics-based Web interface for the ADL gazetteershowing the search result for reservoirs in southern Florida.

When the users begin to enter characters in the feature typeinput field a table appears showing suggested types, startingwith the characters already entered in the input field. There-fore, users can immediately see which feature types are sup-ported by the application without browsing the feature typethesaurus (or ontology) manually. In addition, the table pro-vides information about similar types and super types (whichare computed by the SIM-DL similarity server5 at querytime). These recommendations support the user in browsingthe underlying feature type ontology (vertically and horizon-tally; see figure 3).

To display the similarity rankings in the Web interface fontsize and opacity are used. This is known from tag clouds, asused by several blogs or Web 2.0 recommendation portals,and should therefore be familiar for the user. The font size

5The SIM-DL similarity server as well as the interface are freeand open-source software and can be downloaded at http://sim-dl.sourceforge.net/.

and opacity visualize the ranked order of the similarity val-ues, where a big font size and high opacity indicate a highsimilarity. Each feature type entry in the table is a hyper-link. If a user clicks on one of the suggested feature types,it is selected as search parameter and the similar and supertypes are computed thereupon. The color of the chosen fea-ture type changes to orange to indicate that this type is partof the new query.

The features resulting from a specific query are displayed atthe right side of the interface and on the map. Clicking onthese links or map markers opens a text box which containsthe information from the ADL gazetteer (e.g., names, foot-prints, broader administrative units).

Figure 4 shows the result of an exemplary search for placesof type Reservoir located in southern Florida (without namerestriction). Using the interface, the search could be broad-ened by clicking on the supertype Waterbody, leading to alarger result set (including the results for Reservoir). In casethe search for reservoirs did not yield the desired result, theuser could also decide to search for similar geographic fea-ture types such as Lake or Canal.

To point out why a semantics-based interfaces is valuable forend users, imagine the following example. One of the searchresults shown in figure 4 is Lake Manatee. Due to its name,a user searching for this specific place might be tempted todeduce that it is of type Lake. Specifying a query as shownin figure 6 seems obvious, but will not return Lake Manatee,since it is of type Reservoir. However, facing an empty resultset, the user could decide to either broaden the search byselecting the supertype Waterbody, or try out a similar typeto Lake. In the latter case, searching for Reservoir, which ismost similar to Lake, yields the desired result.

EVALUATIONIn this section we discuss first results and lessons learnedfrom a human participants test carried out using the new in-terface.

Our hypothesis was that an interface combining subsump-tion and similarity to support the user in selecting appro-priate feature types would be more effective than interfacessolely based on either subsumption or similarity reasoning.We did not compare our interface to the original ADL inter-face. This is for the following reasons:

• The original ADL interface makes use of the ADL Fea-ture Type Thesaurus while the new interface is based ona feature type ontology which contains only hydrographicfeature types so far [12]. Additionally, we do not distin-guish between preferred and non-preferred concepts andhave one root node instead of six.

• The new interface supports search-while-you-type, whilethe ADL interface does not. Consequently, the partici-pants would have to scroll through the whole ADL featuretype list – while only some of these types are relevant forthe test.

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• The ADL interface allows for multiple comparison oper-ators for the place names (has any words, has all words,has phrase, equals, matches pattern). To reduce complex-ity for end users, the new interface only supports has allwords for comparison.

• The ADL interface map does not support map markers asused in the Google MapsTMmashup in the new interfaceto narrow down the search to a particular map extent.

To test the hypothesis we developed three versions of thesemantics-based interface, the combined, a similarity-basedand a subsumption-based version. While the subsumption-based interface shows super and sub types, the combined in-terface only lists the super types (see figure 5). The underly-ing assumption was that users will select specific types ratherthan general ones and can use the super types to broadentheir search. Strictly speaking, this could be another hypoth-esis for further testing. Showing similar, super, and sub typesmay overload the interface and overstrain the user.

Figure 5. The geographic feature type recommendation part of theinterfaces: (1) combined interface, (2) similarity-based interface, (3)subsumption-based interface.

To verify the hypothesis we investigated how many taskswere successfully solved per interface and how many userinteractions (e.g., clicks) were necessary. As the interfaceacts as front-end for the ADL gazetteer (whose responsetimes differ), the time needed to solve the tasks cannot betaken as criterion. A total of 30 participants performed thetest6, i.e., each interface was tested by 10 participants. Toensure that the participants understand how the interfaceswork, a video was shown which stepwise presents how tosolve an introductory task:

Ems-Task

• Find the river Ems in Germany. Make it the onlyresult shown in the ’found places’ list. Try usingjust the ’place name’ field. Try using both fields,’place name’ AND ’place type’.

• Find canals connected to the Ems. Try usingjust the ’place name’ field. Try using both fields,’place name’ AND ’place type’. (Note that thenumber of ’found places’ differs. Do you seewhy?)

Next, the participants were asked to solve the task on theirown. Afterwards, four additional tasks (with sub tasks) were6With an age ranging from 21 to 30 years, 9 were female and21 male (most of them students of either geoinformatics or com-puter science). The questionnaire and the introductory video canbe downloaded at http://sim-dl.sourceforge.net/downloads/.

given to the participants. They were asked to speak out loudwhile filling in the test and each test was recorded on video.To make sure that the tasks are not biased towards subsump-tion or similarity reasoning, the questionnaire was designedin a way that it involves both kinds of reasoning as well as in-teraction with the map and the place name field. Finally, theparticipants could give general comments on whether theyliked the interface and how it could be improved.

As depicted in figure 4, all interfaces were designed in a waythat the search parameters form a sentence of the type

You are currently looking for places with . . . in theplace name, and . . . as place type, and that are locatedwithin this region: . . . .

Figure 6. The interface shows the sentence resulting from the user’ssearch parameters while querying.

During a series of pre-tests it turned out that many partici-pants are so accustomed to single keyword search fields (asused in most common Web search engines7) that they put theplace name as well as the feature type into the place namefield. This was still the case when they were asked to speakout the query before submitting it. Therefore, the interfaceswere redesigned to display the sentence resulting from theentered search parameters in the map area while loading (andthe participants were still asked to speak out the query). Thisreduced the number of wrong queries.

Analyzing the first results from our human participants test,it turns out that from a total of 40 tasks per interface, 72.5%were solved completely (i.e., including all sub tasks) usingthe combined interface. The participants using the similarity-based interface were able to solve 65% of the tasks, while62.5% of the tasks were solved using the subsumption-onlyinterface.

As depicted in figure 7, there is no clear difference in thetotal number of user interactions necessary to solve the tasks.However, the inter-quartile range differs clearly. It increasesfrom the combined interface over the subsumption-based tothe similarity-based interface.

7In contrast to upcoming semantics-based search engines such asPowerset (http://www.powerset.com/).

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Figure 7. The box plot shows the median as well as the 0.25 and 0.75quartiles for the number of total user interactions (clicking, typing inwords, following suggestions, interacting with the map) per interfacetype. Comb is the combined interface using subsumption and similarity,while Sim only shows similar types as suggestions and Sub only sub andsuper types.

While these first results support our hypothesis, the test alsopoints so some aspects which let us rethink the design ofthe Web interface as well as the number of user interac-tions as quality measure. Comparing manually entered ver-sus clicked feature types, intuitively one would expect thatthe more types are suggested, the more will be clicked in-stead of being manually entered. Hence, the number of man-ually entered feature types should be lower in the combinedinterface compared to the other ones. This turns out to bea wrong assumption. In fact, the ratio between clicked andmanually entered types was about one to one in case of thecombined and the similarity interface, while being higherfor the subsumption-based interface. To understand theseresults, we reviewed the videos taken from the participantsduring the test. It turns out that several participants had en-tered the type manually after seeing it in the suggestion table.This points towards two difficulties. First, we have to inves-tigate whether this was caused by the interface design, i.e.,by the positioning of the forms and the table (for instanceusing eye tracking). Second, this makes it more difficult tounderstand to which degree the suggested types were helpfulto the user in selecting the next type to be displayed.

After completing the tasks, the participants were asked to an-swer additional questions about the design and functionalityof the interface. While the participants liked the interfacesin general and rated the type suggestion table to be useful(giving both the second best median rank on a 5-level Likertscale), it turns out that some of the participants still had prob-lems dealing with two input fields. A given suggestion forimprovement was to have just one input field and automat-ically identify keywords representing feature types. Someparticipants recommended to cache previous queries, allow-ing for an easy comparison of several result sets using thebrowser’s navigation (back and forward) buttons.

In the current version of the interface, only ten entries of theresult set and their corresponding map markers are shown ata time. Although it is possible to go forth (or back) to seethe next (or previous) ten entries, several participants statedthat they almost overlooked the fact that there are more thanten result entries. As a consequence, they proposed to showall markers on the map, where those currently not listed inthe sidebar could be greyed out. Some participants also re-quested that features of similar types should be directly dis-played on the map using colored markers to visualize theirsimilarity instead of the suggestion table.

Finally, a few participants mentioned that upon sending aquery to the server and reading the query summary, they re-alized a mistake in one of the search parameters, and hencemissed a button to cancel the request. As this was not possi-ble, the wrong query was counted as user interaction duringthe test.

CONCLUSIONS AND FURTHER WORKIn this paper we discussed results from testing the integra-tion of subsumption and similarity reasoning into end userinterfaces to support horizontal and vertical retrieval. Whilea combination of both seems to be the most promising ap-proach, it is not surprising that there is a gap between beingable to implement more intelligent user interfaces and mak-ing them intuitively useable [25]. One solution would beto hide even more of the complexity (e.g., the suggestionstable) from the user by directly displaying features of verysimilar types. However, this may be interpreted as pater-nalism and reduce the user’s trust in such interfaces. Forinstance, while reservoirs and lakes might be similar enoughfor most tasks, some application areas such as water man-agement may require a clear distinction between both. Theextended retrieval capabilities of the semantic Web shouldbe used to offer support, while the final decision should stillbe up to the user. This leads to the question of how to in-corporate contextual information in the user interfaces andto which degree context influences the resulting suggestions[1, 16, 11]. From the viewpoint of the social Web, one couldalso think of allowing human users to add similar types byhand to improve the suggestions.

Figure 8. Conceptual design for the feature type suggestions allowingfor vertical and horizontal navigation.

Based on our experiences and the test results, we designeda new navigation component which will be integrated in thenext version of the Web interface (figure 8). This componentwill replace the feature type suggestion table in the upperright part of the interface. It will appear as soon as a feature

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type is selected or typed in by the user. The suggestion ta-ble itself will appear just below the feature type input fieldas usually implemented in autosuggestion forms. The newcomponent visualizes explicitly the difference between hor-izontal and vertical navigation. A vertical slider can be usedto specify more general or more specific feature types thanthe searched type. The result set only contains places thatare of a type under the slider. The horizontal slider allowsfor including similar types in the result set. This integratesthe idea of a similarity threshold which was discussed in thesemantics-based information retrieval section. Here, onlythose places are included in the result set that are to the left ofthe slider. As in the presented user interface, less opacity in-dicates decreasing similarity. The use of font size, however,differs. Whereas previously the font size also representedthe similarity value, within the conceptual navigation com-ponent the font size indicates the number of features foundwithin the map extent for that specific type; where a largerfont size points to a higher number of found places.

In figure 8, the immediate super type of Reservoir is Water-body, although another immediate super type of Reservoirspecified in the underlying ontology is Manmade. Allow-ing for generalizing also towards Manmade now yields twoproblems. First, the complexity of the navigation componentwould increase, ending up in a graph-like representation ofthe ontology (which is the opposite of our design goal). Sec-ond, generalizing towards multiple super types will result ina large and diverse result set. That is why we propose to onlygeneralize along one inheritance path. Deciding which pathof inheritance is shown in the navigation component dependson the context concept Cc whose integration in the interfaceis an open issue.

ACKNOWLEDGEMENTSThe comments from three anonymous reviewers provideduseful suggestions to improve the content and clarity of thepaper. This work is funded by the Semantic Similarity Mea-surement for Role-Governed Geospatial Categories (SimCat)project granted by the German Research Foundation (DFGRa1062/2-1), as well as the International Research TrainingGroup on Semantic Integration of Geospatial Information(DFG GRK 1498).

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