Knowledge discoveryKnowledge modeling
andg. Eer,. Thugg
gest potentially useful propositions mined from other users knowledge models, while EXTENDER mines
CC) (eactions. A keyate th
requiring formalization, enabling end users to capture knowledgewith minimal training. However, users faced with the task ofdeveloping a concept map may not always be able to rememberall the most relevant concepts, or may have difculty decidingwhich concepts to add to a concept map under construction(referred to as extending the concept map). Likewise, it may be
oth for levoing beyo
knowledge prior maps contain.This article begins by discussing the task context for this
the problem of supporting concept-map-based knowledgeingand the opportunity for knowledge extension support usingideas from CBR. It then presents the methods that we havedeveloped for knowledge extension and topic generation. Themethods developed have been evaluated individually in controlled
Corresponding author. Tel.: +1 8128559756.E-mail addresses: firstname.lastname@example.org (D. Leake), email@example.com
(A. Maguitman), firstname.lastname@example.org (T. Reichherzer).
1 Decision Index for Searching Category Entries by Reducing NEighborhood Radius.2 EXtensive Topic Extender from New Data Exploring Relationships.
Knowledge-Based Systems xxx (2014) xxxxxx
Contents lists available at ScienceDirect
.e lthem in an explicit form which can be compared. It has proven auseful approach for constructing and sharing knowledge without
Together they provide context-relevant support bthe knowledge in prior concept maps and for ghttp://dx.doi.org/10.1016/j.knosys.2014.01.0130950-7051/ 2014 Elsevier B.V. All rights reserved.
Please cite this article in press as: D. Leake et al., Experience-based support for human-centered knowledge modeling, Knowl. Based Syst. (2014),dx.doi.org/10.1016/j.knosys.2014.01.013eragingnd the
workmodel-and sharing of human knowledge. The knowledge-based systemscommunity is well aware of the difculty and cost of buildingknowledge models, which has led to interest in leveraging experi-ence to aid knowledge modeling. This article presents research onapplying ideas from case-based reasoning (CBR) (e.g., ) to thetask of knowledge modeling, supporting users of software toolsfor concept mapping. Concept mapping  aims to elucidate aparticular individuals conceptualizations about a domain, putting
tion. It describes DISCERNER, an experience-based system which aidsknowledge modelers by drawing on other users knowledge models,presenting suggestions mined from them, and EXTENDER,2 a systemwhich complements DISCERNERs experience-based approach by draw-ing on information mined from search engines to help identify novelconnections to consider and new areas to model. Both systems areoptional software components which can be incorporated into theCmapTools  concept mapping system to augment its functionality.1. Introduction
Human-centered computing (Hmethods for improving the intercombined human/machine systemscentered computing is how to facilitsearch engines to suggest new related areas to model. The article presents experimental results address-ing two previously open questions for the project: DISCERNERS retrieval accuracy and EXTENDERS ability togenerate articial topics with content similar to topics determined by domain experts. Both experimentsshow satisfactory results.
2014 Elsevier B.V. All rights reserved.
.g., [21,44,3]) studiesand performance ofchallenge for human-
e construction, capture,
difcult for users modeling a domain to identify the topics forwhich concept maps should be generated. Consequently, there isneed for tools to support the concept mapping process.
The article describes research on the development of intelli-gent suggesters designed to proactively provide information toaid users of knowledge modeling tools for concept map construc-
1Concept mappingIntelligent user interfaces
knowledge models. It describes DISCERNER and EXTENDER, two proactive suggesters that can be incorporatedinto the CmapTools concepts mapping system. DISCERNER applies case-based reasoning techniques to sug-Experience-based support for human-cen
David Leake a,, Ana Maguitman b, Thomas Reichhera School of Informatics and Computing Bloomington, Indiana University, Informatics WbDepartamento de Ciencias e Ingeniera de la Computacin, Universidad Nacional del ScDepartment of Computer Science, University of West Florida, Bldg. 4, 11000 University
a r t i c l e i n f o
Article history:Available online xxxx
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The construction, capturehuman-centered computinknowledge models. Howevto include in these modelsat developing intelligent s
journal homepage: wwwred knowledge modelingc
901 E. 10th St., Bloomington, IN 47408, USAvda Alem 1252, B8000CPB Baha Blanca, Argentinay., Pensacola, FL 32514, USA
sharing of human knowledge is one of the fundamental problems oflectronic concept maps have proven to be a useful vehicle for buildingthe user has to deal with the difcult task of deciding what informationis article reports the culmination of a multi-year research project aimedesters designed to aid users of concept mapping tools as they build their
sevier .com/ locate /knosyshttp://
experiments, as well as informally tested as robust prototypeswithin CmapTools , a widely-used knowledge modeling systemdeveloped at the Institute for Human and Machine Cognition(IHMC), with encouraging results. This paper reports the culmina-tion of a strand of research begun in 2002 in collaboration withAlberto Caas and the CmapTools team (e.g., [26,30,33,29]). Asthe rst journal publication on our work on DISCERNER and EXTENDER,the article summarizes key ideas; it also presents new results ofexperiments designed to address key open questions remainingfrom previous publications: The accuracy of DISCERNERS indexingand the ability of EXTENDER to generate topics similar to those gen-erated by human experts. The article closes with a review of re-lated work, addressing the ramications of this work for CBR andknowledge capture interfaces.
2. Supporting knowledge modeling with concept maps
Concept mapping  was rst proposed in education, to en-able students to externalize their knowledge by constructing atwo-dimensional, visually-based representation of concepts andtheir relationships. This representation was seen as elucidatingtheir internal cognitive structures, suitable for assessment orknowledge sharing. Concept mapping is used worldwide to facili-tate knowledge examination, construction, comparison, and reuseby users ranging from elementary school students to scientists(for a recent sampling of its use, see ).
The CmapTools software  supports generation, storage of,and access to concept maps in electronic form. In addition to
providing basic operations needed to draw and label concept maps,CmapTools includes extensive capabilities for annotating conceptmaps with links to electronic resources such as images, diagrams,video clips, and other concept maps, enabling the construction ofrichly connected concept-map-based knowledge models for particu-lar domains. It also enables distributed storage and access to con-cept maps on multiple servers, to support knowledge sharingacross multiple sites. Fig. 1 shows a screen image captured duringa session in which a user was extending a concept map. The bot-tom left window shows a sample concept map of the Mars explo-ration domain; the window for the initial concept map is thestarting window for the system. The next window to the right isthe window opened for DISCERNER and EXTENDERs suggestions whenthe user invokes those systems. The top portion of the suggestionpanel presents a list of propositions suggested by DISCERNER, andthe bottom of this panel presents topics suggested by EXTENDER.We describe these windows and their use further in Section 3.The windows arranged on the border of the image were generatedby the user during the CmapTools session, by clicking on iconsassociated with nodes of the concept map in the starting window.In this instance, the new windows contain (clockwise from the ori-ginal concept map window) an image, a related concept map, and aweb page.
Many systems have been developed to facilitate human captureof knowledge in formal representations suitable for machine rea-soning; for example, an extensive set of ontology editing toolshas been developed (for reasons of space, we cannot summarizethem here; see Denny  for a survey). The CmapTools project
2 D. Leake et al. / Knowledge-Based Systems xxx (2014) xxxxxxFig. 1. The CmapTools interface. The gure illustrates a concept map under c
Please cite this article in press as: D. Leake et al., Experience-based support fordx.doi.org/10.1016/j.knosys.2014.01.013onstruction, associate resources, and suggestions for concept extensions.
human-centered knowledge modeling, Knowl. Based Syst. (2014), http://
map on Microbial Fossil Records. The user may require additionalmaterial to include in the in-progress concept map, as well as sug-
ing to retrieve prior cases which have not been pre-structured,and in having to deal with unrestricted vocabularies, the concept
sedgestions of related topics to begin the construction of new conceptmaps for the Mars knowledge model.
The top area of the suggestion panel contains DISCERNERs sugges-tions, based on prior concept maps, which may include concepts,propositions (pairs of linked concepts) and other resources. Inthe screenshot these include the proposition Search for Evidenceof Life, Past or Present, pursues a goal of ASTROBIOLOGY. Whenthe user selects this proposition (by clicking), the system opensthe concept map in which it appears, Search for Evidence of Life(shown on the top center of Fig. 1), providing rich related materialto consider for inclusion in the concept map being built.
Simultaneously, EXTENDER generates suggestions of candidatetopics related to, but distinct from, the concept map under con-struction; these are potential topics for additional concept mapsto generate. For each topic, it generates a top-level label consistingof three terms, used to characterize the suggested topic. Any ofthese suggestions can be expanded by clicking on the + symbolappearing at the left side of the topic label, to show additionalterms for the topic. In our example, suggestions sediments, deep,contrasts in taking a human-centered view, aiming to support thecapture of knowledge in a form conducive to human examinationand sharing. Concept maps provide an informal, nonstandardizedrepresentation based on structured, simplied natural language.Electronic concept mapping has been successfully applied toknowledge capture and sharing for a wide range of tasks such asmaintaining Navy equipment , local weather prediction ,explaining the design of rocket engines , and describing Webservices in Service-Oriented Architectures (SOA) composite appli-cations . An overview of a number of applications of conceptmapping tools is presented in .
Informal studies show that when building concept maps, bothexperts and ordinary users often pause for signicant amounts oftime wondering what information to include. Frequently, they lookat existing concept map libraries and information on the Web forconcepts or links to include in their maps or for topics to startnew maps for creating rich, comprehensive knowledge models.Our tools aim to automatically provide suggestions generated fromsuch sources.
3. Using case-based reasoning to support knowledge modeling
During knowledge modeling, concept maps are constructedincrementally. At each step, concepts in an in-progress conceptmap are extended by adding new connections, either to existingor new concepts. Thus the user chooses a concept of a partial con-cept map, in context of already-existing concepts and links, and se-lects an appropriate link or concept/link pair to add to the map,connected to the chosen concept. If previous users have confrontedsimilar situations when building concept maps, and have resolvedthem with particular choices, that knowledge modeling experiencemay be reused. Such reuse ts within the mold of case-based rea-soning (CBR), which solves new problems by retrieving and adapt-ing the solutions of similar prior problems (e.g., ). DISCERNERstask is to propose concept map extensions to the user. Becausethe concept mapping process aims to capture the current usersconceptualizations, there is no single right extension; the systemplays an advisory role.
For example, suppose the user is an astrobiologist building aknowledge model composed of concept maps on the Mars domain,as in Fig. 1, and that the users current task is to construct a concept
D. Leake et al. / Knowledge-Baburial and enzyme, breakdown, temperature have been selectedfor expansion and additional terms are displayed. Furthermore, theuser can examine the web pages that were used by EXTENDER as a
Please cite this article in press as: D. Leake et al., Experience-based support fordx.doi.org/10.1016/j.knosys.2014.01.013map suggester task faces some of the same issues faced by textualcase-based reasoning . However, concept maps explicit linksbetween concepts provide a valuable additional informationsource beyond what is available in text. A major focus of our workis on developing methods to exploit this information.
4. Mining concept map libraries for cases to support knowledgeextension
DISCERNER helps a user to create extensions for a concept bylinking it to other concepts in the same map or new conceptsadded to the map. For example, consider the scenario in Fig. 1,for which DISCERNER is aiding in connecting concepts to Searchfor Evidence of Life, Past or Present. First, DISCERNER retrieves sim-ilar prior concept maps that include Search for Evidence of Life,Past or Present or concepts with similar textual descriptions.After this set of candidate concept maps has been retrieved, thesystem extracts the ways Search for Evidence of Life was linkedto other concepts in those past contexts and lists them in a sidepanel as candidates for potential extensions of a highlighted con-cept in the map.
Consistent with our observation that any part of the conceptmap may be seen as the problemthe concept to extend by add-ing links to other conceptsor the solutionthe concepts to beadded to the concept map and/or linked, DISCERNERs indexing ap-proach does not pre-dene problems or solutions. Instead, itclassies concept maps into a hierarchical set of categories provid-ing a broad characterization of the material in the map, and basesretrieval on that characterization. For efcient matching/retrievalit uses a vector space model  to describe the content of eachcategory and each concept map. Terms in the vector correspondto terms appearing in the concept and link labels in the map. Be-cause structural information plays an important role in determin-starting point for generating its sugge...