Proceedings of the seventh international conference on Knowledge capture最新文献

{"title":"Knowledge capture in the wild: a perspective from semantic wiki communities","authors":"Y. Gil, V. Ratnakar","doi":"10.1145/2479832.2479858","DOIUrl":"https://doi.org/10.1145/2479832.2479858","url":null,"abstract":"Semantic wikis augment wikis with semantic properties that can be used to structure content that can therefore be aggregated and queried through reasoning. Semantic wikis have been adopted by many communities for very diverse purposes, such as organizing genomic knowledge, coding software, learn about hobbies, and tracking environmental data. Although wikis have been analyzed extensively, there has been little analysis of the use of semantic wikis. In this paper, we analyze the formalization of knowledge in 230 semantic wiki communities. We report our findings in terms of the edits of semantic concepts and properties, as well as the communities of editors for these semantic features of the wikis.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128576301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic extraction of advice-revealing sentences foradvice mining from online forums","authors":"A. Wicaksono, Sung-Hyon Myaeng","doi":"10.1145/2479832.2479857","DOIUrl":"https://doi.org/10.1145/2479832.2479857","url":null,"abstract":"Web forums often contain explicit key learnings gleaned from people's experiences since they are platforms for personal communications on sharing information with others. One of the key learnings contained inWeb forums is often expressed in the form of advice. As part of human experience mining from Web resources, we aim to provide a methodology to extract advice-revealing sentences from Web forums due to its usefulness, especially in travel domain. Instead of viewing the problem as a simple classification, we define it as a sequence labeling problem using various features. We identify three different types of features (i.e., syntactic features, context features, and sentence informativeness) and propose a new way of using Hidden Markov Model (HMM) for labeling sequential sentences, which in our experiment gave the best performance for our task. Moreover, the sentence informativeness score serves as an important feature for this task. It is worth noting that this work is the first attempt to extract advice-revealing sentences from Web forums.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124521040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An empirical perspective on representing time","authors":"A. Scheuermann, E. Motta, P. Mulholland, Aldo Gangemi, V. Presutti","doi":"10.1145/2479832.2479854","DOIUrl":"https://doi.org/10.1145/2479832.2479854","url":null,"abstract":"Most Knowledge Representation (KR) research follows a topdown approach: i) formalisms are designed on the basis of modelling needs and computational considerations, and ii) tools and applications based on these formalisms are realized and tested on application domains. As a result, there has traditionally been little attention in the KR research community to user issues, in particular to the usability of alternative modelling solutions. When statements about the intuitiveness of different solutions are found in the literature, these tend to reflect an author's epistemological standpoint, rather than any concrete user experience. In this paper we take a bottom-up, user-centric perspective and we report on an empirical study where subjects have been asked to represent temporal information and have been provided with alternative design patterns to do so. The study shows that, depending on their experience and level of expertise in KR, users tend to select different patterns for the given modelling problems. In particular, experts appear to choose on the basis of representation power, while naïve users appear to select on the basis of surface features and perceived user-friendliness. Interestingly, while some patterns are indeed perceived to be more intuitive than others, these considerations seem to apply primarily to less experienced users. Indeed, our findings appear to indicate that experts consider issues of 'intuitiveness' as secondary and, in contrast with naïve users, may be happy to apply patterns, which can be regarded as counter-intuitive, if they provide the right tool for the job.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"10 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120862326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding action dependencies using the crowd","authors":"Walter S. Lasecki, Leon Weingard, G. Ferguson, Jeffrey P. Bigham","doi":"10.1145/2479832.2479844","DOIUrl":"https://doi.org/10.1145/2479832.2479844","url":null,"abstract":"Training intelligent systems is a time-consuming and costly process that often limits real-world applications. Prior work has attempted to compensate for this challenge by generating sets of labeled training data for machine learning algorithms using affordable human contributors. In this paper, we present ARchitect, a system that uses the crowd to extract context-dependent relational structure. We focus on activity recognition because of its broad applicability, high level of variation, and difficulty of training systems a priority. We demonstrate that using our approach, the crowd can accurately and consistently identify relationships between actions even over sessions containing different workers and varied executions of an activity. This results in the ability to identify multiple valid execution paths from a single observation, suggesting that one-off learning can be facilitated by using the crowd as an on-demand source of human intelligence in the knowledge acquisition process.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127046134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guided entity reuse and class expression generator","authors":"C. Keet, Muhammad Tahir Khan, Chiara Ghidini","doi":"10.1145/2479832.2479837","DOIUrl":"https://doi.org/10.1145/2479832.2479837","url":null,"abstract":"Generic, reusable ontology elements, such as a foundational ontology?s categories and part-whole relations, are essential for good and interoperable knowledge representation. Ontology developers face the challenge to figure out which category their class belongs to and which relationship to choose for their ontology authoring tasks. To reduce this bottleneck, there is a need to have guidance to handle these Ontology-laden entities. We solve this with the generic approach GENERATOR: Guided ENtity reuse and class Expression geneRATOR that enables easy reuse of already represented knowledge such that it guides the modeller toward the comparatively best options of possible axioms to add. This has been realised with DOLCE, part-whole relations, and an automated reasoner that is used during the authoring process to propose feasible axioms, and the software has been integrated in the MoKi ontology development tool.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127156789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge representation in scientific models and theirpublications: a case study","authors":"M. D. Vos, W. V. Hage, J. Wielemaker, G. Schreiber","doi":"10.1145/2479832.2479850","DOIUrl":"https://doi.org/10.1145/2479832.2479850","url":null,"abstract":"It is our pleasure to welcome you to the Seventh International Conference on Knowledge Capture (K-CAP2013). The K-CAP conference series provides a forum that brings together members of several research communities and practitioners who are interested in efficiently capturing knowledge from a variety of sources and in creating representations that can be useful for automated reasoning, analysis, and other forms of machine processing. Therefore, research in knowledge capture is at the intersection of areas such as knowledge engineering, machine learning, big data, natural-language processing, human-computer interaction, Artificial Intelligence, and the Semantic Web. K-CAP 2012 follows on the success of six previous conferences in 2011 (Banff, Alberta Canada), 2009 (Los Angeles, California, USA), 2007 (Whistler, British Columbia, Canada), in 2005 (Banff, Alberta, Canada), in 2003 (Sanibel Island, Florida, USA), and 2001 (Victoria, British Columbia, Canada), and of the series of Knowledge Acquisition Workshops (KAW), the first of which took place in the same location (Banff, Alberta, Canada) in 1986. The theme of this seventh edition of K-CAP is: Knowledge Capture in the Age of Massive Web Data, reflecting the times we are living in. The call for papers attracted 60 submissions from Europe, America, Asia, and Oceania. The program committee accepted 13 full papers, 4 short papers and 3 application papers, that cover a variety of topics, including ontology engineering, ontology learning, linked open data, information extraction for knowledge capture, and knowledge capture from web data including online textual and multimedia resources. In addition, this volume includes descriptions of 8 posters and demos presented at the conference. In this seventh edition of the conference we have tried to innovate in several ways Rather than only aiming at research papers, we introduced an applications track for practitioners to present application-oriented contributions. We issued a call for participation for the first K-CAP \"Datathon\", a \"hackaton\" based on Open Data. Acknowledging the worldwide economic down turn, and the pressure many research and company budgets are suffering, we set up a crowdfunding initiative to help K-CAP 2013 with extra funds (http://www.indiegogo.com/projects/k-cap- 2013). Through this initiative as well as through a standard sponsoring program, we have tried to keep the participation costs as low as possible.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123748304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing contextual and flat representations ofknowledge: a concrete case about football data","authors":"Loris Bozzato, Chiara Ghidini, L. Serafini","doi":"10.1145/2479832.2479842","DOIUrl":"https://doi.org/10.1145/2479832.2479842","url":null,"abstract":"The capability of dealing with context sensitive knowledge is recognized as a crucial aspect in the management of massive amounts of Semantic Web (SW) data. Contextual knowledge can be modelled either by adopting the primitives from RDF/OWL based SW languages or by extending such languages with new specific constructs for context representation. In this paper, we show the benefits of the context-based solution by comparing modelling and reasoning in the two approaches on the paradigmatic use case of FIFA World Cup. The comparison considers the three key aspects of engineering and exploiting knowledge: (i) simplicity and expressivity of the (formal) language; (ii) compactness of the representation; and (iii) efficiency of reasoning. As for (i), we show that the context-based language enables the construction of simpler and more intuitive models while the RDF/OWL \"flat\" model presents practical limitations in modelling cross-contextual knowledge. For (ii), we show that the contextualized model is more compact than the OWL based model. Finally for (iii), query answering in the context-based model outperforms in most of the cases performances on the flat model.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129614907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-driven data harvesting to publish provenance for geospatial references","authors":"W. Francis, R. Atkinson, Paul Box, Terry Rankine, Stuart Woodman, L. Kostanski","doi":"10.1145/2479832.2479863","DOIUrl":"https://doi.org/10.1145/2479832.2479863","url":null,"abstract":"Accurate and timely place-based information from multiple sources is essential for making informed social protection decisions and rapid interventions. Developing solutions to the challenges presented by multi-disciplanary data integration provides a rationale, and mechanisms, to realize the broader goals of Linked Data. The Spatial Identifier Reference Framework (SIRF) combines principles of indentifiers and Linked Data to link place names to related data. Unlike generic placename databases, SIRF uses semantic web technologies to describe relationships between sources of place names and exposes the provenance of identifiers to disambiuguate and explain them. This paper will describe how SIRF uses explicit information models of the spatial datasets from which it builds an index of spatial identifiers in use. Within the SIRF infrastructure the spatial identifiers are harvested from geospatial data sets and published as Web based identifiers (Uniform Resource Identifiers- URIs). These URIs may be used to access multiple forms of data and metadata for the identified feature, including accessing provenance metadata and direct links back to the source datasets. Formal models using the \"Application Schema\" profile defined by the ISO TC 211 General Feature Model drive a repeatable harvesting process and are directly published as part of the provenance metadata. Mappings between the source and common models, used to drive transformations of harvested data into the common index are also presented together with an explanation of their role. Modelled properties, linked to vocabulary mappings, also expposed by web services, to provide a complete Web-accessible provenance of both the source and the interpretation used.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128852829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Axioms & templates: distinctions & transformationsamongst ontologies, frames, & information models","authors":"A. Rector","doi":"10.1145/2479832.2479840","DOIUrl":"https://doi.org/10.1145/2479832.2479840","url":null,"abstract":"The relationships between \"ontologies\", knowledge bases, and information models -- and correspondingly between OWL/Description Logics, frames and UML -- remains confusing to many developers. Understanding which to use when and developing effective hybrid systems that exploit the potential synergies requires clarifying key distinctions: between ontology, background knowledge, and information models; between axiombased and template-based systems; and between logical definitions and queries. As a step towards a more coordinated approach to knowledge-rich systems and a platform for incorporating additional technologies, we propose factoring systems into \"ontology (narrow sense)\", the rest of the \"background knowledge base\", and the \"information model\", with clear distinctions, mutual derivations and interfaces amongst them and clear understanding of the semantics and limitations of each.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124429414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semi-automatic approach for building ontologies from acollection of structured web documents","authors":"Mouna Kamel, Nathalie Aussenac-Gilles, D. Buscaldi, C. Comparot","doi":"10.1145/2479832.2479856","DOIUrl":"https://doi.org/10.1145/2479832.2479856","url":null,"abstract":"Many collections of structured documents are available on the web. The collection generally describes the characteristics of entities from a single type, where each page describes one entity. These documents are adequate knowledge sources for building ontologies. As they benefit from a strong and shared layout, they contain less well written text than plain text files but their architecture is very meaningful. Classical linguistic-based methods for identifying concepts and relations are no longer appropriate for analyzing them.The approach we propose in this paper exploits various properties of such documents, combining layout/formatting analysis and linguistic analysis, and using semantic annotation.","PeriodicalId":388497,"journal":{"name":"Proceedings of the seventh international conference on Knowledge capture","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126463129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}