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There is a broad consensus regarding what constitute "knowledge assets" in an organisation and the mechanisms by which they are created and maintained via the Knowledge Management Cycle (Evans 2014). Their characterisation at the highest level as "tacit" or "explicit" (Nonaka and Takeuchi 1995) establishes a widely-accepted conceptual basis for their understanding. It is argued that particular knowledge assets lie on a spectrum between tacit and explicit extremes and that they move along this spectrum under the impetus of the process of "knowledge conversion" (Nonaka and van Krogh 2009). Knowledge conversion is seen as a fundamental dynamic that interplays between tacit and explicit knowledge in a non-linear way that leads to the enhancement of both. At the explicit end of the spectrum is "codified knowledge", that is, knowledge that has been converted into "accessible and applicable formats" (Grover and Davenport 2001) and can be communicated independently of the knower. This type of knowledge asset is identified as having the advantage that it is easier to distribute and reuse, but there are practical difficulties associated with it, in that its efficacy is dependent on how it is packaged as a "knowledge unit" and presented and controlled within an overall "knowledge architecture" (Zack 1999). Within this context, codified knowledge is examined to determine if approaches can be identified that optimise the knowledge conversion process, thereby enhancing the value of the resultant knowledge assets. This is done by exploring what may constitute a viable knowledge unit within the business environment, in conjunction with a complementary schema for constructing a knowledge architecture that optimises its application to business improvement and innovation. A cross-disciplinary approach is taken that draws on developments in technical communication (specifically DITA*), metadata standards, and continuous improvement practices (Kaizen, Lean, etc.). The proposed approaches to the management of codified knowledge assets, and the knowledge conversion process, are evaluated and discussed using a case study carried out in a knowledge-intensive mutual insurance company. *Darwin Information Typing Architecture [ABSTRACT FROM AUTHOR]

We analyzed topical scopes of two digital and two data curation curricula offered as (post)graduate certificates or specializations. We investigated a question of what the topical similarities and differences are between the curricula. To address this question: first, we identified topics from individual course and curriculum descriptions; second, we compared the topics from curriculum descriptions with each other; and third, we compared the topics from course descriptions that are part of a curriculum. We found several topics which were acting as least common denominators between two digital curation and between two data curation curricula. Such topics illustrated the fundamental components within the same type of curricula. The two common topics among all curricula were 'standards' and 'management.' This may indicate the importance of following the standards in preparation of digital (data) objects and in curatorial processes, as well as the importance of continued management of such resources in a repository. Our approach may be applicable to other types of educational opportunities such as workshops, MOOCs, and webinars. As we analyze more curation curricula, we may be able to develop a more accurate and comprehensive topical framework for the domain of digital and data curation, further enhancing the education for future digital and data curators. [ABSTRACT FROM AUTHOR]

This paper explores topic aspect (i.e., subtopic or facet) classification for collections that contain more than one language (in this case, English and Chinese), and investigates several key technical issues that may affect the classification effectiveness. The evaluation model assumes a bilingual user who has found some documents on a topic and identified a few passages in each language on specific aspects of that topic that are of interest. Additional passages are then automatically labeled using a k-Nearest- Neighbor classifier and local (i.e., result set) Latent Semantic Analysis (LSA). Experiments show that when few manually annotated passages are available in either language, a classification system trained using passages from both languages can often achieve higher effectiveness than a similar system trained using passages from just one language. Using this experimental framework, this paper answers three technical research questions: whether the normalized cosine similarity measure is better than the more common unnormalized cosine similarity measure (yes), whether the number of retained LSA dimensions (which was heuristically chosen) is appropriate (yes), and whether partial corrections of the translated training examples in the LSA space can yield an improvement over no correction (no). [ABSTRACT FROM AUTHOR]

In this work, we discuss Query-Topic Sensitive Ranking algorithm, called Topic-Driven PageRank (TDPR), to inquire general documents based on a notion of importance. The main idea is that we extract knowledge from training data for multiple classification and build characteristic feature for each topic. By this approach, we get documents reflecting queries and topics within so that we can improve query results and to avoid topic drift problems. [ABSTRACT FROM PUBLISHER]

Reviews are the most common way to ensure quality in natural language (NL) requirements specifications. But with increasing size (up to 3,000 pages in the automotive domain) and complexity of the specification documents, the review task tends to be less effective. To improve the review task for large documents, one possible solution is the ‘topic landscape’. The idea of this approach is to introduce a pre-classification and clustering of requirements according to topics. In a first empirical study with eight students, we analyzed the effectiveness of the topic landscape approach. During this study, we encountered a general limitation of experiments with large requirements specifications, especially in external environments like universities. Industries have a strong demand on new approaches and methods to deal with large specifications. However, there is a growing gap between the number of requirements that can be examined during an empirical study and the number of requirements required to ensure results that are valid for real requirements specifications. This paper describes the conducted empirical study in detail and shows recognized problems concerning the limits of educational environments. [ABSTRACT FROM PUBLISHER]

Terrorist group's forums remain a threat for all web users. It stills need to be inspired with algorithms to detect the informative contents. In this paper, we investigate most discussed topics on Arabic Dark Web forums. Arabic Textual contents extracted from selected Arabic Dark Web forums. Vector Space Model (VSM) used as text representation with two different term weighing schemas, Term Frequency (TF) and Term Frequency — Inverse Document Frequency (TF-IDF). Pre-processing phase plays a significant role in processing extracted terms. That consists of filtering, tokenization and stemming. Stemming step is based on proposed stemmer without a root dictionary. Using one of the well-know clustering algorithm k-means to cluster of the terms. The experimental results were presented and showed the most shared terms between the selected forums. [ABSTRACT FROM PUBLISHER]

Migration of the central idea of sub-theme content sufficiently reflects development of the idea of an article. The main purpose of topic segmentation is to identify the boundary between sub-themes. Accurate topic segmentation is of great value to many natural language processing applications such as automatic summarization, request-answering system etc. This paper proposes a method of topic segmentation of Chinese text based on lexical chain, and shows the efficiency and accuracy of the method through many experiments. At last, it put forwards the disadvantages and future prospect of the approach. [ABSTRACT FROM PUBLISHER]

In this paper, we aim to propose an new approach to automatically generate text and photo overviews for landmarks, using the vast quantity of geo-tagged photos from photo sharing websites such as Flickr. To that end we first cluster a large-scale geo-tagged web photo collections into groups by location. And next, we make our focus on analyzing tags(user-entered text labels associated with a photo) and develop an TF-IDF like approach to evaluate the representativeness of a tag within group. To provide a more friendly and comprehensive overview, we try to mine topic patterns in each group. So we propose a clustering method among tags to discover some meaningful topics. The text overviews are generated based on tag's representativeness and topics discovered, and the photo overviews generating is simply a retrieval work according to the overview tags generated. We perform evaluations over 530,123 real geo-tagged photos taken in Beijing from Flickr and The experiment shows our approach can obtain a better result than state-of-the-art method. [ABSTRACT FROM PUBLISHER]

Mining subtopics and analyzing their sentiment dynamics on weblogs have many applications in multiple domains. Current work pays little attention to the combination of topics and their sentiment evolution simultaneously. In this paper, we study the problem of topic detection and sentiment-topic temporal evolution in weblogs, and propose a novel probabilistic model called topic sentiment trend model (TSTM). With the model, we can integrate the topic with sentiment, and analyze the temporal trend of the sentiment-topic. Experiments on two Chinese weblog datasets show that our approach is effective in modeling the topic facets and extracting their sentiment dynamics. [ABSTRACT FROM PUBLISHER]

Journalists' blogs (j-blogs) are novel journalistic practice emerging in Chinese news industry. This study is the first step in establishing what is going on in Chinese news media in terms of j-blogs and finding out how new technology would change and transform the traditional news industry in Mainland China. Content analysis was conducted to study j-blogs on Chinese newspaper websites and investigate their implications for journalism in China. Topic, format, reader comments, hyperlinks and multimedia such as video, audio and pictures in j-blog posts were analyzed. It was revealed that the most often addressed topics in Chinese j-blogs were lifestyle, politics/government/military, crime/accidents/disasters, and business/economic activity. The most frequently adopted formats in Chinese j-blogs were the straight opinion column, the rumor-mill blog, and reporter's notebook of news tidbits and incidentals. It was shown that the formats of j-blog posts varied by topics. Significant influence of topic and format were found on the number of reader comments in Chinese j-blog posts. The use of hyperlinks and multimedia also differed in j-blogs with different topics and formats. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]

In both Romance and Germanic, demonstratives can be 'reinforced' by the locative adverbials here and there, like in the non-standard English this here book, that there book (see Bernstein (1997)). In this work I analyze structures where the DP is followed by a PP (DP-complement or possessor PP). I show that the position of the reinforcer is DP internal in structures of the form Demonstrative+N+Reinf+PP, a standard assumption. However when the reinforcer follows the PP Demonstrative+N+PP+Reinf, the reinforcer is only grammatical if the DP is dislocated. This suggests that the putative reinforcer realizes in fact a Top head either in the left or right periphery. I further show that while the distal adverbial lì can indeed lexicalize a Top head, the proximal adverbial qui fails to do so. The latter can only be used as a bona fide DP-internal reinforcer. [ABSTRACT FROM AUTHOR]

This study applied the functional theory of political campaign discourse to three televised debates in Germany during 2002 and 2005; results are then compared with content analyses of political leaders' debates from America and Israel. Overall, acclaims were the most common function, followed by attacks and then defenses. Policy was addressed more often than character in these debates. The incumbent candidates acclaimed significantly more and attacked less than the challengers. The incumbents used past deeds significantly more often to acclaim - and less to attack - than the challengers. These patterns are consistent with analysis of American and Israeli political debates, suggesting that the situation facing candidates for political office may incline them to produce discourse with important similarities that transcend national borders. ..PAT.-Unpublished Manuscript [ABSTRACT FROM AUTHOR]