This paper presents the Cite4Me Web application and its features created for the LAK Challenge 2013. The Web application focuses on two main directions: (i) interlinking of the LAK dataset with related data sources from the Linked Open Data cloud; and (ii) providing innovative search, visualization, retrieval and recommendation of scientific publications from the LAK dataset and related interlinked resources. Our approach is based on semantic and cooccurrence relations to provide new browsing experiences to Web users and an overview of scientific data available. Furthermore, we present a detailed analysis of the LAK dataset along with applications which contributes to the development of the learning analytics field.
The volume of information on the Web has been growing steadily over the last decade and has doubled every two years. The vast amount of data available on the Web along with new means of communications have transformed our society, including the way we work, live, relate to each other and learn.
In the midst of change, the Learning Analytics emerges to make sense of the produced educational data reported by learners, professors, institutions and so on. Analyzing and understanding the changes along the past years help us to understand the current state and be aware of the forthcoming trends, enabling a new outlook of the future of learning.
A recent challenge initiative of SOLAR1 and LinkedUp2 project arises to leverage the creation of tools that enables the analysis, visualization, browsing and recommendation of scientific and educational data.
Although the scientific field has fostered the creation of new applications in several areas, such as medical, biology, physics, amongst others, the information access is based mostly on free text search and on hierarchical classification system of the pub1Society for Learning Analytics Research - http://www. solaresearch.org 2http://linkedup-project.eu/ Copyright c 2013 by the papers’ authors. Copying permitted only for private and academic purposes.
LAK-Data Challenge ’13 Leuven, Belgium lications3. However, current approaches by main digital library providers, such as ACM Digital Library4 and Elsevier5, do not represent the current state of research on exploring resources using approaches from Information Retrieval, Information Extraction and Semantic Web. Thus, get an overview of research topics, find publications and discover new nomenclatures are an arduous and laborious task that are not always successful.
In this paper, we introduce Cite4Me a novel application for exploratory search, retrieval and visualization of scientific publications. Cite4Me intends to provide to the end users a single point for accessing papers and hence reducing e orts of searching in several data sources. Our system takes advantage of reference datasets, such as DBpedia6, to explore semantic relationships between scientific papers and user queries. Additionally, an analysis of topic coverage and shared concepts from related educational datasets, extracted from the Linked Open Data cloud, will be introduced.
The remaining of the paper is organized as follows. Section 2 presents the approach used for searching, retrieving and recommending papers. Section 3 describes the process of dataset discovery and interlinking and Section 4 shows a brief result analysis of the data discovery. Finally, Section 5 presents related work and Section 6 presents some concluding remarks. 2.
As one of the main goals of the field of “Learning Analytics” is to support students in their learning process, we developed a Web application called Cite4Me7 that assists students in making decisions to find scientific publications and identify relevant research topics.
Cite4Me implements semantic and co-occurrence methods to (a) search and retrieve scientific publications; and (b) recommend scientific publications. Moreover, it provides a Web interface that facilitates the search for publications and may help users on discovering related terms to a given query.
In this section, we provide an overview of the major features of the Web application and its Web interface that assist users to explore scientific data on the Web. 2.1
Cite4Me relies on search functionalities to meet the users needs. Briefly, we implemented standard Information Retrieval (IR) and Semantic Web (SW) approaches to retrieve and recommend scientific papers to the users. We divided this subsection into (i) free text 3http://www.acm.org/about/class/ 4http://dl.acm.org 5http://www.elsevier.com 6http://dbpedia.org 7http://www.cite4me.com/ search; (ii) exploratory search; and (iii) semantic search. 2.1.1
The purpose of the free text search functionality is to o er users the abilities to search for mentions, titles and authors of academic publications contained in the LAK dataset. Even though, this functionality is similar to existing digital libraries, we agree that this is a basic functionality that must be provided by our application. Therefore, we use standard vector space models (tf-idf ) for indexing and retrieving documents.
The tf-idf scores were computed for each term extracted from the
publication content after applying stemming [
In summary, our free text search provides to the users publications (P) that match query terms and non-matching publications P0, which are related to P according to a degree of similarity (see Eq. 1), but does not contain the query terms.
The similarity between a matching publication P and other nonmatching publication P0 in the LAK dataset is measured by the standard cosine similarity measure, which is built on top of the computed tf-idf scores.
S im(P; P0) =
P P0 jPjjP0j (1) where P and P0 represent the tf-idf scores for the terms in two distinct publications. 2.1.2
In this section, we provide detailed insights on the exploratory search functionality of our application. As a preliminary step to provide analytics and information about the actual content and topics coverage, all the scientific publications contained in the LAK dataset are previously enriched. The enrichment process was performed using DBpedia Spotlight API8, where entities, entity types and their respective categories were extracted.
After the enrichment process, we cluster the publications according to entities and its categories found in each document. The publications are clustered in a tree-based structure over the enrichments. Note that, each node of the tree represents a topic in which a publication under this node covers. Thus, the exploratory search is performed through the topics covered by each publication.
The process of linking publications, categories and extra resources is mediated by DBpedia knowledge graph, where we use the dcterms:subject property to match the resources.
Thus, as a result, the exploratory search provides a way to explore resources through the connections between their topics, which facilitates the search for topically related resources. Figure 1 shows the exploratory search. 2.1.3
Cite4Me provides also a semantic search engine that assists users
to find publications semantically related to the query terms.
Analogously to explicit semantic analysis (ESA) technique [
Basically, the semantic search is an adaptation of the free text search presented in the Section 2.1.1. Instead of computing the tf-idf scores for the words in a text, it computes the tf-idf score
for the entities contained in a publication. Finally, the ranking of the results is based on the sum of the tf-idf scores of the matching concepts.
Figure 2 illustrates the semantic search functionality. It also generates a tag cloud from matching publications, showing the most prominent terms for a given query. Specifically, the tag cloug based on the results helps the users to have an insight about the topics and may assist in finding related terms previously unknown by them. 2.2
Another key feature of our system is the paper recommendation
based on semantic relationships extracted from reference datasets.
The recommendation is based on a previous work [
X l jpaths(<al;>b)j
l=1
where jpaths(<al;>b)j is the number of paths between a and b of length l
and 0 < 1 is a positive damping factor. As in [
Based on the score for entities, we then define the semantic connectivity score (S CS w) between two documents W1 and W2 as follows: 0 B B B
B S CS w(W1; W2)=BBBBBB X
B B B BBBe12E1 BB@e22E2 e1,e2 1 C C
C S CS e(e1; e2) + jE1 \ E2j CCCCC 1
C 2 CCCCC jE1j jE2j
C C C C A where Ei is the set of entities associated with Wi, for i = 1; 2. Note that documents that contain the same entities receive an extra bonus (the second term on the right-hand side of Eq. 3).
Thus, a list of documents pairs is generated and ranked according the score and suggested to the user. Figure 3 illustrates the paper recommendation process computed based on S CS w.
This section briefly describes the datasets used on automatic related data discovery from DataHub9 and future steps on dataset discovery and interlinking. 3.1
The LAK dataset contains the metadata of the papers published in the proceedings of LAK conference 2011-12, a special issue of Learning and Knowledge Analytics: Educational Technology & Society, the proceedings of the International Conference on Educational Data Mining (2008-12) and the Journal of Educational Data Mining (2008-12). In total, 315 descriptions of papers containing detailed information about authors, institutions, conference venues and the full content of the paper were available. 3.2
The goal of the data analysis procedure is to align the various publications in the LAK dataset based on mutual information, such
as the topics covered by them. This is achieved using well established datasets like DBpedia10 and Freebase11, where a reference point for the unstructured textual content of publications is created through an enrichment process.
Again, the enrichment process is carried out using DBpedia
Spotlight12 [
Our Web application uses as its starting point the instances in the LAK dataset to automatically explore and recommend to users, datasets that covers similar topics. In order to query, detect and interlink related datasets, we chose the DataHub as a data provider. DataHub serves as a collecting point of datasets from various fields and currently it has over 5000 datasets. Note that, from the large number of datasets, only 300 datasets are provided as Linked Open Data. As the latter is the main focus of our work, the analysis and interlinking process is focused for such datasets.
Briefly, the data discovery is performed using CKAN13 data management framework from DataHub, where based on data analysis and user interests (such as topics covered by a publication/resource) related datasets are suggested.
Additionally, we provide to the user a set of resources, amongst other data analytics, that enables the user to harvest and correlate new information from the discovered resources, considering the LAK dataset as a starting point of such discovery.
This approach presents several advantages such as the adoption and the widespread use of Linked Data principles for publishing scientific papers. Nowadays, many conferences make their proceedings and journals freely accessible, hence our approach would take advantage of such open data and o er users topically relevant papers for a particular resource in the LAK dataset. 10http://dbpedia.org 11http://www.freebase.com/ 12http://spotlight.dbpedia.org/ 13http://www.ckan.org 4.
This section presents an overview of the results obtained by analyzing the LAK dataset with respect to the constructed feature set that describes topics covered by individual publications. Moreover, based on the data analysis procedure and shared information, we show that the establishment of links between the di erent publications within the LAK dataset and from other datasets in DataHub is possible.
In the following subsections, we show the analysis of the LAK dataset and the discovery of relevant datasets and publications. 4.1
The data analysis of the LAK dataset focuses mostly on assessing the individual publications for their topic coverage. In this manner, we build a connected data graph consisting of the individual publications and items from the feature set. This step is necessary to provide the exploratory search functionality, where based on the established edges between publications and feature set items, we can navigate through the publications or topics of interest. Therefore, the results obtained with respect to the constructed feature set and LAK dataset graph are shown in what follows.
Table 1 shows the top ranked items for each of the feature sets, along with the number of associations an item has with respect to all publications (entity, category and type items). Figure 5 shows the constructed data graph for the LAK dataset. 4.2
After creating the feature set based on the information provided from reference datasets, we are able to query for relevant datasets in DataHub.
Thus, for the top ranked feature set items, the data discovery for relevant resources is considered. Table 2 shows the discovered resources and datasets for the top-10 entity items. Note that, we focus only on bibliographic datasets, since we aim at recommending topically related scientific publications. Due to the lack of bibliographic datasets, we were not able to find related publications for all entities considered. Table 2 summarizes the discovered resources. The dataset names are represented by their acronyms as follows: b3kat - “Bayerische Staatsbibliothek", hebis - “Hessisches Bibliotheks Informations System" and npg - “Nature Publishing Group - ALL".
Additionally, from the set of 96 bibliographic datasets available, only a few of them were o ered as Linked Data, thus narrowing our search space for relevant resources.
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In the same way, McAuley et al. [
In the related work, we showed how di erent perspectives and the necessity of new tools and methods to make data available and help decision-makers.
In this paper we presented the main features of the Cite4Me Web application. Cite4Me makes use of several data sources to provide information for users interested on scientific publications and its applications.
Additionally, we provided a general framework on data discovery and correlated resources based on a constructed feature set, consisting of items extracted from reference datasets. It made possible for users, to search and relate resources from a dataset with other resources o ered as Linked Data.
For more information about the Cite4Me Web application refer to http://www.cite4me.com.