Expertise Oriented Search aims at providing comprehensive analysis and mining for people from distributed sources. In this paper, we give an overview of the expertise oriented search system (ArnetMiner). The system addresses several key research issues in extraction and mining of a researcher social network. The system is in operation on the internet for more than one year and receives accesses from about 1,500 users per month. Feedbacks from users and system logs indicate that users consider the system can really help people to find and share information in the web community.
Web-based communities have become one of the most important online applications
[
In this paper, we present a novel expertise oriented search system for web
community, which is available at http://www.arnetminer.org [
In ArnetMiner, we define the researcher profile ontology and perform researcher profiling automatically using a unified approach. We integrate publications from the existing bibliography datasets. In the integration, we propose a constraints-based probabilistic model to deal with the problem of name disambiguation. We provide three types of search services. Moreover, we provide several mining services, such as expert finding, people association finding, and hot-topic finding.
The system advances four points: 1) proposal of a unified approach to researcher profiling, 2) proposal of a constraint-based probabilistic model to name disambiguation, 3) proposal of a score-and-propagate approach to expert finding, and 4) proposal of an efficient approach to association search.
other features, e.g., storage, knowledge access, and searching, we utilize the state-ofthe-art methods. This is because, these issues have been intensively investigated previously and the conventional methods can result in good performances. We also provides easy access interface (web services) for developing advanced applications.
Please note that this is a product of an ongoing project. Visitors should expect the system to change. We are extracting more researcher profiles and publications and are also developing more practical search services based on feedbacks from users.
We define the researcher profile ontology (Figure 2), by extending FOAF [
Publication_venue
Start_page
End_page
Date
Publisher Download_URL
Research_Interest Affiliation
Postion Person Photo Homepage Name Phddate
Phduniv
Phdmajor
Msdate
Fax
Phone
Address Email
Bsdate Bsuniv
Bsmajor authored_by
Publication Researcher
author Msuniv Property Relation
Msmajor Concept
Figure 2. The researcher profile ontology
We randomly selected 1K researchers and studied how to extract profiles of the researchers from the Web. We found that it is non-trivial to perform the extraction. Specifically, we observed that 85.62% of the researchers are faculties of universities and 14.38% are researchers from company. For researchers from the same company, they might have similar template-based homepages. However, different companies have different templates. For researchers from universities, the layout and the content of the homepages vary largely depending on the authors. We have also found that 71.88% of the 1K Web pages are researchers’ homepages and the rest are introducing pages. Characteristics of the two types of pages significantly differ from each other.
Statistical study also unveils that (strong) dependencies exist between profile properties. For example, there are 3,842 cases (12.98%) in our data that extraction of a property needs use the extraction results of the other properties. An ideal method should consider annotating all the properties together.
We propose a unified approach to researcher profiling [
We conducted experiments to evaluate the performance of the unified approach. On the randomly chosen 1K researchers’ pages, our approach can reach 83.37% (in terms of F1-measure) on average. We compared our method with several state-of-theart methods, i.e., rule learning based method (Amilcare) and classification based method (SVM-based method). Our approach outperforms the two baseline methods.
We integrate the publication data from existing online data source. We chose DBLP bibliography (dblp.uni-trier.de/), which is one of the best formatted and organized bibliography datasets. DBLP covers approximately 800,000 papers from major Computer Science publication venues. In DBLP, authors are identified by their names. For integrating the researcher profiles and the publications data, we use researcher names and the author names as the identifier. The method inevitably has the ambiguity problem (different researchers have the same name).
The task of name disambiguation can be defined as follow: Given a person name a, we denote all publications containing the author named a as P={p1, p2, …, pn}. For each publication pi, it has attributes: title, conference, year, abstract, authors, and references. Suppose there existing ; actual researchers {y1, y2, …, y;} having the name a, our task is to assign these n publications to their real researcher yi.
Our method is based on a unified probabilistic model using Hidden Markov
Random Fields (HMRF) [
For evaluating the proposed disambiguation method, we created two test sets from
the data collected in ArnetMiner. We applied our method to the two datasets and
obtained 75% in terms of F1-measure. We compared our method with a baseline
method using unsupervised clustering algorithm. The baseline is similar to that
proposed by [
ArnetMiner represents the data based on RDF/OWL and stores the extracted data in
MySQL database using Jena, version 1.5 [
For searching for instances with one property containing some keyword such as “Professor”, the naive SPARQL based method would not be efficient (sometimes even need use dozen of minutes). For efficiently performing this kind of search, we create an inverted-file index. Using the inverted-file index, we can efficiently search for the URI of the instances/properties that contain the keyword. Then we employ SPARQL to query the specified URI. In this way, the index-based method uses only 0.14 second to conduct an average search.
In ArnetMiner, we provide three types of searches: person search, publication search, and conference search. 1. Person search. The user inputs a person name, and the system returns the profile of the person. We perform person search in the constructed researcher network. If a person can be found, the profile of the person stored in the local knowledge base will be displayed. The system also supports searching with constraints, for example, the user can input a query like “Jie Tang, aff:Tsinghua” to searches for the person “Jie Tang” and with its “affiliation” containing “Tsinghua”. 2. Publication search. The user inputs keywords, and the system returns publications with the most relevant publications on the top. We employ the conventional information retrieval model to do the publication search. Moreover, the system tries to find the download link of each publication from the web. 3. Conference search. The user inputs keywords (e.g. “ISWC 2006”), and the system returns the detailed information of the conference.
Currently, ArnetMiner provides five mining services: expert finding, people association finding, hot-topic finding, sub-topic finding, and survey paper finding.
The goal of expert finding is to identify persons with some given expertise from the community: “Who are the experts on topic X in the researcher community?”.
We propose a new approach for finding experts in a web community in which we take into consideration of both person profile and relationships between persons. The approach consists of two stages, Candidate Scoring and Expert Propagation.
In Candidate Scoring, we use the person profile information to calculate an initial expert score for each person. The basic idea here is that if a person has (co)authored many documents on a topic or if the person’s name co-occurs many times with the topic, then it is likely that he/she is a candidate expert on the topic.
In Expert Propagation, we make use of relationships between persons to improve the accuracy of expert finding. The basic idea here is that if a person knows many experts on a topic or if the person’s name co-occurs many times with an expert, then it is more likely that he/she is an expert on the topic.
Our intuition stems from our observations on how humans find an expert in the real world, namely by a) reading person profile information, and b) asking known experts to make a recommendation. Our approach is an implementation of the two observations by combining the person profile and the relationships in the Web community.
We conducted experiments to evaluate the method for expert finding. We assume
that a real ‘expert’ is often active in the committees of the top conferences and
organizations in his/her related research topics. We collected topics and answers
(http://keg.cs.tsinghua.edu.cn/project/PSN/dataset.html). Experimental results show that
our method outperforms the baseline method using only researcher profiles and the
method using PageRank. See [
Given a web community, the people association is defined as a sequence of relationships {eri1, er12, …, erl=} satisfying erm(m+1)!E for m=1, 2, …, l-1, where vi and v= represents the source person and the target person, respectively.
Given a large-scale web community, to find all possible associations between two persons is obviously an NP-hard problem. In ArnetMiner, we concentrate ourselves on finding the most ‘goodness’ associations. We call the association with the smallest score (the small the best) as the shortest association and our goal is to find the near@ shortest associations, whose scores are within a factor of (1+!) of the score of the shortest association for some user-defined !>0. Our method consists of two stages.
1. Shortest association finding. It aims at finding shortest associations from all persons v!A\v= in the community to the target person v= (including the shortest association from vi to v= with score Lmin). We employed a heap-based Dijkstra algorithm to find the shortest associations between two persons.
2. Near-shortest associations finding. Based on the found shortest association score Lmin>0 and a pre-defined parameter !, the algorithm requires enumeration of all associations that are less than (1+!)Lmin by a depth-first search. We constrain the length of an association to be less than a pre-defined threshold.
To evaluate the effectiveness of our proposed approach, we created 9 test sets. Experimental results show that our approach achieves high performance in all of the association search tasks. In terms of the average time, our approach can find associations in less than 3 seconds in most of the search tasks.
Finding the hottest research topics and the sub topics in a research field is a very important issue. For sub-topic finding, a clustering algorithm is utilized to group the papers that contain the keyword inputted by the user. A threshold is used to determine the number of clusters. Then each cluster is viewed as a sub-topic.
For hot-topic fining, we employ a language model based methods. It uses two steps: n-best Part-Of-Speech (POS) tagging and term (base-noun phrase) identification given the n-best POS-sequences. In the first step, it finds the n-best POS sequences for a sentence in the paper or paper title by estimating a language model from the training data. In the second step, it again uses a trained language model to estimate the best term sequence. For each term, a probability is assigned, representing its popularity. We view the terms with the highest probabilities as the hot topics.
A survey paper objectively surveys a body of previously published research on a topic, integrating information from several published papers. Researchers often start investigating a new research issue by first studying the survey papers of that field. We employed a classification based method to find the survey papers. Specifically, given a keyword, we use the state-of-the-art retrieval method to find a set of relevant papers and view the papers as candidates. Then we utilize a classification model to identify whether a paper is a survey paper or not. As the classification model, we employ Support Vector Machines (SVM). Features were defined in the classification model.
Here, we share some thoughts about the strengths and the weaknesses of system.
From experimental results, we see that ArnetMiner can achieve high performance in most of the key issues addressed, including profiling, integration, expert finding, and association finding. Some concluding remarks are as follows:
1) Automatic extraction of the researcher profile from the Web is feasible and the profile properties are usually inter-independent. By making use of the dependencies between the properties, the accuracy of the profile extraction can be improved.
2) Integration of data from different sources is necessary for web community. Name disambiguation is the key issue in the integration. Our approach based on HMRF model can obtain better results than the baseline method.
3) Efficient storing and access is very important for a Semantic Web application. Using the index-based method, the system can provide high efficiency in search.
4) Expert finding is an important issue in the academic community. The score-andpropagate approach can effectively combine the researcher profile and the relationships between researchers, and thus obtain high performance.
5) People association search is another important issue for searching web community. The proposed approach can efficiently find associations between people.
1) Extraction of more types of relationship. In ArnetMiner, we use only the coauthorship as the relationship. In the future, we will extract other relationships, e.g., the relationship of co-organization and co-project etc.
2) FOAF file integration. In the current system, we use the Google search API to locate the pages and extract the profile from the identified page for a researcher. FOAF files are also important sources to get person description. We can further integrate FOAF files on the Web to get more information about the person.
As future work, we also plan to investigate more mining issues to empower the system, for example expertise publication finding and rising ‘star’ finding on a topic.
We received feedbacks from about one hundred users. Most of the feedbacks are positive. For example, some suggest that the expert finding approach is useful and it can be enhanced by adding several new features (e.g. reviewers finding for a paper). Some other feedbacks also ask for improvements of the system. For example, 5% of the feedbacks complain mistakes made in the profile extraction and 6.8% point out the integration mistakes (assigning publications to a wrong researcher). In addition, 5.5% of the feedbacks mention that the found research interests are not accurate and the method should be improved, which is also our current research issue.
In this paper, we have presented an expertise oriented search system, called ArnetMiner, for web community. We introduced the architecture and the main features of the system. We have described in detail the several issues that we are focusing on and proposed our approaches to them. We have carried out experiments for evaluating each of the proposed approaches. We also simply analyzed the strengths and weakness of the system.