Results 1 - 10
of
32
From Information to Knowledge: Harvesting Entities and Relationships from Web Sources
"... There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-l ..."
Abstract
-
Cited by 26 (7 self)
- Add to MetaCart
(Show Context)
There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. Recent endeavors of this kind include DBpedia, EntityCube, KnowItAll, ReadTheWeb, and our own YAGO-NAGA project (and others). The goal is to automatically construct and maintain a comprehensive knowledge base of facts about named entities, their semantic classes, and their mutual relations as well as temporal contexts, with high precision and high recall. This tutorial discusses state-ofthe-art methods, research opportunities, and open challenges along this avenue of knowledge harvesting.
Discriminative models of integrating document evidence and document-candidate associations for expert search
- In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’10
, 2010
"... Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indi-cated in supporting documents. On the other hand, dis-criminative models have received little attention in expert search re ..."
Abstract
-
Cited by 14 (1 self)
- Add to MetaCart
(Show Context)
Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indi-cated in supporting documents. On the other hand, dis-criminative models have received little attention in expert search research, although they have been shown to outper-form generative models in many other information retrieval and machine learning applications. In this paper, we propose a principled relevance-based discriminative learning frame-work for expert search and derive specific discriminative models from the framework. Compared with the state-of-the-art language models for expert search, the proposed re-search can naturally integrate various document evidence and document-candidate associations into a single model without extra modeling assumptions or effort. An extensive set of experiments have been conducted on two TREC En-terprise track corpora (i.e., W3C and CERC) to demonstrate the effectiveness and robustness of the proposed framework.
A Survey of Algorithms and Systems for Expert Location in Social Networks. In
- C. C. Aggarwal, Social Network Data Analytics
, 2011
"... ..."
Enhanced models for expertise retrieval using community-aware strategies
- IEEE Trans. SMC-B
, 2012
"... Abstract—Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of interest, has received increasing interest in recent years. One of the issues is that previous algorithms mainly consider the documents associated with the experts while ignoring the community infor ..."
Abstract
-
Cited by 9 (1 self)
- Add to MetaCart
(Show Context)
Abstract—Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of interest, has received increasing interest in recent years. One of the issues is that previous algorithms mainly consider the documents associated with the experts while ignoring the community information that is affiliated with the documents and the experts. Motivated by the observation that communities could provide valuable insight and distinctive information, we investigate and develop two community-aware strategies to enhance expertise retrieval. We first propose a new smoothing method using the community context for statistical language modeling, which is employed to identify the most relevant documents so as to boost the performance of expertise retrieval in the document-based model. Furthermore, we propose a querysensitive AuthorRank to model the authors ’ authorities based on the community coauthorship networks and develop an adaptive ranking refinement method to enhance expertise retrieval. Experimental results demonstrate the effectiveness and robustness of both community-aware strategies. Moreover, the improvements made in the enhanced models are significant and consistent. Index Terms—Community-aware strategy, expertise retrieval, language model, query-sensitive AuthorRank. I.
Generative models for ticket resolution in expert networks
- In KDD
, 2010
"... Ticket resolution is a critical, yet challenging, aspect of the delivery of IT services. A large service provider needs to handle, on a daily basis, thousands of tickets that report various types of problems. Many of those tickets bounce among multiple expert groups before being transferred to the g ..."
Abstract
-
Cited by 8 (2 self)
- Add to MetaCart
(Show Context)
Ticket resolution is a critical, yet challenging, aspect of the delivery of IT services. A large service provider needs to handle, on a daily basis, thousands of tickets that report various types of problems. Many of those tickets bounce among multiple expert groups before being transferred to the group with the right expertise to solve the problem. Finding a methodology that reduces such bouncing and hence shortens ticket resolution time is a long-standing challenge. In this paper, we present a unified generative model, the Optimized Network Model (ONM), that characterizes the lifecycle of a ticket, using both the content and the routing sequence of the ticket. ONM uses maximum likelihood estimation, to represent how the information contained in a ticket is used by human experts to make ticket routing decisions. Based on ONM, we develop a probabilistic algorithm to generate ticket routing recommendations for new tickets in a network of expert groups. Our algorithm calculates all possible routes to potential resolvers and makes globally optimal recommendations, in contrast to existing classification methods that make static and locally optimal recommendations. Experiments show that our method significantly outperforms existing solutions.
Citation author topic model in expert search
- In COLING
, 2010
"... This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the que ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
(Show Context)
This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the query Syntactic Parsing. Our contribution in this paper is two-fold. First, we model the cited author information together with words and paper authors. Such extra contextual information directly models linkage among authors and enhances the author-topic association, thus produces more coherent author-topic distribution. Second, we provide a preliminary solution to the task of expert search when the learning repository contains exclusively research related documents authored by the experts. When compared with a previous proposed model (Johri et al., 2010), the proposed model produces high quality author topic linkage and achieves over 33 % error reduction evaluated by the standard MAP measurement. 1
Contextual Factors for Finding Similar Experts
, 2010
"... Expertise-seeking research studies how people search for expertise and choose whom to contact in the context of a specific task. An important outcome are models that identify factors that influence expert finding. Expertise retrieval addresses the same problem, expert finding, but from a system-cent ..."
Abstract
-
Cited by 8 (1 self)
- Add to MetaCart
Expertise-seeking research studies how people search for expertise and choose whom to contact in the context of a specific task. An important outcome are models that identify factors that influence expert finding. Expertise retrieval addresses the same problem, expert finding, but from a system-centered perspective. The main focus has been on developing content-based algorithms similar to document search. These algorithms identify matching experts primarily on the basis of the textual content of documents with which experts are associated. Other factors, such as the ones identified by expertise-seeking models, are rarely taken into account. In this article, we extend content-based expert-finding approaches with contextual factors that have been found to influence human expert finding. We focus on a task of science
Expert recommendation based on social drivers, social network analysis, and semantic data representation
- In Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec '11). ACM
, 2011
"... ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous ..."
Abstract
-
Cited by 6 (1 self)
- Add to MetaCart
(Show Context)
ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.
Adapting language modeling methods for expert search to rank Wikipedia entities
- Lecture Notes in Computer Science
, 2009
"... Abstract. In this paper, we propose two methods to adapt language modeling methods for expert search to the INEX entity ranking task. In our experiments, we notice that language modeling methods for expert search, if directly applied to the INEX entity ranking task, cannot effectively distinguish en ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper, we propose two methods to adapt language modeling methods for expert search to the INEX entity ranking task. In our experiments, we notice that language modeling methods for expert search, if directly applied to the INEX entity ranking task, cannot effectively distinguish entity types. Thus, our proposed methods aim at resolving this problem. First, we propose a method to take into account the INEX category query field. Second, we use an interpolation of two language models to rank entities, which can solely work on the text query. Our experiments indicate that both methods can effectively adapt language modeling methods for expert search to the INEX entity ranking task.
Ranking Authors in Digital Libraries
"... Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document’s ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
(Show Context)
Searching for people with expertise on a particular topic also known as expert search is a common task in digital libraries. Most models for this task use only documents as evidence for expertise while ranking people. In digital libraries, other sources of evidence are available such as a document’s association with venues and citation links with other documents. We propose graph-based models that accommodate multiple sources of evidence in a PageRank-like algorithm for ranking experts. Our studies on two publiclyavailable datasets indicate that our model despite being general enough to be directly useful for ranking other types of objects performs on par with probabilistic models commonly used for expert ranking. 1.
