Unsupervised, Efficient and Semantic Expertise Retrieval

2008

Enterprise corpora contain evidence of what employees work on and therefore can be used to automatically find experts on a given topic. We present a general approach for representing the knowledge of a potential expert as a mixture of language models from associated documents. First we retrieve documents given the expert's name using a generative probabilistic technique and weight the retrieved documents according to expert-specific posterior distribution.

With the enormous growth of data, retrieving information from the Web became more desirable and even more challenging because of the Big Data issues (e.g. noise, corruption, bad quality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given a topic, has been a real concern in the last 15 years. This kind of task comes in handy when building scientific committees, requiring to identify the scholars' experience to assign them the most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In this paper, we propose an expert seeking approach with specifying the most desirable features (i.e. criteria on which researcher's evaluation is done) along with their estimation techniques. We utilized some machine learning techniques in our system and we aim at verifying the effectiveness of incorporating influential features that go beyond publications.

Knowledge-Based Systems, 2021

Here, we propose a topic sensitive hybrid expertise retrieval system in community question answering services. We introduce three new expertise signatures: knowledge, reputation, and authority. These signatures consider the questions, and hence, their answerers from a topic sensitive perspective. We estimate the knowledge of an answerer on a new question based on the previously answered subset of questions with similar topic distributions to the new question. The reputation of an answerer, moreover, is derived from the qualities of previously answered questions by the answerer with similar distributions of topics. Furthermore, we propose a topic sensitive authority model. It considers some topic related information associated with questions and the relationships among their answerers. We compare the proposed method with 26 existing methods on 4 real-world datasets using 5 performance measures. It outperforms the comparing algorithms in 91.73% (477 out of 520) cases.

Proceedings of the 9th International Conference on Agents and Artificial Intelligence, 2017

In this work, we propose enhanced data-driven techniques that optimize expert representation and identify subject experts via automated analysis of the available online information. We use a weighting method to assess the levels of expertise of an expert to the domain-specific topics. An expert profile is presented by a description of the topics in which the person is an expert plus the relative levels (weights) of knowledge or experience he/she has in the different topics. In this context, we define a way to estimate the expertise similarity between experts. Then the experts finding task is viewed as a list completion task and techniques that return similar experts to ones provided by the user are considered. The proposed techniques are tested and evaluated on data extracted from PubMed repository.

2011

Abstract. The task of expert finding has been getting increasing attention in information retrieval literature. However, the current stateof-the-art is still lacking in principled approaches for combining different sources of evidence.

2008

An expert search system assists users with their “expertise need” by suggesting people with relevant expertise to their query. Most systems work by ranking documents in response to the query, then ranking the candidates using information from this initial document ranking and known associations between documents and candidates. In this paper, we aim to determine whether we can approximate an evaluation of the expert search system using the underlying document ranking.

Applied Intelligence, 2018

In this paper, we propose a hybrid expertise retrieval system for community question answering services. The proposed system consists of two segments: a text based segment and a network based segment. For a given question, the text based segment estimates users' knowledge introducing two new concepts: question hardness and question answerer association. The network based segment, moreover, incorporates users' relative performances into the network structure. We denote the outputs of these two segments as knowledge score and authority score, respectively. We aggregate these two scores using a fusion technique to quantify the expertise of a given user for a given question. We have generated four datasets by downloading questions and answers from Yahoo! Answers. The performance of the proposed system is found to be superior than that of 18 state-of-the-art algorithms on these four real-world datasets.

Social Network Analysis and Mining

Mining topical experts on social media is a problem that has gained significant attention due to its wide-ranging applications. Here we present the first study that combines data from four major social networks-Twitter, Facebook, Google+ and LinkedIn, along with the Wikipedia graph and internet webpage text and metadata, to rank topical experts across the global population of users. We perform an in-depth analysis of 37 features derived from various data sources such as message text, user lists, webpages, social graphs and wikipedia. This large-scale study includes more than 12 billion messages over a 90-day sliding window and 58 billion social graph edges. Comparison reveals that features derived from Twitter Lists, Wikipedia, internet webpages and Twitter Followers are especially good indicators of expertise. We train an expertise ranking model using these features on a large ground truth dataset containing almost 90,000 labels. This model is applied within a production system that ranks over 650 million experts in more than 9,000 topical domains on a daily basis. We provide results and examples on the effectiveness of our expert ranking system, along with empirical validation. Finally, we make the topical expertise data available through open REST APIs for wider use.

Decision Support Systems, 2006

Knowledge intensive organizations have vast array of information contained in large document repositories. With the advent of E-commerce and corporate intranets/extranets, these repositories are expected to grow at a fast pace. This explosive growth has led to huge, fragmented, and unstructured document collections. Although it has become easier to collect and store information in document collections, it has become increasingly difficult to retrieve relevant information from these large document collections. Information Retrieval systems help users identify relevant documents for their information needs. Matching functions match the information in documents with that required by users in terms of queries to produce a set of documents to be presented to the users. It is well known that a single matching function does not produce the best retrieval results for all contexts (documents and queries). In this paper we combine the results obtained from well known matching functions in the literature. We employ Genetic Algorithms to do such combinations and test our method using a large well known document dataset. It is observed that our method produces better retrieval results for both the consensus search and the routing tasks in information retrieval.

Expert Systems With Applications, 1991

A new technology for intelligent full text document retrieval is presented. The retrieval of a document is treated as an expert system problem, recognizing that human document retrieval is expert behavior. The technology is semantic measurement. A working prototype system, LIBRARY, has been built based on the technology. Input is a request for information, in unrestricted technical English; output is all documents with measured content similar to that of the request, ranked in order of relevance. Retrieval is unaffected by similarity or dissimilarity of terms between request and document. LIBRARY's performance is comparable to that of an expert human librarian, representing a significant improvement over traditional document retrieval systems.