This paper addresses the Target-Value Search (TVS) problem, which is the problem of finding a path between two nodes in a graph whose cost is as close as possible to a given target value, T. This problem has been previously addressed: first, for directed acyclic graphs; second, for general graphs under the assumption that nodes can be revisited given that the same edge can not be traversed twice. In this work we focus on a more restrictive variant of the same problem where nodes can not be revisited. We prove that this variant is NP-complete and discuss novel theoretical properties and provide empirical results to solve this problem optimally.

Tweets ranking is important for information acquisition in Microblog. Due to the content sparsity and lackof labeled data, it is better to employ semi-supervisedlearning methods to utilize the unlabeled data. However,most of previous semi-supervised learning methods donot consider the pair conflict problem, which means thatthe new selected unlabeled data may conflict with the labeled and previously selected data. It will hurt the learning performance a lot, if the training data contains manyconflict pairs. In this paper, we propose a new collaborative semi-supervised SVM ranking model (CSR-TC)with consideration of the order conflict. The unlabeleddata is selected based on a dynamically maintained transitive closure graph to avoid pair conflict. We also investigate the two views of features, intrinsic and contentrelevant features, for the proposed model. Extensive experiments are conducted on TREC Microblogging corpus. The results demonstrate that our proposed methodachieves significant improvement, compared to severalstate-of-the-art models.

CiteSeerX is a digital library search engine that provides access to more than 4 million academic documents with nearly a million users and millions of hits per day. Artificial intelligence (AI) technologies are used in many components of CiteSeerX, e.g. to accurately extract metadata, intelligently crawl the web, and ingest documents. We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5–6 years. We also show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.

In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques. Our efforts focus on the specific problem of cost-based join-order optimization for conjunctive relational queries, a critical component of production-quality query optimizers. We characterize the general query-planning problem as a delete-free planning problem, and query plan optimization as a context-sensitive cost-optimal planning problem. We propose algorithms that generate high-quality query plans, guaranteeing optimality under certain conditions. Our approach is general, supporting the use of a broad suite of domain-independent and domain-specific optimization criteria. Experimental results demonstrate the effectiveness of AI planning techniques for query plan generation and optimization.

Keyphrases for a document concisely describe the document using a small set of phrases. Keyphrases were previously shown to improve several document processing and retrieval tasks. In this work, we study keyphrase extraction from research papers by leveraging citation networks. We propose CiteTextRank for keyphrase extraction from research articles, a graph-based algorithm that incorporates evidence from both a document's content as well as the contexts in which the document is referenced within a citation network. Our model obtains significant improvements over the state-of-the-art models for this task. Specifically, on several datasets of research papers, CiteTextRank improves precision at rank 1 by as much as 9-20% over state-of-the-art baselines.

In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.

"Fraudulent support phones" refers to the misleading telephone numbers placed on Web pages or other media that claim to provide services with which they are not associated. Most fraudulent support phone information is found on search engine result pages (SERPs), and such information substantially degrades the search engine user experience. In this paper, we propose an approach to identify fraudulent support telephone numbers on the Web based on the co-occurrence relations between telephone numbers that appear on SERPs. We start from a small set of seed official support phone numbers and seed fraudulent numbers. Then, we construct a co-occurrence graph according to the co-occurrence relationships of the telephone numbers that appear on Web pages. Additionally, we take the page layout information into consideration on the assumption that telephone numbers that appear in nearby page blocks should be regarded as more closely related. Finally, we develop a propagation algorithm to diffuse the trust scores of seed official support phone numbers and the distrust scores of the seed fraudulent numbers on the co-occurrence graph to detect additional fraudulent numbers. Experimental results based on over 1.5 million SERPs produced by a popular Chinese commercial search engine indicate that our approach outperforms TrustRank, Anti-TrustRank and Good-Bad Rank algorithms by achieving an AUC value of over 0.90.

Markov Chains are a fundamental tool for the analysis of real world phenomena and randomized algorithms. Given a graph with some specified sink nodes and an initial probability distribution,we consider the problem of designing an absorbing Markov Chain that minimizes the time required to reach a sink node, by selecting transition probabilities subject to some natural regularity constraints. By exploiting the Markovian structure, we obtain closed form expressions for the objective function as well as its gradient, which can be thus evaluated efficiently without any simulation of the underlying process and fed to a gradient-based optimization package. For the special case of designing reversible Markov Chains, we show that global optimum can be efficiently computed by exploiting convexity. We demonstrate how our method can be used for the evaluation and design of local search methods tailored for certain domains.

Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is how to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query.We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.

The standard way of answering queries over incomplete databases is to compute certain answers, defined as the intersection of query answers on all complete databases that the incomplete database represents. But is this universally accepted definition correct? We argue that this "one-size-fits-all" definition can often lead to counterintuitive or just plain wrong results, and propose an alternative framework for defining certain answers. The idea of the framework is to move away from the standard, in the database literature, assumption that query results be given in the form of a database object, and to allow instead two alternative representations of answers: as objects defining all other answers, or as knowledge we can deduce with certainty about all such answers. We show that the latter is often easier to achieve than the former, that in general certain answers need not be defined as intersection, and may well contain missing information in them. We also show that with a proper choice of semantics, we can often reduce computing certain answers - as either objects or knowledge - to standard query evaluation. We describe the framework in the most general way, applicable to a variety of data models, and test it on three concrete relational semantics of incompleteness: open, closed, and weak closed world.