Microsoft Technology Licensing, LLC

Vasileios Kandylas

Omar Rogelio Alonso

Serge-Eric Tremblay

Kartikay Khandelwal

Martin Hentschel

Thibault Sellam

Siddhartha Sen

Jake M Hofman

Rukmini Iyer

Justin Ormont

Aspects of the technology described herein detect experts in social networks. Experts may be identified within social networks using social network data. A collection of topics may be compiled based on search engine behavioral data. A combination of the social network data and search engine behavioral data is utilized to score each topic within potential topics for a user. When a topic score is over a predetermined threshold, a user may be classified as an expert for that topic. Expert search interfaces may be generated for use in browsing or searching for experts within a topic.

Expert detection in social networks

Described herein are technologies relating to generating search results responsive to receipt of a query. More specifically, the query is mapped to a topic in response to receipt of a query, and social media accounts that have been labeled as being knowledgeable on the topic are identified. Messages in a message feed of the social media account that are germane to the topic are retrieved, and documents referenced (linked) in the retrieved messages are identified. These documents are positioned in a ranked list based upon the documents being referenced in the messages.

Search engine results for low-frequency queries

Examples are disclosed that relate to grouping query clusters based on intent of a search query. One example provides a system for generating recommended queries related to a search query, the system including a logic subsystem, and memory holding instructions executable by the logic subsystem to receive the search query, execute the search query on an index of pre-computed query clusters to identify a baseline query cluster and one or more neighboring query clusters, form a data structure including queries from within the baseline query cluster and the one or more neighboring query clusters, traverse the data structure to form a hierarchical arrangement of queries, and send the hierarchical arrangement of queries to a computing device from which the search query was received.

Providing query recommendations

One or more techniques and/or systems are provided for populating an information interface based upon social media data. For example, users may post, share, and/or discuss various information through social media sources. Accordingly, social media data may be obtained from such social media sources. The social media data may be grouped into sets of social media data based upon temporal information. Within the sets of social media data, social media entries may be clustered into topic clusters (e.g., a royal wedding topic cluster, a plane crash topic cluster, etc.). Event summaries may be generated for respective topic clusters. The event summaries may be used to populate timeslots of an information interface, such as a calendar or timeline, to create annotated timeslots. In this way, the information interface may provide users with an interactive view of events over a time period, such as a year-in-review, based upon social media data.

Social media driven information interface

A technique is described for generating a knowledge graph that links names associated with a first subject matter category (C1) (such as brands) with names associated with a second subject matter category (C2) (such as products). In one implementation, the technique relies on two similarly-constituted processing pipelines, a first processing pipeline for processing the C1 names, and a second processing pipeline for processing the C2 names. Each processing pipeline includes three main stages, including a name-generation stage, a verification stage, and an augmentation stage. The generation stage uses a voting strategy to form an initial set of seed names. The verification stage removes noisy seed names. And the augmentation stage expands each verified name to include related terms. A final edge-forming stage identifies relationships between the expanded C1 names and the expanded C2 names using a voting strategy.

Resource-efficient generation of a knowledge graph

Representative embodiments disclose mechanisms to compile documents into a timeline document that tracks the evolution of a topic over time. Social media documents can be used to identify importance or popularity of linked documents (i.e., documents shared by social media in a post, tweet, etc.). A collection of social media documents is analyzed and used to identify a series of n-grams and a ranked list of linked documents. A subset of the ranked list is selected based upon similarity to the series of n-grams. The subset is then summarized and captured, along with underlying supporting data, into an entry of a timeline document. Related entries in different timeline documents can be linked to create a pivot point that allows a user to jump from one timeline to another. Timeline documents can be made available as part of a search performed by a query system.

Compiling documents into a timeline per event

Aspects of the technology described herein generate a comment-summary interface that can help a user find comments of interest to the user. The comment-summary interface allows a user to access comments according to topics instead of scrolling through all of the comments found in a comment section associated with a primary content, such as a news article on a website, a social post, product review, and such. Categorizing the comments into topics makes more efficient use of computer resources by avoiding the need to display comments that are not of interest to the user. The plurality of comments analyzed by aspects of the technology described herein are unstructured comments. Unstructured comments lack specific subject matter categories designated by a user or preselected by a webpage.

Content categorization

One or more techniques and/or systems are provided for augmenting a social snippet with an augmentation tagline. For example, a search user may submit a search query through a search interface (e.g., 'algebra help' search query). An entity associated with the search query may be identified (e.g., a math professor). A set of taglines associated with the entity may be retrieved (e.g., descriptive information about the math professor extracted from social networks, documents, websites, etc.). A social snippet for the entity may be augmented with an augmentation tagline selected from the set of taglines or generated based upon information relating to the entity. The augmented social snippet may be displayed through the search interface, and the augmentation tagline may indicate a relevance of the entity to the search user (e.g., an indication that the math professor teaches algebra at a university attended by the search user).

Social snippet augmenting

Techniques for designing an awareness engine that organizes and serves popularly discussed and viral online content in response to user search queries. In an aspect, quality online content is identified by analyzing posts by users of a social network over specific time periods. For each item of quality online content identified, a virality score is calculated, and a social signature is constructed. The social signature can be constructed from the content itself, as well as from posts referencing the content. Based on this processing, relevant quality online content having the highest virality scores may be retrieved and served in response to user queries. Further techniques are provided for designing a user interface for the awareness engine.

Awareness engine

Systems and methods for generating a social sketch from social communications are provided. Unlike a typical summary of a subject (or of multiple subjects), a social sketch represents a 'snapshot' summary of the social communications of people during a given period of time. The social sketch is generated by obtaining a corpus of social communications and filtering the social communications according to time. The filtered results are clustered according to the subject matter/topics of the social communications. Selected clusters are identified and the topic, representative high-quality social communications from non-experts and experts are extracted from each of the selected clusters and saved as a social sketch corresponding to the time period.

Social sketches

Non-limiting examples of the present disclosure describe utilization of a social graph, created from evaluation of extracted social media data, to generate temporal snapshots related to social media data. The temporal snapshots enable users to explore different levels of interest related to social media data. In one example, a social media identifier is identified. A social graph may be accessed to evaluate the social media identifier. An exemplary social graph includes clustered relationships developed based on analysis of social media data extracted from at least one social networking service. The social graph may be used to generate a temporal snapshot for the social media identifier. The temporal snapshot may be transmitted to an entry point for output of the temporal snapshot. Other examples are also described including navigation between content of temporal snapshots based on selection of linked data, among other examples.

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Sunnyvale, CA, United States of America

Vasileios Kandylas